You Are What You Tweet: Connecting the Geographic Variation in America's Obesity Rate to Twitter Content

Screen exposure, mental health and emotional well-being in the adolescent population: is it time for governments to take action

During the last decade, a multitude of epidemiological studies with different designs have been published assessing the association between the use of digital media and psychological well-being, including the incidence of mental disorders and suicidal behaviours. Particularly, available research has very often focused on smartphone use in teenagers, with highly addictive potential, coining the term 'problematic smartphone use' and developing specific scales to measure the addictive or problematic use of smartphones. Available studies, despite some methodological limitations and gaps in knowledge, suggest that higher screen time is associated with impaired psychological well-being, lower self-esteem, higher levels of body dissatisfaction, higher incidence of eating disorders, poorer sleeping outcomes and higher odds of depressive symptoms in adolescents. Moreover, a significant association has also been found between screen time and higher suicide risk. Finally, problematic pornography has been shown to be highly prevalent and it is a strong cause of concern to many public health departments and national governments because it might be eventually associated with aggressive sexual behaviours.

Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content

BACKGROUND

The COVID-19 pandemic had a devastating global impact. In the United States, there were >98 million COVID-19 cases and >1 million resulting deaths. One consequence of COVID-19 infection has been post-COVID-19 condition (PCC). People with this syndrome, colloquially called long haulers, experience symptoms that impact their quality of life. The root cause of PCC and effective treatments remains unknown. Many long haulers have turned to social media for support and guidance.

OBJECTIVE

In this study, we sought to gain a better understanding of the long hauler experience by investigating what has been discussed and how information about long haulers is perceived on social media. We specifically investigated the following: (1) the range of symptoms that are discussed, (2) the ways in which information about long haulers is perceived, (3) informational and emotional support that is available to long haulers, and (4) discourse between viewers and creators. We selected YouTube as our data source due to its popularity and wide range of audience.

METHODS

We systematically gathered data from 3 different types of content creators: medical sources, news sources, and long haulers. To computationally understand the video content and viewers' reactions, we used Biterm, a topic modeling algorithm created specifically for short texts, to analyze snippets of video transcripts and all top-level comments from the comment section. To triangulate our findings about viewers' reactions, we used the Valence Aware Dictionary and Sentiment Reasoner to conduct sentiment analysis on comments from each type of content creator. We grouped the comments into positive and negative categories and generated topics for these groups using Biterm. We then manually grouped resulting topics into broader themes for the purpose of analysis.

RESULTS

We organized the resulting topics into 28 themes across all sources. Examples of medical source transcript themes were Explanations in layman's terms and Biological explanations. Examples of news source transcript themes were Negative experiences and handling the long haul. The 2 long hauler transcript themes were Taking treatments into own hands and Changes to daily life. News sources received a greater share of negative comments. A few themes of these negative comments included Misinformation and disinformation and Issues with the health care system. Similarly, negative long hauler comments were organized into several themes, including Disillusionment with the health care system and Requiring more visibility. In contrast, positive medical source comments captured themes such as Appreciation of helpful content and Exchange of helpful information. In addition to this theme, one positive theme found in long hauler comments was Community building.

CONCLUSIONS

The results of this study could help public health agencies, policy makers, organizations, and health researchers understand symptomatology and experiences related to PCC. They could also help these agencies develop their communication strategy concerning PCC.

Content and User Engagement of Health-Related Behavior Tweets Posted by Mass Media Outlets From Spain and the United States Early in the COVID-19 Pandemic: Observational Infodemiology Study

BACKGROUND

During the early pandemic, there was substantial variation in public and government responses to COVID-19 in Europe and the United States. Mass media are a vital source of health information and news, frequently disseminating this information through social media, and may influence public and policy responses to the pandemic.

OBJECTIVE

This study aims to describe the extent to which major media outlets in the United States and Spain tweeted about health-related behaviors (HRBs) relevant to COVID-19, compare the tweeting patterns between media outlets of both countries, and determine user engagement in response to these tweets.

METHODS

We investigated tweets posted by 30 major media outlets (n=17, 57% from Spain and n=13, 43% from the United States) between December 1, 2019 and May 31, 2020, which included keywords related to HRBs relevant to COVID-19. We classified tweets into 6 categories: mask-wearing, physical distancing, handwashing, quarantine or confinement, disinfecting objects, or multiple HRBs (any combination of the prior HRB categories). Additionally, we assessed the likes and retweets generated by each tweet. Poisson regression analyses compared the average predicted number of likes and retweets between the different HRB categories and between countries.

RESULTS

Of 50,415 tweets initially collected, 8552 contained content associated with an HRB relevant to COVID-19. Of these, 600 were randomly chosen for training, and 2351 tweets were randomly selected for manual content analysis. Of the 2351 COVID-19-related tweets included in the content analysis, 62.91% (1479/2351) mentioned at least one HRB. The proportion of COVID-19 tweets mentioning at least one HRB differed significantly between countries (P=.006). Quarantine or confinement was mentioned in nearly half of all the HRB tweets in both countries. In contrast, the least frequently mentioned HRBs were disinfecting objects in Spain 6.9% (56/809) and handwashing in the United States 9.1% (61/670). For tweets from the United States mentioning at least one HRB, disinfecting objects had the highest median likes and retweets, whereas mask-wearing- and handwashing-related tweets achieved the highest median number of likes in Spain. Tweets from Spain that mentioned social distancing or disinfecting objects had a significantly lower predicted count of likes compared with tweets mentioning a different HRB (P=.02 and P=.01, respectively). Tweets from the United States that mentioned quarantine or confinement or disinfecting objects had a significantly lower predicted number of likes compared with tweets mentioning a different HRB (P<.001), whereas mask- and handwashing-related tweets had a significantly greater predicted number of likes (P=.04 and P=.02, respectively).

CONCLUSIONS

The type of HRB content and engagement with media outlet tweets varied between Spain and the United States early in the pandemic. However, content related to quarantine or confinement and engagement with handwashing was relatively high in both countries.

Crime, inequality and public health: a survey of emerging trends in urban data science

Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.

Sentiment analysis of vegan related tweets using mutual information for feature selection

Nowadays, people get increasingly attached to social media to connect with other people, to study, and to work. The presented article uses Twitter posts to better understand public opinion regarding the vegan (plant-based) diet that has traditionally been portrayed negatively on social media. However, in recent years, studies on health benefits, COVID-19, and global warming have increased the awareness of plant-based diets. The study employs a dataset derived from a collection of vegan-related tweets and uses a sentiment analysis technique for identifying the emotions represented in them. The purpose of sentiment analysis is to determine whether a piece of text (tweet in our case) conveys a negative or positive viewpoint. We use the mutual information approach to perform feature selection in this study. We chose this method because it is suitable for mining the complicated features from vegan tweets and extracting users' feelings and emotions. The results revealed that the vegan diet is becoming more popular and is currently framed more positively than in previous years. However, the emotions of fear were mostly strong throughout the period, which is in sharp contrast to other types of emotions. Our findings place new information in the public domain, which has significant implications. The article provides evidence that the vegan trend is growing and new insights into the key emotions associated with this growth from 2010 to 2022. By gaining a deeper understanding of the public perception of veganism, medical experts can create appropriate health programs and encourage more people to stick to a healthy vegan diet. These results can be used to devise appropriate government action plans to promote healthy veganism and reduce the associated emotion of fear.

Deciphering Latent Health Information in Social Media Using a Mixed-Methods Design

Natural language processing techniques have increased the volume and variety of text data that can be analyzed. The aim of this study was to identify the positive and negative topical sentiments among diet, diabetes, exercise, and obesity tweets. Using a sequential explanatory mixed-method design for our analytical framework, we analyzed a data corpus of 1.7 million diet, diabetes, exercise, and obesity (DDEO)-related tweets collected over 12 months. Sentiment analysis and topic modeling were used to analyze the data. The results show that overall, 29% of the tweets were positive, and 17% were negative. Using sentiment analysis and latent Dirichlet allocation (LDA) topic modeling, we analyzed 800 positive and negative DDEO topics. From the 800 LDA topics—after the qualitative and computational removal of incoherent topics—473 topics were characterized as coherent. Obesity was the only query health topic with a higher percentage of negative tweets. The use of social media by public health practitioners should focus not only on the dissemination of health information based on the topics discovered but also consider what they can do for the health consumer as a result of the interaction in digital spaces such as social media. Future studies will benefit from using multiclass sentiment analysis methods associated with other novel topic modeling approaches.

Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work

Background

Millions of workers experience work-related ill health every year. The loss of working days often accounts for poor well-being because of discomfort and stress caused by the workplace. The ongoing pandemic and postpandemic shift in socioeconomic and work culture can continue to contribute to adverse work-related sentiments. Critically investigating state-of-the-art technologies, this study identifies the research gaps in recognizing workers’ need for well-being support, and we aspire to understand how such evidence can be collected to transform the workforce and workplace.

Objective

Building on recent advances in sentiment analysis, this study aims to closely examine the potential of social media as a tool to assess workers’ emotions toward the workplace.

Methods

This study collected a large Twitter data set comprising both pandemic and prepandemic tweets facilitated through a human-in-the-loop approach in combination with unsupervised learning and meta-heuristic optimization algorithms. The raw data preprocessed through natural language processing techniques were assessed using a generative statistical model and a lexicon-assisted rule-based model, mapping lexical features to emotion intensities. This study also assigned human annotations and performed work-related sentiment analysis.

Results

A mixed methods approach, including topic modeling using latent Dirichlet allocation, identified the top topics from the corpus to understand how Twitter users engage with discussions on work-related sentiments. The sorted aspects were portrayed through overlapped clusters and low intertopic distances. However, further analysis comprising the Valence Aware Dictionary for Sentiment Reasoner suggested a smaller number of negative polarities among diverse subjects. By contrast, the human-annotated data set created for this study contained more negative sentiments. In this study, sentimental juxtaposition revealed through the labeled data set was supported by the n-gram analysis as well.

Conclusions

The developed data set demonstrates that work-related sentiments are projected onto social media, which offers an opportunity to better support workers. The infrastructure of the workplace, the nature of the work, the culture within the industry and the particular organization, employers, colleagues, person-specific habits, and upbringing all play a part in the health and well-being of any working adult who contributes to the productivity of the organization. Therefore, understanding the origin and influence of the complex underlying factors both qualitatively and quantitatively can inform the next generation of workplaces to drive positive change by relying on empirically grounded evidence. Therefore, this study outlines a comprehensive approach to capture deeper insights into work-related health.

Increased Online Aggression During COVID-19 Lockdowns: Two-Stage Study of Deep Text Mining and Difference-in-Differences Analysis

BACKGROUND

The COVID-19 pandemic caused a critical public health crisis worldwide, and policymakers are using lockdowns to control the virus. However, there has been a noticeable increase in aggressive social behaviors that threaten social stability. Lockdown measures might negatively affect mental health and lead to an increase in aggressive emotions. Discovering the relationship between lockdown and increased aggression is crucial for formulating appropriate policies that address these adverse societal effects. We applied natural language processing (NLP) technology to internet data, so as to investigate the social and emotional impacts of lockdowns.

OBJECTIVE

This research aimed to understand the relationship between lockdown and increased aggression using NLP technology to analyze the following 3 kinds of aggressive emotions: anger, offensive language, and hate speech, in spatiotemporal ranges of tweets in the United States.

METHODS

We conducted a longitudinal internet study of 11,455 Twitter users by analyzing aggressive emotions in 1,281,362 tweets they posted from 2019 to 2020. We selected 3 common aggressive emotions (anger, offensive language, and hate speech) on the internet as the subject of analysis. To detect the emotions in the tweets, we trained a Bidirectional Encoder Representations from Transformers (BERT) model to analyze the percentage of aggressive tweets in every state and every week. Then, we used the difference-in-differences estimation to measure the impact of lockdown status on increasing aggressive tweets. Since most other independent factors that might affect the results, such as seasonal and regional factors, have been ruled out by time and state fixed effects, a significant result in this difference-in-differences analysis can not only indicate a concrete positive correlation but also point to a causal relationship.

RESULTS

In the first 6 months of lockdown in 2020, aggression levels in all users increased compared to the same period in 2019. Notably, users under lockdown demonstrated greater levels of aggression than those not under lockdown. Our difference-in-differences estimation discovered a statistically significant positive correlation between lockdown and increased aggression (anger: P=.002, offensive language: P<.001, hate speech: P=.005). It can be inferred from such results that there exist causal relations.

CONCLUSIONS

Understanding the relationship between lockdown and aggression can help policymakers address the personal and societal impacts of lockdown. Applying NLP technology and using big data on social media can provide crucial and timely information for this effort.

Tweets Related to Motivation and Physical Activity for Obesity-Related Behavior Change: Descriptive Analysis

BACKGROUND

Obesity is one of the greatest modern public health problems, due to the associated health and economic consequences. Decreased physical activity is one of the main societal changes driving the current obesity pandemic.

OBJECTIVE

Our goals are to fill a gap in the literature and study whether users organically utilize a social media platform, Twitter, for providing motivation. We examine the topics of messages and social network structures on Twitter. We discuss social media's potential for providing peer support and then draw insights to inform the development of interventions for long-term health-related behavior change.

METHODS

We examined motivational messages related to physical activity on Twitter. First, we collected tweets related to physical activity. Second, we analyzed them using (1) a lexicon-based approach to extract and characterize motivation-related tweets, (2) a thematic analysis to examine common themes in retweets, and (3) topic models to understand prevalent factors concerning motivation and physical activity on Twitter. Third, we created 2 social networks to investigate organically arising peer-support network structures for sustaining physical activity and to form a deeper understanding of the feasibility of these networks in a real-world context.

RESULTS

We collected over 1.5 million physical activity-related tweets posted from August 30 to November 6, 2018. A relatively small percentage of the tweets mentioned the term motivation; many of these were made on Mondays or during morning or late morning hours. The analysis of retweets showed that the following three themes were commonly conveyed on the platform: (1) using a number of different types of motivation (self, process, consolation, mental, or quotes), (2) promoting individuals or groups, and (3) sharing or requesting information. Topic models revealed that many of these users were weightlifters or people trying to lose weight. Twitter users also naturally forged relations, even though 98.12% (2824/2878) of these users were in different physical locations.

CONCLUSIONS

This study fills a knowledge gap on how individuals organically use social media to encourage and sustain physical activity. Elements related to peer support are found in the organic use of social media. Our findings suggest that geographical location is less important for providing peer support as long as the support provides motivation, despite users having few factors in common (eg, the weather) affecting their physical activity. This presents a unique opportunity to identify successful motivation-providing peer support groups in a large user base. However, further research on the effects in a real-world context, as well as additional design and usability features for improving user engagement, are warranted to develop a successful intervention counteracting the current obesity pandemic. This is especially important for young adults, the main user group for social media, as they develop lasting health-related behaviors.

A Hybrid Hand-Crafted and Deep Neural Spatio-Temporal EEG Features Clustering Framework for Precise Emotional Status Recognition

Human emotions are variant with time, non-stationary, complex in nature, and are invoked as a result of human reactions during our daily lives. Continuously detecting human emotions from one-dimensional EEG signals is an arduous task. This paper proposes an advanced signal processing mechanism for emotion detection from EEG signals using continuous wavelet transform. The space and time components of the raw EEG signals are converted into 2D spectrograms followed by feature extraction. A hybrid spatio-temporal deep neural network is implemented to extract rich features. A differential-based entropy feature selection technique adaptively differentiates features based on entropy, based on low and high information regions. Bag of Deep Features (BoDF) is applied to create clusters of similar features and computes the features vocabularies for reduction of feature dimensionality. Extensive experiments are performed on the SEED dataset, which shows the significance of the proposed method compared to state-of-the-art methods. Specifically, the proposed model achieved 96.7%, 96.2%, 95.8%, and 95.3% accuracy with the SJTU SEED dataset, for SVM, ensemble, tree, and KNN classifiers, respectively.

Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets

Background

The issue of food insecurity is becoming increasingly important to public health practitioners because of the adverse health outcomes and underlying racial disparities associated with insufficient access to healthy foods. Prior research has used data sources such as surveys, geographic information systems, and food store assessments to identify regions classified as food deserts but perhaps the individuals in these regions unknowingly provide their own accounts of food consumption and food insecurity through social media. Social media data have proved useful in answering questions related to public health; therefore, these data are a rich source for identifying food deserts in the United States.

Objective

The aim of this study was to develop, from geotagged Twitter data, a predictive model for the identification of food deserts in the United States using the linguistic constructs found in food-related tweets.

Methods

Twitter’s streaming application programming interface was used to collect a random 1% sample of public geolocated tweets across 25 major cities from March 2020 to December 2020. A total of 60,174 geolocated food-related tweets were collected across the 25 cities. Each geolocated tweet was mapped to its respective census tract using point-to-polygon mapping, which allowed us to develop census tract–level features derived from the linguistic constructs found in food-related tweets, such as tweet sentiment and average nutritional value of foods mentioned in the tweets. These features were then used to examine the associations between food desert status and the food ingestion language and sentiment of tweets in a census tract and to determine whether food-related tweets can be used to infer census tract–level food desert status.

Results

We found associations between a census tract being classified as a food desert and an increase in the number of tweets in a census tract that mentioned unhealthy foods (P=.03), including foods high in cholesterol (P=.02) or low in key nutrients such as potassium (P=.01). We also found an association between a census tract being classified as a food desert and an increase in the proportion of tweets that mentioned healthy foods (P=.03) and fast-food restaurants (P=.01) with positive sentiment. In addition, we found that including food ingestion language derived from tweets in classification models that predict food desert status improves model performance compared with baseline models that only include socioeconomic characteristics.

Conclusions

Social media data have been increasingly used to answer questions related to health and well-being. Using Twitter data, we found that food-related tweets can be used to develop models for predicting census tract food desert status with high accuracy and improve over baseline models. Food ingestion language found in tweets, such as census tract–level measures of food sentiment and healthiness, are associated with census tract–level food desert status.

Misinformation versus Facts: Understanding the Influence of News regarding COVID-19 Vaccines on Vaccine Uptake

Background

There is a lot of fact-based information and misinformation in the online discourses and discussions about the COVID-19 vaccines.

Method

Using a sample of nearly four million geotagged English tweets and the data from the CDC COVID Data Tracker, we conducted the Fama-MacBeth regression with the Newey-West adjustment to understand the influence of both misinformation and fact-based news on Twitter on the COVID-19 vaccine uptake in the US from April 19 when US adults were vaccine eligible to June 30, 2021, after controlling state-level factors such as demographics, education, and the pandemic severity. We identified the tweets related to either misinformation or fact-based news by analyzing the URLs.

Results

One percent increase in fact-related Twitter users is associated with an approximately 0.87 decrease (B = -0.87, SE = 0.25, and p < .001) in the number of daily new vaccinated people per hundred. No significant relationship was found between the percentage of fake-news-related users and the vaccination rate.

Conclusion

The negative association between the percentage of fact-related users and the vaccination rate might be due to a combination of a larger user-level influence and the negative impact of online social endorsement on vaccination intent.

Tracking Public Attitudes toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-based Sentiment Analysis

Background

The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation, poses a major barrier to achieving herd immunity.

Objective

We aim to investigate Twitter users’ attitudes toward COVID-19 vaccination in Canada after vaccine rollout.

Methods

We applied a weakly supervised aspect-based sentiment analysis (ABSA) technique, which involves the human-in-the-loop system, on COVID-19 vaccination–related tweets in Canada. Automatically generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiments toward “vaccination” changed over time. In addition, we analyzed the most retweeted or liked tweets by observing most frequent nouns and sentiments toward key aspects.

Results

After applying the ABSA system, we obtained 170 aspect terms (eg, “immunity” and “pfizer”) and 6775 opinion terms (eg, “trustworthy” for the positive sentiment and “jeopardize” for the negative sentiment). While manually verifying or editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for analysis. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to “vaccine distribution,” “side effects,” “allergy,” “reactions,” and “anti-vaxxer,” and positive sentiments related to “vaccine campaign,” “vaccine candidates,” and “immune response.” These results indicate that the Twitter users express concerns about the safety of vaccines but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of the remaining tweets, the most retweeted or liked tweets showed more positive sentiment overall toward key aspects (P<.001), especially vaccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines: the “anti-vaxxer” population that used negative sentiments as a means to discourage vaccination and the “Covid Zero” population that used negative sentiments to encourage vaccinations while critiquing the public health response.

Conclusions

Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination.

The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

Background

During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns.

Methods

COVID-19-related tweets from March 5th, 2020, to January 31st, 2021, were collected through Twitter streaming API using keywords (i.e., "corona," "covid19," and "covid"). By further filtering using keywords (i.e., "depress," "failure," and "hopeless"), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users' discussions surrounding mental health concerns. Deep learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the pandemic.

Results

We observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that "stay-at-home," "death poll," and "politics and policy" were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns.

Conclusions

The COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males and White) were more likely to have mental health concerns during the COVID-19 pandemic.

Mediterranean Diet Social Network Impact along 11 Years in the Major US Media Outlets: Thematic and Quantitative Analysis Using Twitter

Background: Media outlets influence social attitudes toward health. Thus, it is important that they share contents which promote healthy habits. The Mediterranean diet (MedDiet) is associated with lower cardiovascular disease risk. Analysis of tweets has become a tool for understanding perceptions on health issues. Methods: We investigated tweets posted between January 2009 and December 2019 by 25 major US media outlets about MedDiet and its components as well as the retweets and likes generated. In addition, we measured the sentiment analysis of these tweets and their dissemination. Results: In total, 1608 tweets, 123,363 likes and 48,946 retweets about MedDiet or its components were analyzed. Dairy (inversely weighted in MedDiet scores) accounted for 45.0% of the tweets (723/1608), followed by nuts 19.7% (317/1608). MedDiet, as an overall dietary pattern, generated only 9.8% (157/1608) of the total tweets, while olive oil generated the least number of tweets. Twitter users’ response was quantitatively related to the number of tweets posted by these US media outlets, except for tweets on olive oil and MedDiet. None of the MedDiet components analyzed was more likely to be liked or retweeted than the MedDiet itself. Conclusions: The US media outlets analyzed showed reduced interest in MedDiet as a whole, while Twitter users showed greater interest in the overall dietary pattern than in its particular components.

Social media analysis of Twitter tweets related to ASD in 2019-2020, with particular attention to COVID-19: topic modelling and sentiment analysis

BACKGROUND

Social media contains an overabundance of health information relating to people living with different type of diseases. Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with lifelong impacts and reported trends have revealed a considerable increase in prevalence and incidence. Research had shown that the ASD community provides significant support to its members through Twitter, providing information about their values and perceptions through their use of words and emotional stance. Our purpose was to analyze all the messages posted on Twitter platform regarding ASD and analyze the topics covered within the tweets, to understand the attitude of the various people interested in the topic. In particular, we focused on the discussion of ASD and COVID-19.

METHODS

The data collection process was based on the search for tweets through hashtags and keywords. After bots screening, the NMF (Non-Negative Matrix Factorization) method was used for topic modeling because it produces more coherent topics compared to other solutions. Sentiment scores were calculated using AFiNN for each tweet to represent its negative to positive emotion.

RESULTS

From the 2.458.929 tweets produced in 2020, 691.582 users were extracted (188 bots which generated 59.104 tweets), while from the 2.393.236 total tweets from 2019, the number of identified users was 684.032 (230 bots which generated 50.057 tweets). The total number of COVID-ASD tweets is only a small part of the total dataset. Often, the negative sentiment identified in the sentiment analysis referred to anger towards COVID-19 and its management, while the positive sentiment reflected the necessity to provide constant support to people with ASD.

CONCLUSIONS

Social media contributes to a great discussion on topics related to autism, especially with regards to focus on family, community, and therapies. The COVID-19 pandemic increased the use of social media, especially during the lockdown period. It is important to help develop and distribute appropriate, evidence-based ASD-related information.

Expectations, concerns and experiences of rehabilitation patients during the COVID-19 pandemic in Germany: a qualitative analysis of online forum posts

Background

The COVID-19 pandemic, as well as efforts to prevent its spread, have had a strong impact on the delivery of rehabilitative services in Germany. While several studies have addressed the impact of these developments on health service providers and COVID-19 patients, little is known about its impact on patients in need of rehabilitative treatment because of other conditions. This study aims to identify expectations, concerns and experiences of rehabilitation patients related to service delivery in this situation.

Methods

Using a qualitative study design, user posts from six German online forums between March and Mid-November 2020 were systematically searched with respect to experiences, concerns and expectations of health care users toward receiving rehabilitative treatment. We used qualitative content analysis with inductive coding as our methodological approach.

Results

Users fearing physical or psychological impairment were concerned about not receiving timely or effective treatment due to closed hospitals, reduced treatments and limited admissions. In contrast, patients more concerned about getting infected with COVID-19 worried about the effectiveness of protective measures and being denied postponement of treatment by the funding bodies. During their stay, some patients reported feeling isolated due to contact restrictions and did not feel their treatment was effective, while others reported being satisfied and praised hospitals for their efforts to ensure the safety of the patients. Many patients reported communication problems before and during their treatment, including concerns about the safety and effectiveness of their treatment, as well as financial concerns and worries about future treatments. Several users felt that their concerns were disregarded by the hospitals and the funding bodies, leaving them feeling distressed, insecure and dissatisfied.

Conclusions

While some users report only minor concerns related to the pandemic and its impact on rehabilitation, others report strong concerns relating not only to their own health and safety, but also to financial aspects and their ability to work. Many users feel ignored and disregarded, showing a strong need for more coordinated strategies and improved communication specifically with funding bodies like health insurance companies and the German pension funds.

Emotional Tone, Analytical Thinking, and Somatosensory Processes of a Sample of Italian Tweets During the First Phases of the COVID-19 Pandemic: Observational Study

BACKGROUND

The COVID-19 pandemic is a traumatic individual and collective chronic experience, with tremendous consequences on mental and psychological health that can also be reflected in people's use of words. Psycholinguistic analysis of tweets from Twitter allows obtaining information about people's emotional expression, analytical thinking, and somatosensory processes, which are particularly important in traumatic events contexts.

OBJECTIVE

We aimed to analyze the influence of official Italian COVID-19 daily data (new cases, deaths, and hospital discharges) and the phase of managing the pandemic on how people expressed emotions and their analytical thinking and somatosensory processes in Italian tweets written during the first phases of the COVID-19 pandemic in Italy.

METHODS

We retrieved 1,697,490 Italian COVID-19-related tweets written from February 24, 2020 to June 14, 2020 and analyzed them using LIWC2015 to calculate 3 summary psycholinguistic variables: emotional tone, analytical thinking, and somatosensory processes. Official daily data about new COVID-19 cases, deaths, and hospital discharges were retrieved from the Italian Prime Minister's Office and Civil Protection Department GitHub page. We considered 3 phases of managing the COVID-19 pandemic in Italy. We performed 3 general models, 1 for each summary variable as the dependent variable and with daily data and phase of managing the pandemic as independent variables.

RESULTS

General linear models to assess differences in daily scores of emotional tone, analytical thinking, and somatosensory processes were significant (F6,104=21.53, P<.001, R2= .55; F5,105=9.20, P<.001, R2= .30; F6,104=6.15, P<.001, R2=.26, respectively).

CONCLUSIONS

The COVID-19 pandemic affects how people express emotions, analytical thinking, and somatosensory processes in tweets. Our study contributes to the investigation of pandemic psychological consequences through psycholinguistic analysis of social media textual data.

The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses

BACKGROUND

The COVID-19 pandemic has affected populations worldwide, with extreme health, economic, social, and political implications. Health care professionals (HCPs) are at the core of pandemic response and are among the most crucial factors in maintaining coping capacities. Yet, they are also vulnerable to mental health effects caused by managing a long-lasting emergency with a lack of resources and under complicated personal concerns. However, there are a lack of longitudinal studies that investigate the HCP population.

OBJECTIVE

The aim of this study was to analyze the state of mind of HCPs as expressed in online discussions published on Twitter in light of the COVID-19 pandemic, from the onset of the pandemic until the end of 2020.

METHODS

The population for this study was selected from followers of a few hundred Twitter accounts of health care organizations and common HCP points of interest. We used active learning, a process that iteratively uses machine learning and manual data labeling, to select the large-scale population of Twitter accounts maintained by English-speaking HCPs, focusing on individuals rather than official organizations. We analyzed the topics and emotions in their discourses during 2020. The topic distributions were obtained using the latent Dirichlet allocation algorithm. We defined a measure of topic cohesion and described the most cohesive topics. The emotions expressed in tweets during 2020 were compared to those in 2019. Finally, the emotion intensities were cross-correlated with the pandemic waves to explore possible associations between the pandemic development and emotional response.

RESULTS

We analyzed the timelines of 53,063 Twitter profiles, 90% of which were maintained by individual HCPs. Professional topics accounted for 44.5% of tweets by HCPs from January 1, 2019, to December 6, 2020. Events such as the pandemic waves, US elections, or the George Floyd case affected the HCPs' discourse. The levels of joy and sadness exceeded their minimal and maximal values from 2019, respectively, 80% of the time (P=.001). Most interestingly, fear preceded the pandemic waves, in terms of the differences in confirmed cases, by 2 weeks with a Spearman correlation coefficient of ρ(47 pairs)=0.340 (P=.03).

CONCLUSIONS

Analyses of longitudinal data over the year 2020 revealed that a large fraction of HCP discourse is directly related to professional content, including the increase in the volume of discussions following the pandemic waves. The changes in emotional patterns (ie, decrease in joy and increase in sadness, fear, and disgust) during the year 2020 may indicate the utmost importance in providing emotional support for HCPs to prevent fatigue, burnout, and mental health disorders during the postpandemic period. The increase in fear 2 weeks in advance of pandemic waves indicates that HCPs are in a position, and with adequate qualifications, to anticipate pandemic development, and could serve as a bottom-up pathway for expressing morbidity and clinical situations to health agencies.

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

BACKGROUND

As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter.

OBJECTIVE

The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media.

METHODS

To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity.

RESULTS

After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy.

CONCLUSIONS

This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.

Investigating the Impact of New York State Flavor Ban on E-cigarettes Discussion on Twitter: Observational Study (Preprint)

Background

On May 18, 2020, the New York State Department of Health implemented a statewide flavor ban to prohibit the sales of all flavored vapor products, except for tobacco or any other authorized flavor.

Objective

This study aims to investigate the discussion changes in e-cigarette–related tweets over time with the implementation of the New York State flavor ban.

Methods

Through the Twitter streaming application programming interface, 59,883 e-cigarette–related tweets were collected within the New York State from February 6, 2020, to May 17, 2020 (period 1, before the implementation of the flavor ban), May 18, 2020-June 30, 2020 (period 2, between the implementation of the flavor ban and the online sales ban), July 1, 2020-September 15, 2020 (period 3, the short term after the online sales ban), and September 16, 2020-November 30, 2020 (period 4, the long term after the online sales ban). Sentiment analysis and topic modeling were conducted to investigate the changes in public attitudes and discussions in e-cigarette–related tweets. The popularity of different e-cigarette flavor categories was compared before and after the implementation of the New York State flavor ban.

Results

Our results showed that the proportion of e-cigarette–related tweets with negative sentiment significantly decreased (4305/13,246, 32.5% vs 3855/14,455, 26.67%, P<.001), and tweets with positive sentiment significantly increased (5246/13,246, 39.6% vs 7038/14,455, 48.69%, P<.001) in period 4 compared to period 3. “Teens and nicotine products” was the most frequently discussed e-cigarette–related topic in the negative tweets. In contrast, “nicotine products and quitting” was more prevalent in positive tweets. The proportion of tweets mentioning mint and menthol flavors significantly increased right after the flavor ban and decreased to lower levels over time. The proportions of fruit and sweet flavors were most frequently mentioned in period 1, decreased in period 2, and dominated again in period 4.

Conclusions

The proportion of e-cigarette–related tweets with different attitudes and frequently discussed flavor categories changed over time after the implementation of the New York State ban of flavored vaping products. This change indicated a potential impact of the flavor ban on public discussions of flavored e-cigarettes.

Public Sentiment and Discourse on Domestic Violence During the COVID-19 Pandemic in Australia: Analysis of Social Media Posts

BACKGROUND

Measuring public response during COVID-19 is an important way of ensuring the suitability and effectiveness of epidemic response efforts. An analysis of social media provides an approximation of public sentiment during an emergency like the current pandemic. The measures introduced across the globe to help curtail the spread of the coronavirus have led to the development of a situation labeled as a "perfect storm," triggering a wave of domestic violence. As people use social media to communicate their experiences, analyzing public discourse and sentiment on social platforms offers a way to understand concerns and issues related to domestic violence during the COVID-19 pandemic.

OBJECTIVE

This study was based on an analysis of public discourse and sentiment related to domestic violence during the stay-at-home periods of the COVID-19 pandemic in Australia in 2020. It aimed to understand the more personal self-reported experiences, emotions, and reactions toward domestic violence that were not always classified or collected by official public bodies during the pandemic.

METHODS

We searched social media and news posts in Australia using key terms related to domestic violence and COVID-19 during 2020 via digital analytics tools to determine sentiments related to domestic violence during this period.

RESULTS

The study showed that the use of sentiment and discourse analysis to assess social media data is useful in measuring the public expression of feelings and sharing of resources in relation to the otherwise personal experience of domestic violence. There were a total of 63,800 posts across social media and news media. Within these posts, our analysis found that domestic violence was mentioned an average of 179 times a day. There were 30,100 tweets, 31,700 news reports, 1500 blog posts, 548 forum posts, and 7 comments (posted on news and blog websites). Negative or neutral sentiment centered on the sharp rise in domestic violence during different lockdown periods of the 2020 pandemic, and neutral and positive sentiments centered on praise for efforts that raised awareness of domestic violence as well as the positive actions of domestic violence charities and support groups in their campaigns. There were calls for a positive and proactive handling (rather than a mishandling) of the pandemic, and results indicated a high level of public discontent related to the rising rates of domestic violence and the lack of services during the pandemic.

CONCLUSIONS

This study provided a timely understanding of public sentiment related to domestic violence during the COVID-19 lockdown periods in Australia using social media analysis. Social media represents an important avenue for the dissemination of information; posts can be widely dispersed and easily accessed by a range of different communities who are often difficult to reach. An improved understanding of these issues is important for future policy direction. Heightened awareness of this could help agencies tailor and target messaging to maximize impact.

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

BACKGROUND

The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance.

OBJECTIVE

The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter.

METHODS

We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes.

RESULTS

An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis.

CONCLUSIONS

The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study

Background

The early conversations on social media by emergency physicians offer a window into the ongoing response to the COVID-19 pandemic.

Objective

This retrospective observational study of emergency physician Twitter use details how the health care crisis has influenced emergency physician discourse online and how this discourse may have use as a harbinger of ensuing surge.

Methods

Followers of the three main emergency physician professional organizations were identified using Twitter’s application programming interface. They and their followers were included in the study if they identified explicitly as US-based emergency physicians. Statuses, or tweets, were obtained between January 4, 2020, when the new disease was first reported, and December 14, 2020, when vaccination first began. Original tweets underwent sentiment analysis using the previously validated Valence Aware Dictionary and Sentiment Reasoner (VADER) tool as well as topic modeling using latent Dirichlet allocation unsupervised machine learning. Sentiment and topic trends were then correlated with daily change in new COVID-19 cases and inpatient bed utilization.

Results

A total of 3463 emergency physicians produced 334,747 unique English-language tweets during the study period. Out of 3463 participants, 910 (26.3%) stated that they were in training, and 466 of 902 (51.7%) participants who provided their gender identified as men. Overall tweet volume went from a pre-March 2020 mean of 481.9 (SD 72.7) daily tweets to a mean of 1065.5 (SD 257.3) daily tweets thereafter. Parameter and topic number tuning led to 20 tweet topics, with a topic coherence of 0.49. Except for a week in June and 4 days in November, discourse was dominated by the health care system (45,570/334,747, 13.6%). Discussion of pandemic response, epidemiology, and clinical care were jointly found to moderately correlate with COVID-19 hospital bed utilization (Pearson r=0.41), as was the occurrence of “covid,” “coronavirus,” or “pandemic” in tweet texts (r=0.47). Momentum in COVID-19 tweets, as demonstrated by a sustained crossing of 7- and 28-day moving averages, was found to have occurred on an average of 45.0 (SD 12.7) days before peak COVID-19 hospital bed utilization across the country and in the four most contributory states.

Conclusions

COVID-19 Twitter discussion among emergency physicians correlates with and may precede the rising of hospital burden. This study, therefore, begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online and suggests a potential avenue for understanding predictors of surge.

Declining well-being during the COVID-19 pandemic reveals US social inequities

Background

The COVID-19 pandemic led to mental health fallout in the US; yet research about mental health and COVID-19 primarily rely on samples that may overlook variance in regional mental health. Indeed, between-city comparisons of mental health decline in the US may provide further insight into how the pandemic is disproportionately affecting at-risk groups.

Purpose

This study leverages social media and COVID-19-city infection data to measure the longitudinal (January 22- July 31, 2020) mental health effects of the COVID-19 pandemic in 20 metropolitan areas.

Methods

We used longitudinal VADER sentiment analysis of Twitter timelines (January-July 2020) for cohorts in 20 metropolitan areas to examine mood changes over time. We then conducted simple and multivariate Ordinary Least Squares (OLS) regressions to examine the relationship between COVID-19 infection city data, population, population density, and city demographics on sentiment across those 20 cities.

Results

Longitudinal sentiment tracking showed mood declines over time. The univariate OLS regression highlighted a negative linear relationship between COVID-19 city data and online sentiment (β = -.017). Residing in predominantly white cities had a protective effect against COVID-19 driven negative mood (β = .0629, p < .001).

Discussion

Our results reveal that metropolitan areas with larger communities of color experienced a greater subjective well-being decline than predominantly white cities, which we attribute to clinical and socioeconomic correlates that place communities of color at greater risk of COVID-19.

Conclusion

The COVID-19 pandemic is a driver of declining US mood in 20 metropolitan cities. Other factors, including social unrest and local demographics, may compound and exacerbate mental health outlook in racially diverse cities.

COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis

BACKGROUND

Vaccination is a cornerstone of the prevention of communicable infectious diseases; however, vaccines have traditionally met with public fear and hesitancy, and COVID-19 vaccines are no exception. Social media use has been demonstrated to play a role in the low acceptance of vaccines.

OBJECTIVE

The aim of this study is to identify the topics and sentiments in the public COVID-19 vaccine-related discussion on social media and discern the salient changes in topics and sentiments over time to better understand the public perceptions, concerns, and emotions that may influence the achievement of herd immunity goals.

METHODS

Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, the day the World Health Organization declared COVID-19 a pandemic, to January 31, 2021. We used R software to clean the tweets and retain tweets that contained the keywords vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. The final data set included in the analysis consisted of 1,499,421 unique tweets from 583,499 different users. We used R to perform latent Dirichlet allocation for topic modeling as well as sentiment and emotion analysis using the National Research Council of Canada Emotion Lexicon.

RESULTS

Topic modeling of tweets related to COVID-19 vaccines yielded 16 topics, which were grouped into 5 overarching themes. Opinions about vaccination (227,840/1,499,421 tweets, 15.2%) was the most tweeted topic and remained a highly discussed topic during the majority of the period of our examination. Vaccine progress around the world became the most discussed topic around August 11, 2020, when Russia approved the world's first COVID-19 vaccine. With the advancement of vaccine administration, the topic of instruction on getting vaccines gradually became more salient and became the most discussed topic after the first week of January 2021. Weekly mean sentiment scores showed that despite fluctuations, the sentiment was increasingly positive in general. Emotion analysis further showed that trust was the most predominant emotion, followed by anticipation, fear, sadness, etc. The trust emotion reached its peak on November 9, 2020, when Pfizer announced that its vaccine is 90% effective.

CONCLUSIONS

Public COVID-19 vaccine-related discussion on Twitter was largely driven by major events about COVID-19 vaccines and mirrored the active news topics in mainstream media. The discussion also demonstrated a global perspective. The increasingly positive sentiment around COVID-19 vaccines and the dominant emotion of trust shown in the social media discussion may imply higher acceptance of COVID-19 vaccines compared with previous vaccines.

Language and Sentiment Regarding Telemedicine and COVID-19 on Twitter: Longitudinal Infodemiology Study

Background

The COVID-19 pandemic has necessitated a rapid shift in how individuals interact with and receive fundamental services, including health care. Although telemedicine is not a novel technology, previous studies have offered mixed opinions surrounding its utilization. However, there exists a dearth of research on how these opinions have evolved over the course of the current pandemic.

Objective

This study aims to evaluate how the language and sentiment surrounding telemedicine has evolved throughout the COVID-19 pandemic.

Methods

Tweets published between January 1, 2020, and April 24, 2021, containing at least one telemedicine-related and one COVID-19–related search term (“telemedicine-COVID”) were collected from the Twitter full archive search (N=351,718). A comparator sample containing only COVID-19 terms (“general-COVID”) was collected and sampled based on the daily distribution of telemedicine-COVID tweets. In addition to analyses of retweets and favorites, sentiment analysis was performed on both data sets in aggregate and within a subset of tweets receiving the top 100 most and least retweets.

Results

Telemedicine gained prominence during the early stages of the pandemic (ie, March through May 2020) before leveling off and reaching a steady state from June 2020 onward. Telemedicine-COVID tweets had a 21% lower average number of retweets than general-COVID tweets (incidence rate ratio 0.79, 95% CI 0.63-0.99; P=.04), but there was no difference in favorites. A majority of telemedicine-COVID tweets (180,295/351,718, 51.3%) were characterized as “positive,” compared to only 38.5% (135,434/351,401) of general-COVID tweets (P<.001). This trend was also true on a monthly level from March 2020 through April 2021. The most retweeted posts in both telemedicine-COVID and general-COVID data sets were authored by journalists and politicians. Whereas the majority of the most retweeted posts within the telemedicine-COVID data set were positive (55/101, 54.5%), a plurality of the most retweeted posts within the general-COVID data set were negative (44/89, 49.4%; P=.01).

Conclusions

During the COVID-19 pandemic, opinions surrounding telemedicine evolved to become more positive, especially when compared to the larger pool of COVID-19–related tweets. Decision makers should capitalize on these shifting public opinions to invest in telemedicine infrastructure and ensure its accessibility and success in a postpandemic world.

Indications of Depressive Symptoms During the COVID-19 Pandemic in Germany: Comparison of National Survey and Twitter Data

BACKGROUND

The current COVID-19 pandemic is associated with extensive individual and societal challenges, including challenges to both physical and mental health. To date, the development of mental health problems such as depressive symptoms accompanying population-based federal distancing measures is largely unknown, and opportunities for rapid, effective, and valid monitoring are currently a relevant matter of investigation.

OBJECTIVE

In this study, we aim to investigate, first, the temporal progression of depressive symptoms during the COVID-19 pandemic and, second, the consistency of the results from tweets and survey-based self-reports of depressive symptoms within the same time period.

METHODS

Based on a cross-sectional population survey of 9011 German adolescents and adults (n=4659, 51.7% female; age groups from 15 to 50 years and older) and a sample of 88,900 tweets (n=74,587, 83.9% female; age groups from 10 to 50 years and older), we investigated five depressive symptoms (eg, depressed mood and energy loss) using items from the Patient Health Questionnaire (PHQ-8) before, during, and after relaxation of the first German social contact ban from January to July 2020.

RESULTS

On average, feelings of worthlessness were the least frequently reported symptom (survey: n=1011, 13.9%; Twitter: n=5103, 5.7%) and fatigue or loss of energy was the most frequently reported depressive symptom (survey: n=4472, 51.6%; Twitter: n=31,005, 34.9%) among both the survey and Twitter respondents. Young adult women and people living in federal districts with high COVID-19 infection rates were at an increased risk for depressive symptoms. The comparison of the survey and Twitter data before and after the first contact ban showed that German adolescents and adults had a significant decrease in feelings of fatigue and energy loss over time. The temporal progression of depressive symptoms showed high correspondence between both data sources (ρ=0.76-0.93; P<.001), except for diminished interest and depressed mood, which showed a steady increase even after the relaxation of the contact ban among the Twitter respondents but not among the survey respondents.

CONCLUSIONS

Overall, the results indicate relatively small differences in depressive symptoms associated with social distancing measures during the COVID-19 pandemic and highlight the need to differentiate between positive (eg, energy level) and negative (eg, depressed mood) associations and variations over time. The results also underscore previous suggestions of Twitter data's potential to help identify hot spots of declining and improving public mental health and thereby help provide early intervention measures, especially for young and middle-aged adults. Further efforts are needed to investigate the long-term consequences of recurring lockdown phases and to address the limitations of social media data such as Twitter data to establish real-time public mental surveillance approaches.

Topics of Nicotine-Related Discussions on Twitter: Infoveillance Study

BACKGROUND

Cultural trends in the United States, the nicotine consumer marketplace, and tobacco policies are changing.

OBJECTIVE

The goal of this study was to identify and describe nicotine-related topics of conversation authored by the public and social bots on Twitter, including any misinformation or misconceptions that health education campaigns could potentially correct.

METHODS

Twitter posts containing the term "nicotine" were obtained from September 30, 2018 to October 1, 2019. Methods were used to distinguish between posts from social bots and nonbots. Text classifiers were used to identify topics in posts (n=300,360).

RESULTS

Prevalent topics of posts included vaping, smoking, addiction, withdrawal, nicotine health risks, and quit nicotine, with mentions of going "cold turkey" and needing help in quitting. Cessation was a common topic, with mentions of quitting and stopping smoking. Social bots discussed unsubstantiated health claims including how hypnotherapy, acupuncture, magnets worn on the ears, and time spent in the sauna can help in smoking cessation.

CONCLUSIONS

Health education efforts are needed to correct unsubstantiated health claims on Twitter and ultimately direct individuals who want to quit smoking to evidence-based cessation strategies. Future interventions could be designed to follow these topics of discussions on Twitter and engage with members of the public about evidence-based cessation methods in near real time when people are contemplating cessation.

Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?

Twitter’s APIs are now the main data source for social media researchers. A large number of studies have utilized Twitter data for diverse research interests. Twitter users can share their precise real-time location, and Twitter APIs can provide this information as longitude and latitude. These geotagged Twitter data can help to study human activities and movements for different applications. Compared to the mostly small-scale data samples in different domains, such as social science, collecting geotagged data offers large samples. There is a fundamental question whether geotagged users can represent non-geotagged users. While some studies have investigated the question from different perspectives, they did not investigate profile information and the contents of tweets of geotagged and non-geotagged users. This empirical study addresses this limitation by applying text mining, statistical analysis, and machine learning techniques on Twitter data comprising more than 88,000 users and over 170 million tweets. Our findings show that there is a significant difference (p-value < 0.001) between geotagged and non-geotagged users based on 73% of the features obtained from the users’ profiles and tweets. The features can also help to distinguish between geotagged and non-geotagged users with around 80% accuracy. This research illustrates that geotagged users do not represent the Twitter population.

Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence-Enabled Social Media Analysis

BACKGROUND

The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2.

OBJECTIVE

In this study, we sought to explore the suitability of artificial intelligence (AI)-enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom.

METHODS

We extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19-related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app-related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning-based approaches.

RESULTS

Overall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology.

CONCLUSIONS

Variations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns.

Determination of Patient Sentiment and Emotion in Ophthalmology: Infoveillance Tutorial on Web-Based Health Forum Discussions

Background

Clinical data in social media are an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes, and preferences.

Objective

This tutorial aims to describe a novel, robust, and modular method for the sentiment analysis and emotion detection of free text from web-based forums and the factors to consider during its application.

Methods

We mined the discussion and user information of all posts containing search terms related to a medical subspecialty (oculoplastics) from MedHelp, the largest web-based platform for patient health forums. We used data cleaning and processing tools to define the relevant subset of results and prepare them for sentiment analysis. We executed sentiment and emotion analyses by using IBM Watson Natural Language Understanding to generate sentiment and emotion scores for the posts and their associated keywords. The keywords were aggregated using natural language processing tools.

Results

Overall, 39 oculoplastic-related search terms resulted in 46,381 eligible posts within 14,329 threads. Posts were written by 18,319 users (117 doctors; 18,202 patients) and included 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify the most prominent topics, including specific symptoms, medication, and complications. The sentiment and emotion scores of these keywords and eligible posts were analyzed to provide concrete examples of the potential of this methodology to allow for a better understanding of patients’ attitudes. The overall sentiment score reflects a positive, neutral, or negative sentiment, whereas the emotion scores (anger, disgust, fear, joy, and sadness) represent the likelihood of the presence of the emotion. In keyword grouping analyses, medical signs, symptoms, and diseases had the lowest overall sentiment scores (−0.598). Complications were highly associated with sadness (0.485). Forum posts mentioning body parts were related to sadness (0.416) and fear (0.321). Administration was the category with the highest anger score (0.146). The top 6 forum subgroups had an overall negative sentiment score; the most negative one was the Neurology forum, with a score of −0.438. The Undiagnosed Symptoms forum had the highest sadness score (0.448). The least likely fearful posts were those from the Eye Care forum, with a score of 0.260. The overall sentiment score was much more negative before the doctor replied. The anger, disgust, fear, and sadness emotion scores decreased in likelihood, whereas joy was slightly more likely to be expressed after doctors replied.

Conclusions

This report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during its application include evaluating the scope of the search; selecting search terms and understanding their linguistic usages; and establishing selection, filtering, and processing criteria for posts and keywords tailored to the desired results.

Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis

Background

Suicide is a serious public health issue, accounting for 1.4% of all deaths worldwide. Current risk assessment tools are reported as performing little better than chance in predicting suicide. New methods for studying dynamic features in electronic health records (EHRs) are being increasingly explored. One avenue of research involves using sentiment analysis to examine clinicians’ subjective judgments when reporting on patients. Several recent studies have used general-purpose sentiment analysis tools to automatically identify negative and positive words within EHRs to test correlations between sentiment extracted from the texts and specific medical outcomes (eg, risk of suicide or in-hospital mortality). However, little attention has been paid to analyzing the specific words identified by general-purpose sentiment lexicons when applied to EHR corpora.

Objective

This study aims to quantitatively and qualitatively evaluate the coverage of six general-purpose sentiment lexicons against a corpus of EHR texts to ascertain the extent to which such lexical resources are fit for use in suicide risk assessment.

Methods

The data for this study were a corpus of 198,451 EHR texts made up of two subcorpora drawn from a 1:4 case-control study comparing clinical notes written over the period leading up to a suicide attempt (cases, n=2913) with those not preceding such an attempt (controls, n=14,727). We calculated word frequency distributions within each subcorpus to identify representative keywords for both the case and control subcorpora. We quantified the relative coverage of the 6 lexicons with respect to this list of representative keywords in terms of weighted precision, recall, and F score.

Results

The six lexicons achieved reasonable precision (0.53-0.68) but very low recall (0.04-0.36). Many of the most representative keywords in the suicide-related (case) subcorpus were not identified by any of the lexicons. The sentiment-bearing status of these keywords for this use case is thus doubtful.

Conclusions

Our findings indicate that these 6 sentiment lexicons are not optimal for use in suicide risk assessment. We propose a set of guidelines for the creation of more suitable lexical resources for distinguishing suicide-related from non–suicide-related EHR texts.

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Background

Social media is a rich source where we can learn about people’s reactions to social issues. As COVID-19 has impacted people’s lives, it is essential to capture how people react to public health interventions and understand their concerns.

Objective

We aim to investigate people’s reactions and concerns about COVID-19 in North America, especially in Canada.

Methods

We analyzed COVID-19–related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people’s sentiment about COVID-19–related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians.

Results

Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, “vaccines,” “economy,” and “masks”) and 60 opinion terms such as “infectious” (negative) and “professional” (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing.

Conclusions

Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19–related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.

Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data

BACKGROUND

Previous studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic.

OBJECTIVE

In this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data.

METHODS

The study dataset containing COVID-19-related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19-related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette-related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette-related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups.

RESULTS

The US COVID-19 dataset consisted of 4,500,248 COVID-19-related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19-related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government's responses to the COVID-19 pandemic.

CONCLUSIONS

Our findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.

Difficulty Regulating Social Media Content of Age-Restricted Products: Comparing JUUL's Official Twitter Timeline and Social Media Content About JUUL

Background

In 2018, JUUL Labs Inc, a popular e-cigarette manufacturer, announced it would substantially limit its social media presence in compliance with the Food and Drug Administration's (FDA) call to curb underage e-cigarette use. However, shortly after the announcement, a series of JUUL-related hashtags emerged on various social media platforms, calling the effectiveness of the FDA's regulations into question.

Objective

The purpose of this study is to determine whether hashtags remain a common venue to market age-restricted products on social media.

Methods

We used Twitter's standard application programming interface to download the 3200 most-recent tweets originating from JUUL Labs Inc's official Twitter Account (@JUULVapor), and a series of tweets (n=28,989) from other Twitter users containing either #JUUL or mentioned JUUL in the tweet text. We ran exploratory (10×10) and iterative Latent Dirichlet Allocation (LDA) topic models to compare @JUULVapor's content versus our hashtag corpus. We qualitatively deliberated topic meanings and substantiated our interpretations with tweets from either corpus.

Results

The topic models generated for @JUULVapor's timeline seemingly alluded to compliance with the FDA's call to prohibit marketing of age-restricted products on social media. However, the topic models generated for the hashtag corpus of tweets from other Twitter users contained several references to flavors, vaping paraphernalia, and illicit drugs, which may be appealing to younger audiences.

Conclusions

Our findings underscore the complicated nature of social media regulation. Although JUUL Labs Inc seemingly complied with the FDA to limit its social media presence, JUUL and other e-cigarette manufacturers are still discussed openly in social media spaces. Much discourse about JUUL and e-cigarettes is spread via hashtags, which allow messages to reach a wide audience quickly. This suggests that social media regulations on manufacturers cannot prevent e-cigarette users, influencers, or marketers from spreading information about e-cigarette attributes that appeal to the youth, such as flavors. Stricter protocols are needed to regulate discourse about age-restricted products on social media.

Long Covid: Online patient narratives, public health communication and vaccine hesitancy

INTRODUCTION

This study combines quantitative and qualitative analyses of social media data collected through three key stages of the pandemic, to highlight the following: 'First wave' (March to May, 2020): negative consequences arising from a disconnect between official health communications, and unofficial Long Covid sufferers' narratives online.'Second wave' (October 2020 to January 2021): closing the 'gap' between official health communications and unofficial patient narratives, leading to a better integration between patient voice, research and services.'Vaccination phase' (January 2021, early stages of the vaccination programme in the UK): continuing and new emerging concerns.

METHODS

We adopted a mixed methods approach involving quantitative and qualitative analyses of 1.38 million posts mentioning long-term symptoms of Covid-19, gathered across social media and news platforms between 1 January 2020 and 1 January 2021, on Twitter, Facebook, Blogs, and Forums. Our inductive thematic analysis was informed by our discourse analysis of words, and sentiment analysis of hashtags and emojis.

RESULTS

Results indicate that the negative impacts arise mostly from conflicting definitions of Covid-19 and fears around the Covid-19 vaccine for Long Covid sufferers. Key areas of concern are: time/duration; symptoms/testing; emotional impact; lack of support and resources.

CONCLUSIONS

Whilst Covid-19 is a global issue, specific sociocultural, political and economic contexts mean patients experience Long Covid at a localised level, needing appropriate localised responses. This can only happen if we build a knowledge base that begins with the patient, ultimately informing treatment and rehabilitation strategies for Long Covid.

Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data

BACKGROUND

The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world's mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population's mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being.

OBJECTIVE

This study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic?

METHODS

We analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool.

RESULTS

LDA topics generated in the early months of the data set corresponded to major COVID-19-specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March.

CONCLUSIONS

Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.

Tweets by People With Arthritis During the COVID-19 Pandemic: Content and Sentiment Analysis

BACKGROUND

Emerging evidence suggests that people with arthritis are reporting increased physical pain and psychological distress during the COVID-19 pandemic. At the same time, Twitter's daily usage has surged by 23% throughout the pandemic period, presenting a unique opportunity to assess the content and sentiment of tweets. Individuals with arthritis use Twitter to communicate with peers, and to receive up-to-date information from health professionals and services about novel therapies and management techniques.

OBJECTIVE

The aim of this research was to identify proxy topics of importance for individuals with arthritis during the COVID-19 pandemic, and to explore the emotional context of tweets by people with arthritis during the early phase of the pandemic.

METHODS

From March 20 to April 20, 2020, publicly available tweets posted in English and with hashtag combinations related to arthritis and COVID-19 were extracted retrospectively from Twitter. Content analysis was used to identify common themes within tweets, and sentiment analysis was used to examine positive and negative emotions in themes to understand the COVID-19 experiences of people with arthritis.

RESULTS

In total, 149 tweets were analyzed. The majority of tweeters were female and were from the United States. Tweeters reported a range of arthritis conditions, including rheumatoid arthritis, systemic lupus erythematosus, and psoriatic arthritis. Seven themes were identified: health care experiences, personal stories, links to relevant blogs, discussion of arthritis-related symptoms, advice sharing, messages of positivity, and stay-at-home messaging. Sentiment analysis demonstrated marked anxiety around medication shortages, increased physical symptom burden, and strong desire for trustworthy information and emotional connection.

CONCLUSIONS

Tweets by people with arthritis highlight the multitude of concurrent concerns during the COVID-19 pandemic. Understanding these concerns, which include heightened physical and psychological symptoms in the context of treatment misinformation, may assist clinicians to provide person-centered care during this time of great health uncertainty.

Predicting Health Disparities in Regions at Risk of Severe Illness to Inform Health Care Resource Allocation During Pandemics: Observational Study

Background

Pandemics including COVID-19 have disproportionately affected socioeconomically vulnerable populations.

Objective

Our objective was to create a repeatable modeling process to identify regional population centers with pandemic vulnerability.

Methods

Using readily available COVID-19 and socioeconomic variable data sets, we used stepwise linear regression techniques to build predictive models during the early days of the COVID-19 pandemic. The models were validated later in the pandemic timeline using actual COVID-19 mortality rates in high population density states. The mean sample size was 43 and ranged from 8 (Connecticut) to 82 (Michigan).

Results

The New York, New Jersey, Connecticut, Massachusetts, Louisiana, Michigan, and Pennsylvania models provided the strongest predictions of top counties in densely populated states with a high likelihood of disproportionate COVID-19 mortality rates. For all of these models, P values were less than .05.

Conclusions

The models have been shared with the Department of Health Commissioners of each of these states with strong model predictions as input into a much needed “pandemic playbook” for local health care agencies in allocating medical testing and treatment resources. We have also confirmed the utility of our models with pharmaceutical companies for use in decisions pertaining to vaccine trial and distribution locations.

[Medical applications of social networks. Specific aspects of the COVID-19 pandemic]

For years, social networks have been incorporated into the day-to-day of the majority of the population. In this context, a new area of knowledge in medicine has been developed: infodemiology. It is defined as the evaluation, with the objective of improving public health, of health-related information that users upload to the network. In addition, social networks offer many possibilities for conducting public health campaigns, accessing patients, or carrying out treatment interventions.

Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media

BACKGROUND

The COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a "mental health tsunami", the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances.

OBJECTIVE

Our study aims to provide insights regarding people's psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context.

METHODS

We obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people's social media self-disclosure. Using these data sets, we studied people's self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets.

RESULTS

We found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis-mental health symptomatic expressions have increased by about 14%, and support expressions have increased by about 5%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis.

CONCLUSIONS

We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people's mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their "new normal." Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis.

Discovering the Context of People With Disabilities: Semantic Categorization Test and Environmental Factors Mapping of Word Embeddings from Reddit

BACKGROUND

The World Health Organization's International Classification of Functioning Disability and Health (ICF) conceptualizes disability not solely as a problem that resides in the individual, but as a health experience that occurs in a context. Word embeddings build on the idea that words that occur in similar contexts tend to have similar meanings. In spite of both sharing "context" as a key component, word embeddings have been scarcely applied in disability. In this work, we propose social media (particularly, Reddit) to link them.

OBJECTIVE

The objective of our study is to train a model for generating word associations using a small dataset (a subreddit on disability) able to retrieve meaningful content. This content will be formally validated and applied to the discovery of related terms in the corpus of the disability subreddit that represent the physical, social, and attitudinal environment (as defined by a formal framework like the ICF) of people with disabilities.

METHODS

Reddit data were collected from pushshift.io with the pushshiftr R package as a wrapper. A word2vec model was trained with the wordVectors R package using the disability subreddit comments, and a preliminary validation was performed using a subset of Mikolov analogies. We used Van Overschelde's updated and expanded version of the Battig and Montague norms to perform a semantic categories test. Silhouette coefficients were calculated using cosine distance from the wordVectors R package. For each of the 5 ICF environmental factors (EF), we selected representative subcategories addressing different aspects of daily living (ADLs); then, for each subcategory, we identified specific terms extracted from their formal ICF definition and ran the word2vec model to generate their nearest semantic terms, validating the obtained nearest semantic terms using public evidence. Finally, we applied the model to a specific subcategory of an EF involved in a relevant use case in the field of rehabilitation.

RESULTS

We analyzed 96,314 comments posted between February 2009 and December 2019, by 10,411 Redditors. We trained word2vec and identified more than 30 analogies (eg, breakfast - 8 am + 8 pm = dinner). The semantic categorization test showed promising results over 60 categories; for example, s(A relative)=0.562, s(A sport)=0.475 provided remarkable explanations for low s values. We mapped the representative subcategories of all EF chapters and obtained the closest terms for each, which we confirmed with publications. This allowed immediate access (≤ 2 seconds) to the terms related to ADLs, ranging from apps "to know accessibility before you go" to adapted sports (boccia). For example, for the support and relationships EF subcategory, the closest term discovered by our model was "resilience," recently regarded as a key feature of rehabilitation, not yet having one unified definition. Our model discovered 10 closest terms, which we validated with publications, contributing to the "resilience" definition.

CONCLUSIONS

This study opens up interesting opportunities for the exploration and discovery of the use of a word2vec model that has been trained with a small disability dataset, leading to immediate, accurate, and often unknown (for authors, in many cases) terms related to ADLs within the ICF framework.

Using Social Media to Understand Online Social Factors Concerning Obesity: A Systematic Review (Preprint)

Background

Evidence in the literature surrounding obesity suggests that social factors play a substantial role in the spread of obesity. Although social ties with a friend who is obese increase the probability of becoming obese, the role of social media in this dynamic remains underexplored in obesity research. Given the rapid proliferation of social media in recent years, individuals socialize through social media and share their health-related daily routines, including dieting and exercising. Thus, it is timely and imperative to review previous studies focused on social factors in social media and obesity.

Objective

This study aims to examine web-based social factors in relation to obesity research.

Methods

We conducted a systematic review. We searched PubMed, Association for Computing Machinery, and ScienceDirect for articles published by July 5, 2019. Web-based social factors that are related to obesity behaviors were studied and analyzed.

Results

In total, 1608 studies were identified from the selected databases. Of these 1608 studies, 50 (3.11%) studies met the eligibility criteria. In total, 10 types of web-based social factors were identified, and a socioecological model was adopted to explain their potential impact on an individual from varying levels of web-based social structure to social media users’ connection to the real world.

Conclusions

We found 4 levels of interaction in social media. Gender was the only factor found at the individual level, and it affects user’s web-based obesity-related behaviors. Social support was the predominant factor identified, which benefits users in their weight loss journey at the interpersonal level. Some factors, such as stigma were also found to be associated with a healthy web-based social environment. Understanding the effectiveness of these factors is essential to help users create and maintain a healthy lifestyle.

COVID-19 and the Gendered Use of Emojis on Twitter: Infodemiology Study

Background

The online discussion around the COVID-19 pandemic is multifaceted, and it is important to examine the different ways by which online users express themselves. Since emojis are used as effective vehicles to convey ideas and sentiments, they can offer important insight into the public’s gendered discourses about the pandemic.

Objective

This study aims at exploring how people of different genders (eg, men, women, and sex and gender minorities) are discussed in relation to COVID-19 through the study of Twitter emojis.

Methods

We collected over 50 million tweets referencing the hashtags #Covid-19 and #Covid19 for a period of more than 2 months in early 2020. Using a mixed method, we extracted three data sets containing tweets that reference men, women, and sexual and gender minorities, and we then analyzed emoji use along each gender category. We identified five major themes in our analysis including morbidity fears, health concerns, employment and financial issues, praise for frontline workers, and unique gendered emoji use. The top 600 emojis were manually classified based on their sentiment, indicating how positive, negative, or neutral each emoji is and studying their use frequencies.

Results

The findings indicate that the majority of emojis are overwhelmingly positive in nature along the different genders, but sexual and gender minorities, and to a lesser extent women, are discussed more negatively than men. There were also many differences alongside discourses of men, women, and gender minorities when certain topics were discussed, such as death, financial and employment matters, gratitude, and health care, and several unique gendered emojis were used to express specific issues like community support.

Conclusions

Emoji research can shed light on the gendered impacts of COVID-19, offering researchers an important source of information on health crises as they happen in real time.

Public Reactions to the New York State Policy on Flavored E-Cigarettes on Twitter: Observational Study (Preprint)

Background

Objective

Methods

Results

Conclusions

Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study

BACKGROUND

Infodemiology is an emerging field of research that utilizes user-generated health-related content, such as that found in social media, to help improve public health. Twitter has become an important venue for studying emerging patterns in health issues such as substance use because it can reflect trends in real-time and display messages generated directly by users, giving a uniquely personal voice to analyses. Over the past year, several states in the United States have passed legislation to legalize adult recreational use of cannabis and the federal government in Canada has done the same. There are few studies that examine the sentiment and content of tweets about cannabis since the recent legislative changes regarding cannabis have occurred in North America.

OBJECTIVE

To examine differences in the sentiment and content of cannabis-related tweets by state cannabis laws, and to examine differences in sentiment between the United States and Canada between 2017 and 2019.

METHODS

In total, 1,200,127 cannabis-related tweets were collected from January 1, 2017, to June 17, 2019, using the Twitter application programming interface. Tweets then were grouped geographically based on cannabis legal status (legal for adult recreational use, legal for medical use, and no legal use) in the locations from which the tweets came. Sentiment scoring for the tweets was done with VADER (Valence Aware Dictionary and sEntiment Reasoner), and differences in sentiment for states with different cannabis laws were tested using Tukey adjusted two-sided pairwise comparisons. Topic analysis to determine the content of tweets was done using latent Dirichlet allocation in Python, using a Java implementation, LdaMallet, with Gensim wrapper.

RESULTS

Significant differences were seen in tweet sentiment between US states with different cannabis laws (P=.001 for negative sentiment tweets in fully illegal compared to legal for adult recreational use states), as well as between the United States and Canada (P=.003 for positive sentiment and P=.001 for negative sentiment). In both cases, restrictive state policy environments (eg, those where cannabis use is fully illegal, or legal for medical use only) were associated with more negative tweet sentiment than less restrictive policy environments (eg, where cannabis is legal for adult recreational use). Six key topics were found in recent US tweet contents: fun and recreation (keywords, eg, love, life, high); daily life (today, start, live); transactions (buy, sell, money); places of use (room, car, house); medical use and cannabis industry (business, industry, company); and legalization (legalize, police, tax). The keywords representing content of tweets also differed between the United States and Canada.

CONCLUSIONS

Knowledge about how cannabis is being discussed online, and geographic differences that exist in these conversations may help to inform public health planning and prevention efforts. Public health education about how to use cannabis in ways that promote safety and minimize harms may be especially important in places where cannabis is legal for adult recreational and medical use.

Changing Emotions About Fukushima Related to the Fukushima Nuclear Power Station Accident-How Rumors Determined People's Attitudes: Social Media Sentiment Analysis

Background

Public interest in radiation rose after the Tokyo Electric Power Company (TEPCO) Fukushima Daiichi Nuclear Power Station accident was caused by an earthquake off the Pacific coast of Tohoku on March 11, 2011. Various reports on the accident and radiation were spread by the mass media, and people displayed their emotional reactions, which were thought to be related to information about the Fukushima accident, on Twitter, Facebook, and other social networking sites. Fears about radiation were spread as well, leading to harmful rumors about Fukushima and the refusal to test children for radiation. It is believed that identifying the process by which people emotionally responded to this information, and hence became gripped by an increased aversion to Fukushima, might be useful in risk communication when similar disasters and accidents occur in the future. There are few studies surveying how people feel about radiation in Fukushima and other regions in an unbiased form.

Objective

The purpose of this study is to identify how the feelings of local residents toward radiation changed according to Twitter.

Methods

We used approximately 19 million tweets in Japanese containing the words “radiation” (放射線), “radioactivity” (放射能), and “radioactive substances” (放射性物質) that were posted to Twitter over a 1-year period following the Fukushima nuclear accident. We used regional identifiers contained in tweets (ie, nouns, proper nouns, place names, postal codes, and telephone numbers) to categorize them according to their prefecture, and then analyzed the feelings toward those prefectures from the semantic orientation of the words contained in individual tweets (ie, positive impressions or negative impressions).

Results

Tweets about radiation increased soon after the earthquake and then decreased, and feelings about radiation trended positively. We determined that, on average, tweets associating Fukushima Prefecture with radiation show more positive feelings than those about other prefectures, but have trended negatively over time. We also found that as other tweets have trended positively, only bots and retweets about Fukushima Prefecture have trended negatively.

Conclusions

The number of tweets about radiation has decreased overall, and feelings about radiation have trended positively. However, the fact that tweets about Fukushima Prefecture trended negatively, despite decreasing in percentage, suggests that negative feelings toward Fukushima Prefecture have become more extreme. We found that while the bots and retweets that were not about Fukushima Prefecture gradually trended toward positive feelings, the bots and retweets about Fukushima Prefecture trended toward negative feelings.