Increasing Interest of Mass Communication Media and the General Public in the Distribution of Tweets About Mental Disorders: Observational Study

Assessment of beliefs and attitudes towards benzodiazepines using machine learning based on social media posts: an observational study

BACKGROUND

Benzodiazepines are frequently prescribed drugs; however, their prolonged use can lead to tolerance, dependence, and other adverse effects. Despite these risks, long-term use remains common, presenting a public health concern. This study aims to explore the beliefs and opinions held by the public regarding benzodiazepines, as understanding these perspectives may provide insights into their usage patterns.

METHODS

We collected public tweets published in English between January 1, 2019, and October 31, 2020, that mentioned benzodiazepines. The content of each tweet and the characteristics of the users were analyzed using a mixed-method approach, including manual analysis and semi-supervised machine learning.

RESULTS

Over half of the Twitter users highlighted the efficacy of benzodiazepines, with minimal discussion of their side effects. The most active participants in these conversations were patients and their families, with health professionals and institutions being notably absent. Additionally, the drugs most frequently mentioned corresponded with those most commonly prescribed by healthcare professionals.

CONCLUSIONS

Social media platforms offer valuable insights into users' experiences and opinions regarding medications. Notably, the sentiment towards benzodiazepines is predominantly positive, with users viewing them as effective while rarely mentioning side effects. This analysis underscores the need to educate physicians, patients, and their families about the potential risks associated with benzodiazepine use and to promote clinical guidelines that support the proper management of these medications.

CLINICAL TRIAL NUMBER

Not applicable.

Understanding public perceptions and discussions on diseases involving chronic pain through social media: cross-sectional infodemiology study

BACKGROUND

Chronic pain is a highly prevalent medical condition that negatively impacts quality of life and is associated with considerable functional disability. Certain diseases, such as fibromyalgia, headache, paraplegia, neuropathy, and multiple sclerosis, manifest with chronic pain.

OBJECTIVE

The aim of this study is to examine the number and type of tweets (original or retweet) related to chronic pain, as well as to analyze the emotions and compare the societal impact of the diseases under study.

METHODS

We investigated tweets posted between January 1, 2018, and December 31, 2022, by Twitter users in English and Spanish, as well as the generated retweets. Additionally, emotions were extracted from these tweets and their diffusion was analyzed. Furthermore, the topics most frequently discussed by users were collected.

RESULTS

A total of 72,874 tweets were analyzed, including 44,467 in English and 28,407 in Spanish. Paraplegia represented 23.3% with 16,461 of the classified tweets, followed by headache and fibromyalgia with 15,337 (21.7%) and 15,179 (21.5%) tweets, respectively. Multiple sclerosis generated 14,781 tweets (21%), and the fewest tweets were related to neuropathy with 8,830 tweets (12.5%). The results showed that the primary emotions extracted were "fear" and "sadness." Additionally, the reach and impact of these tweets were investigated through the generated retweets, with those related to headaches showing the highest interest and interaction among users.

CONCLUSION

Our results underscore the potential of leveraging social media for a better understanding of patients suffering from chronic pain and its impact on society. Among the most frequently encountered topics are those related to treatment, symptoms, or causes of the disease. Therefore, it is relevant to inform the patient to prevent misconceptions regarding their illness.

Twitter Discussions on #digitaldementia: Content and Sentiment Analysis

BACKGROUND

Digital dementia is a term that describes a possible decline in cognitive abilities, especially memory, attributed to the excessive use of digital technology such as smartphones, computers, and tablets. This concept has gained popularity in public discourse and media lately. With the increasing use of social media platforms such as Twitter (subsequently rebranded as X), discussions about digital dementia have become more widespread, which offer a rich source of information to understand public perceptions, concerns, and sentiments regarding this phenomenon.

OBJECTIVE

The aim of this research was to delve into a comprehensive content and sentiment analysis of Twitter discussions regarding digital dementia using the hashtag #digitaldementia.

METHODS

Retrospectively, publicly available English-language tweets with hashtag combinations related to the topic of digital dementia were extracted from Twitter. The tweets were collected over a period of 15 years, from January 1, 2008, to December 31, 2022. Content analysis was used to identify major themes within the tweets, and sentiment analysis was conducted to understand the positive and negative emotions associated with these themes in order to gain a better understanding of the issues surrounding digital dementia. A one-way ANOVA was performed to gather detailed statistical insights regarding the selected tweets from influencers within each theme.

RESULTS

This study was conducted on 26,290 tweets over 15 years by 5123 Twitter users, mostly female users in the United States. The influencers had followers ranging from 20,000 to 1,195,000 and an average of 214,878 subscribers. The study identified four themes regarding digital dementia after analyzing tweet content: (1) cognitive decline, (2) digital dependency, (3) technology overload, and (4) coping strategies. Categorized according to Glaser and Strauss's classifications, most tweets (14,492/26,290, 55.12%) fell under the categories of wretched (purely negative) or bad (mostly negative). However, only a small proportion of tweets (3122/26,290, 11.86%) were classified as great (purely positive) or swell sentiment (mostly positive). The ANOVA results showed significant differences in mean sentiment scores among the themes (F3,3581=29.03; P<.001). The mean sentiment score was -0.1072 (SD 0.4276).

CONCLUSIONS

Various negative tweets have raised concerns about the link between excessive use of digital devices and cognitive decline, often known as digital dementia. Of particular concern is the rapid increase in digital device use. However, some positive tweets have suggested coping strategies. Engaging in digital detox activities, such as increasing physical exercise and participating in yoga and meditation, could potentially help prevent cognitive decline.

Regional insights on tobacco-related tweets: unveiling user opinions and usage patterns

Introduction

Tobacco consumption and its impact on health remain high worldwide. Additionally, it is a contentious issue generating significant controversy. Twitter has proven to be a useful platform for evaluating public health topics related to population health behaviors, and tobacco consumption.

Objective

The objective of this study is to analyze the content of tweets related to tobacco. Moreover, geolocation data will be considered to understand regional differences.

Methods

Tweets published between 2018 and 2022, in both English and Spanish, containing the keyword "tobacco," were analyzed. A total of 56,926 tweets were obtained. The tweets were classified into different categories. 550 tweets were manually analyzed, and an automated and computerized classification was performed for the remaining and largest subset of tweets.

Results

The analysis yielded 30,812 classifiable tweets. Healthcare professionals were the most frequent contributors to the topic (50.2%), with the most common theme being general information about the toxic effects of tobacco. 57.9% of the tweets discussed the harmful effects of tobacco on health, with fear being the predominant emotion. The largest number of tweets were located in America.

Conclusions

Our study revealed a substantial number of tweets highlighting the health risks and negative perceptions of tobacco consumption. Africa showed the lowest percentage of tweets discussing the health risks associated with tobacco, coinciding with the continent having the least developed anti-tobacco policies. Healthcare professionals emerged as the most prominent users discussing the topic, which is encouraging as they play a crucial role in disseminating accurate and scientific health information.

Hype or hope? Ketamine for the treatment of depression: results from the application of deep learning to Twitter posts from 2010 to 2023

Objective

To investigate societal perceptions of ketamine's use in depression therapy by analysing Twitter posts from January 1, 2010 to April 1, 2023.

Methods

Using Twitter as the social media platform of choice, and employing search terms based on (depression OR depressed OR depressive) AND (ketamine OR esketamine OR Spravato), we collected English-language tweets from January 1, 2010, to April 1, 2023. Using unsupervised machine learning and natural language processing (NLP) techniques, including Bidirectional Encoder Representations from Transformers (BERT) and BERTopic, the study identified prevalent topics surrounding public chatter around the use of ketamine in depression treatment. Manual thematic analyses further refined these topics into themes.

Results

Out of an initial dataset of 99,405 tweets, after removing duplicate tweets, re-tweets and tweets posted by organizations over Twitter, 18,899 unique tweets from presumably individual users were analysed. Analysis of temporal trends revealed a shift in public attitudes, particularly after the United States Food and Drug Administration (FDA)'s 2019 approval of ketamine for depression. Three major themes emerged: a changing regulatory landscape, cautious optimism, and personal experiences with the drug. There was an initial spike in discussions post-FDA approval in 2019. Thereafter, cautious optimism (Theme 2) decreased among the general public, with more personal accounts (Theme 3) highlighting the potential benefits for some treatment-resistant patients. Limitations of the study include Twitter's inherent biases towards younger, English-speaking demographics.

Conclusion

In summary, the public's multifaceted perception leans towards a hopeful stance on ketamine's therapeutic potential for depression.

Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine

Introduction

Cocaine abuse represents a major public health concern. The social perception of cocaine has been changing over the decades, a phenomenon closely tied to its patterns of use and abuse. Twitter is a valuable tool to understand the status of drug use and abuse globally. However, no specific studies discussing cocaine have been conducted on this platform.

Methods

111,508 English and Spanish tweets containing "cocaine" from 2018 to 2022 were analyzed. 550 were manually studied, and the largest subset underwent automated classification. Then, tweets related to cocaine were analyzed to examine their content, types of Twitter users, usage patterns, health effects, and personal experiences. Geolocation data was also considered to understand regional differences.

Results

A total of 71,844 classifiable tweets were obtained. Among these, 15.95% of users discussed the harm of cocaine consumption to health. Media outlets had the highest number of tweets (35.11%) and the most frequent theme was social/political denunciation (67.88%). Regarding the experience related to consumption, there are more tweets with a negative sentiment. The 9.03% of tweets explicitly mention frequent use of the drug. The continent with the highest number of tweets was America (55.44% of the total).

Discussion

The findings underscore the significance of cocaine as a current social and political issue, with a predominant focus on political and social denunciation in the majority of tweets. Notably, the study reveals a concentration of tweets from the United States and South American countries, reflecting the high prevalence of cocaine-related disorders and overdose cases in these regions. Alarmingly, the study highlights the trivialization of cocaine consumption on Twitter, accompanied by a misleading promotion of its health benefits, emphasizing the urgent need for targeted interventions and antidrug content on social media platforms. Finally, the unexpected advocacy for cocaine by healthcare professionals raises concerns about potential drug abuse within this demographic, warranting further investigation.

Analysis of the hikikomori phenomenon - an international infodemiology study of Twitter data in Portuguese

BACKGROUND

Hikikomori refers to the extreme isolation of individuals in their own homes, lasting at least six months. In recent years social isolation has become an important clinical, social, and public health problem, with increased awareness of hikikomori around the globe. Portuguese is one of the six most spoken languages in the world, but no studies have analysed the content regarding this phenomenon expressed in Portuguese.

OBJECTIVE

To explore the hikikomori phenomenon on Twitter in Portuguese, utilising a mixed-methods approach encompassing content analysis, emotional analysis, and correlation analysis.

METHODS

A mixed methods analysis of all publicly available tweets in the Portuguese language using a specific keyword (hikikomori) between 1st January 2008 and 19th October 2022. The content analysis involved categorising tweets based on tone, content, and user types, while correlation analysis was used to investigate user engagement and geographical distribution. Statistical analysis and artificial intelligence were employed to classify and interpret the tweet data.

RESULTS

Among the total of 13,915 tweets generated, in terms of tone 10,731 were classified as "negative", and 3184 as "positive". Regarding content, "curiosities" was the most posted, as well as the most retweeted and liked topic. Worldwide, most of the hikikomori related tweets in Portuguese were posted in Europe, while "individuals with hikikomori" were the users most active posting. Regarding emotion analysis, the majority of tweets were "neutral".

CONCLUSIONS

These findings show the global prevalence of the discourse on hikikomori phenomenon among Portuguese speakers. It also indicates an increase in the number of tweets on this topic in certain continents over the years. These findings can contribute to developing specific interventions, support networks, and awareness-raising campaigns for affected individuals.

Privacy Concerns About Sharing General and Specific Health Information on Twitter: Quantitative Study

BACKGROUND

Twitter is a common platform for people to share opinions, discuss health-related topics, and engage in conversations with a wide audience. Twitter users frequently share health information related to chronic diseases, mental health, and general wellness topics. However, sharing health information on Twitter raises privacy concerns as it involves sharing personal and sensitive data on a web-based platform.

OBJECTIVE

This study aims to adopt an interactive approach and develop a model consisting of privacy concerns related to web-based vendors and web-based peers. The research model integrates the 4 dimensions of concern for information privacy that express concerns related to the practices of companies and the 4 dimensions of peer privacy concern that reflect concerns related to web-based interactions with peers. This study examined how this interaction may affect individuals' information-sharing behavior on Twitter.

METHODS

Data were collected from 329 Twitter users in the United States using a web-based survey.

RESULTS

Results suggest that privacy concerns related to company practices might not significantly influence the sharing of general health information, such as details about hospitals and medications. However, privacy concerns related to companies and third parties can negatively shape the disclosure of specific health information, such as personal medical issues (β=-.43; P<.001). Findings show that peer-related privacy concerns significantly predict sharing patterns associated with general (β=-.38; P<.001) and specific health information (β=-.72; P<.001). In addition, results suggest that people may disclose more general health information than specific health information owing to peer-related privacy concerns (t165=4.72; P<.001). The model explains 41% of the variance in general health information disclosure and 67% in specific health information sharing on Twitter.

CONCLUSIONS

The results can contribute to privacy research and propose some practical implications. The findings provide insights for developers, policy makers, and health communication professionals about mitigating privacy concerns in web-based health information sharing. It particularly underlines the importance of addressing peer-related privacy concerns. The study underscores the need to build a secure and trustworthy web-based environment, emphasizing the significance of peer interactions and highlighting the need for improved regulations, clear data handling policies, and users' control over their own data.

Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study

BACKGROUND

Opioids are used for the treatment of refractory pain, but their inappropriate use has detrimental consequences for health. Understanding the current experiences and perceptions of patients in a spontaneous and colloquial environment regarding the key drugs involved in the opioid crisis is of utmost significance.

OBJECTIVE

The study aims to analyze Twitter content related to opioids, with objectives including characterizing users participating in these conversations, identifying prevalent topics and gauging public perception, assessing opinions on drug efficacy and tolerability, and detecting discussions related to drug dispensing, prescription, or acquisition.

METHODS

In this cross-sectional study, we gathered public tweets concerning major opioids posted in English or Spanish between January 1, 2019, and December 31, 2020. A total of 256,218 tweets were collected. Approximately 27% (69,222/256,218) were excluded. Subsequently, 7000 tweets were subjected to manual analysis based on a codebook developed by the researchers. The remaining databases underwent analysis using machine learning classifiers. In the codebook, the type of user was the initial classification domain. We differentiated between patients, family members and friends, health care professionals, and institutions. Next, a distinction was made between medical and nonmedical content. If it was medical in nature, we classified it according to whether it referred to the drug's efficacy or adverse effects. In nonmedical content tweets, we analyzed whether the content referred to management issues (eg, pharmacy dispensation, medical appointment prescriptions, commercial advertisements, or legal aspects) or the trivialization of the drug.

RESULTS

Among the entire array of scrutinized pharmaceuticals, fentanyl emerged as the predominant subject, featuring in 27% (39,997/148,335 posts) of the tweets. Concerning user categorization, roughly 70% (101,259/148,335) were classified as patients. Nevertheless, tweets posted by health care professionals obtained the highest number of retweets (37/16,956, 0.2% of their posts received over 100 retweets). We found statistically significant differences in the distribution concerning efficacy and side effects among distinct drug categories (P<.001). Nearly 60% (84,401/148,335) of the posts were devoted to nonmedical subjects. Within this category, legal facets and recreational use surfaced as the most prevalent themes, while in the medical discourse, efficacy constituted the most frequent topic, with over 90% (45,621/48,777) of instances characterizing it as poor or null. The opioid with the greatest proportion of tweets concerning legal considerations was fentanyl. Furthermore, fentanyl was the drug most frequently offered for sale on Twitter, while methadone generated the most tweets about pharmacy delivery.

CONCLUSIONS

The opioid crisis is present on social media, where tweets discuss legal and recreational use. Opioid users are the most active participants, prioritizing medication efficacy over side effects. Surprisingly, health care professionals generate the most engagement, indicating their positive reception. Authorities must monitor web-based opioid discussions to detect illicit acquisitions and recreational use.

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.

Googling "mental health" after mental health legislation and during the COVID-19 pandemic: an infodemiological study of public interest in mental health in the Philippines

BACKGROUND

Public interest in mental health in the Philippines can change after the Mental Health (MH) act approval and during the COVID-19 pandemic. To determine these changes, online searches in Google can be analyzed as an alternative measure.

AIMS

To determine the trends of public interest in mental health before and after the MH act approval and during the COVID-19 pandemic.

METHOD

Aggregated search volumes for mental health using the keyword, "Mental Health (Topic)" were gathered from Google Trends. Search volume indices (SVIs), and related queries were analyzed. SVIs from the different periods were compared using means, t test, ANOVA, and ARIMA.

RESULTS

Public interest in mental health increased after the MH Act approval and during the COVID-19 pandemic. The top and rising related queries revolved around mental health definitions, translations, factors, services, policy, adverse mental state, and its context during the pandemic.

CONCLUSIONS

These increased public interests may stem from aspirations for mental health after legislation and higher mental health needs during the pandemic. Related queries may indicate that the public may be using online information to search for mental health services and concepts. Thus, there is a need to improve reliable and trustworthy online mental health information and services.

Areas of Interest and Social Consideration of Antidepressants on English Tweets: A Natural Language Processing Classification Study

Background: Antidepressants are the foundation of the treatment of major depressive disorders. Despite the scientific evidence, there is still a sustained debate and concern about the efficacy of antidepressants, with widely differing opinions among the population about their positive and negative effects, which may condition people’s attitudes towards such treatments. Our aim is to investigate Twitter posts about antidepressants in order to have a better understanding of the social consideration of antidepressants. Methods: We gathered public tweets mentioning antidepressants written in English, published throughout a 22-month period, between 1 January 2019 and 31 October 2020. We analysed the content of each tweet, determining in the first place whether they included medical aspects or not. Those with medical content were classified into four categories: general aspects, such as quality of life or mood, sleep-related conditions, appetite/weight issues and aspects around somatic alterations. In non-medical tweets, we distinguished three categories: commercial nature (including all economic activity, drug promotion, education or outreach), help request/offer, and drug trivialization. In addition, users were arranged into three categories according to their nature: patients and relatives, caregivers, and interactions between Twitter users. Finally, we identified the most mentioned antidepressants, including the number of retweets and likes, which allowed us to measure the impact among Twitter users. Results: The activity in Twitter concerning antidepressants is mainly focused on the effects these drugs may have on certain health-related areas, specifically sleep (20.87%) and appetite/weight (8.95%). Patients and relatives are the type of user that most frequently posts tweets with medical content (65.2%, specifically 80% when referencing sleep and 78.6% in the case of appetite/weight), whereas they are responsible for only 2.9% of tweets with non-medical content. Among tweets classified as non-medical in this study, the most common subject was drug trivialization (66.86%). Caregivers barely have any presence in conversations in Twitter about antidepressants (3.5%). However, their tweets rose more interest among other users, with a ratio 11.93 times higher than those posted by patients and their friends and family. Mirtazapine is the most mentioned antidepressant in Twitter (45.43%), with a significant difference with the rest, agomelatine (11.11%). Conclusions: This study shows that Twitter users that take antidepressants, or their friends and family, use social media to share medical information about antidepressants. However, other users that do not talk about antidepressants from a personal or close experience, frequently do so in a stigmatizing manner, by trivializing them. Our study also brings to light the scarce presence of caregivers in Twitter.

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.

Examining Tweet Content and Engagement of Users With Tweets About Hikikomori in Japanese: Mixed Methods Study of Social Withdrawal

BACKGROUND

Hikikomori is a form of severe social withdrawal that is particularly prevalent in Japan. Social media posts offer insight into public perceptions of mental health conditions and may also inform strategies to identify, engage, and support hard-to-reach patient populations such as individuals affected by hikikomori.

OBJECTIVE

In this study, we seek to identify the types of content on Twitter related to hikikomori in the Japanese language and to assess Twitter users' engagement with that content.

METHODS

We conducted a mixed methods analysis of a random sample of 4940 Japanese tweets from February to August 2018 using a hashtag (#hikikomori). Qualitative content analysis included examination of the text of each tweet, development of a codebook, and categorization of tweets into relevant codes. For quantitative analysis (n=4859 tweets), we used bivariate and multivariate logistic regression models, adjusted for multiple comparisons, and estimated the predicted probabilities of tweets receiving engagement (likes or retweets).

RESULTS

Our content analysis identified 9 codes relevant to tweets about hikikomori: personal anecdotes, social support, marketing, advice, stigma, educational opportunities, refuge (ibasho), employment opportunities, and medicine and science. Tweets about personal anecdotes were the most common (present in 2747/4859, 56.53% of the tweets), followed by social support (902/4859, 18.56%) and marketing (624/4859, 12.84%). In the adjusted models, tweets coded as stigma had a lower predicted probability of likes (-33 percentage points, 95% CI -42 to -23 percentage points; P<.001) and retweets (-11 percentage points, 95% CI -18 to -4 percentage points; P<.001), personal anecdotes had a lower predicted probability of retweets (-8 percentage points, 95% CI -14 to -3 percentage points; P=.002), marketing had a lower predicted probability of likes (-13 percentage points, 95% CI -21 to -6 percentage points; P<.001), and social support had a higher predicted probability of retweets (+15 percentage points, 95% CI 6-24 percentage points; P=.001), compared with all tweets without each of these codes.

CONCLUSIONS

Japanese tweets about hikikomori reflect a unique array of topics, many of which have not been identified in prior research and vary in their likelihood of receiving engagement. Tweets often contain personal stories of hikikomori, suggesting the potential to identify individuals with hikikomori through Twitter.

Analyzing Psychotherapy on Twitter: An 11-Year Analysis of Tweets From Major U.S. Media Outlets

BACKGROUND

The Internet has become the main source of information on health issues, and information now determines the therapeutic preferences of patients. For this reason, it is relevant to analyze online information discussing psychotherapy.

OBJECTIVE

To investigate tweets posted by 25 major US media outlets between 2009 and 2019 concerning psychotherapy.

METHODS

We investigated tweets posted by 25 major US media outlets about psychotherapy between January 2009 and December 2019 as well as the likes generated. In addition, we measured the sentiment analysis of these tweets.

RESULTS

Most of the tweets analyzed focused on Mindfulness (5,498), while a low number were related to Psychoanalysis (376) and even less to Cognitive-Behavioral Therapy (61). Surprisingly, Computer-supported therapy, Psychodynamic therapy, Systemic therapy, Acceptance and commitment therapy, and Dialectical behavior therapy did not generate any tweet. In terms of content, efficacy was the main focus of the posted tweets, receiving Cognitive-Behavioral Therapy and Mindfulness a positive appraisal.

CONCLUSIONS

US media outlets focused their interest on Mindfulness which may have contributed to the growing popularity in the past years of this therapeutic modality.

Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter

BACKGROUND

Antiobesity drugs are prescribed for the treatment of obesity in conjunction with healthy eating, physical activity, and behavior modification. However, poor adherence rates have been reported. Attitudes or beliefs toward medications are important to ascertain because they may be associated with patient behavior. The analysis of tweets has become a tool for health research.

OBJECTIVE

The aim of this study is to investigate the content and key metrics of tweets referring to antiobesity drugs.

METHODS

In this observational quantitative and qualitative study, we focused on tweets containing hashtags related to antiobesity drugs between September 20, 2019, and October 31, 2019. Tweets were first classified according to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. We additionally rated it as positive or negative. Furthermore, we classified any links included within a tweet as either scientific or nonscientific. Finally, the number of retweets generated as well as the dissemination and sentiment score obtained by the antiobesity drugs analyzed were also measured.

RESULTS

We analyzed a total of 2045 tweets, 945 of which were excluded according to the criteria of the study. Finally, 320 out of the 1,100 remaining tweets were also excluded because their content, although related to drugs for obesity treatment, did not address the efficacy, side effects, or adherence to medication. Liraglutide and semaglutide accumulated the majority of tweets (682/780, 87.4%). Notably, the content that generated the highest frequency of tweets was related to treatment efficacy, with liraglutide-, semaglutide-, and lorcaserin-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to liraglutide and semaglutide. Semaglutide-related tweets obtained the highest probability of likes and were the most disseminated within the Twitter community.

CONCLUSIONS

This analysis of posted tweets related to antiobesity drugs shows that the interest, beliefs, and experiences regarding these pharmacological treatments are heterogeneous. The efficacy of the treatment accounts for the majority of interest among Twitter users.

Analysis of Tweets Containing Information Related to Rheumatological Diseases on Twitter

BACKGROUND

Tweets often indicate the interests of Twitter users. Data from Twitter could be used to better understand the interest in and perceptions of a variety of diseases and medical conditions, including rheumatological diseases which have increased in prevalence over the past several decades. The aim of this study was to perform a content analysis of tweets referring to rheumatological diseases.

METHODS

The content of each tweet was rated as medical (including a reference to diagnosis, treatment, or other aspects of the disease) or non-medical (such as requesting help). The type of user and the suitability of the medical content (appropriate content or, on the contrary, fake content if it was medically inappropriate according to the current medical knowledge) were also evaluated. The number of retweets and likes generated were also investigated.

RESULTS

We analyzed a total of 1514 tweets: 1093 classified as medical and 421 as non-medical. The diseases with more tweets were the most prevalent. Within the medical tweets, the content of these varied according to the disease (some more focused on diagnosis and others on treatment). The fake content came from unidentified users and mostly referred to the treatment of diseases.

CONCLUSIONS

According to our results, the analysis of content posted on Twitter in regard to rheumatological diseases may be useful for investigating the public's prevailing areas of interest, concerns and opinions. Thus, it could facilitate communication between health care professionals and patients, and ultimately improve the doctor-patient relationship. Due to the interest shown in medical issues it seems desirable to have healthcare institutions and healthcare workers involved in Twitter.

Areas of Interest and Attitudes towards the Pharmacological Treatment of Attention Deficit Hyperactivity Disorder: Thematic and Quantitative Analysis Using Twitter

We focused on tweets containing hashtags related to ADHD pharmacotherapy between 20 September and 31 October 2019. Tweets were classified as to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. Furthermore, we classified any links included within a tweet as either scientific or non-scientific. We created a dataset of 6568 tweets: 4949 (75.4%) related to stimulants, 605 (9.2%) to non-stimulants and 1014 (15.4%) to alpha-2 agonists. Next, we manually analyzed 1810 tweets. In the end, 481 (48%) of the tweets in the stimulant group, 218 (71.9%) in the non-stimulant group and 162 (31.9%) in the alpha agonist group were considered classifiable. Stimulants accumulated the majority of tweets. Notably, the content that generated the highest frequency of tweets was that related to treatment efficacy, with alpha-2 agonist-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to alpha-2 agonists. Stimulant-related tweets obtained the highest proportion of likes and were the most disseminated within the Twitter community. Understanding the public view of these medications is necessary to design promotional strategies aimed at the appropriate population.

Analysis of Media Outlets on Women's Health: Thematic and Quantitative Analyses Using Twitter

Background: Media outlets influence social attitudes toward health habits. The analysis of tweets has become a tool for health researchers.

Objective: The objective of this study was to investigate the distribution of tweets about women's health and the interest generated among Twitter users.

Methods: We investigated tweets posted by 25 major U.S. media outlets about pre-menopausal and post-menopausal women's health between January 2009 and December 2019 as well as the retweets generated. In addition, we measured the sentiment analysis of these tweets as well as their potential dissemination.

Results: A total of 376 tweets were analyzed. Pre-menopausal women's health accounted for most of the tweets (75.3%). Contraception was the main focus of the tweets, while a very limited number were related to infertility (1.4%). With regard to medical content, the effectiveness of contraceptive methods was the most frequent topic (46.2%). However, tweets related to side effects achieved the highest retweet-to-tweet ratio (70.3). The analysis of sentiments showed negative perceptions on tubal ligation.

Conclusions: The U.S. media outlets analyzed are more interested in pre-menopausal than in post-menopausal women health and focused their content on contraception, while Twitter users showed greater interest in side effects.

Assessing Suicide Reporting in Top Newspaper Social Media Accounts in China: Content Analysis Study

BACKGROUND

Previous studies have shown that suicide reporting in mainstream media has a significant impact on suicidal behaviors (eg, irresponsible suicide reporting can trigger imitative suicide). Traditional mainstream media are increasingly using social media platforms to disseminate information on public-related topics, including health. However, there is little empirical research on how mainstream media portrays suicide on social media platforms and the quality of their coverage.

OBJECTIVE

This study aims to explore the characteristics and quality of suicide reporting by mainstream publishers via social media in China.

METHODS

Via the application programming interface of the social media accounts of the top 10 Chinese mainstream publishers (eg, People's Daily and Beijing News), we obtained 2366 social media posts reporting suicide. This study conducted content analysis to demonstrate the characteristics and quality of the suicide reporting. According to the World Health Organization (WHO) guidelines, we assessed the quality of suicide reporting by indicators of harmful information and helpful information.

RESULTS

Chinese mainstream publishers most frequently reported on suicides stated to be associated with conflict on their social media (eg, 24.47% [446/1823] of family conflicts and 16.18% [295/1823] of emotional frustration). Compared with the suicides of youth (730/1446, 50.48%) and urban populations (1454/1588, 91.56%), social media underreported suicides in older adults (118/1446, 8.16%) and rural residents (134/1588, 8.44%). Harmful reporting practices were common (eg, 54.61% [1292/2366] of the reports contained suicide-related words in the headline and 49.54% [1172/2366] disclosed images of people who died by suicide). Helpful reporting practices were very limited (eg, 0.08% [2/2366] of reports provided direct information about support programs).

CONCLUSIONS

The suicide reporting of mainstream publishers on social media in China broadly had low adherence to the WHO guidelines. Considering the tremendous information dissemination power of social media platforms, we suggest developing national suicide reporting guidelines that apply to social media. By effectively playing their separate roles, we believe that social media practitioners, health institutions, social organizations, and the general public can endeavor to promote responsible suicide reporting in the Chinese social media environment.

Assessment of Antipsychotic Medications on Social Media: Machine Learning Study

Background: Antipsychotic medications are the first-line treatment for schizophrenia. However, non-adherence is frequent despite its negative impact on the course of the illness. In response, we aimed to investigate social media posts about antipsychotics to better understand the online environment in this regard. Methods: We collected tweets containing mentions of antipsychotic medications posted between January 1st 2019 and October 31st 2020. The content of each tweet and the characteristics of the users were analyzed as well as the number of retweets and likes generated. Results: Twitter users, especially those identified as patients, showed an interest in antipsychotic medications, mainly focusing on the topics of sexual dysfunction and sedation. Interestingly, paliperidone, despite being among one of the newest antipsychotics, accounted for a low number of tweets and did not generate much interest. Conversely, retweet and like ratios were higher in those tweets asking for or offering help, in those posted by institutions and in those mentioning cognitive complaints. Moreover, health professionals did not have a strong presence in tweet postings, nor did medical institutions. Finally, trivialization was frequently observed. Conclusion: This analysis of tweets about antipsychotic medications provides insights into experiences and opinions related to this treatment. Twitter user perspectives therefore constitute a valuable input that may help to improve clinicians' knowledge of antipsychotic medications and their communication with patients regarding this treatment.

What Motivates Internet Users to Search for Asperger Syndrome and Autism on Google?

Social campaigns are carried out to promote autism spectrum disorder (ASD) awareness, normalization, and visibility. The internet helps to shape perceptions of Asperger syndrome and autism. In fact, these campaigns often coincide with the increase in searches for both diagnoses on Google. We have two study objectives: to use Google Trends to identify the annual time points from 2015 to 2019 with the highest Google search traffic in Spain for the terms "autism" and "Asperger", and to identify news and trending topics related to ASD that took place during the weeks with the highest number of Google searches for these terms. Google Trend, MyNews and Trendinalia were used to analyze the volume of searches and trending topics related to ASD. As a result, social marketing campaigns, social networks and the publication of news items act as powerful voices that can provide a realistic or sensationalist picture of the disorder. For this reason, we concluded that campaigns play an important role in the normalization of ASD, and that it is important for organizations concerned with the visibility and social inclusion of people with ASD to check the way ASD is portrayed through the internet, media, and social networks.

[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.

Eating Disorder Awareness Campaigns: Thematic and Quantitative Analysis Using Twitter

Background

Health awareness initiatives are frequent but their efficacy is a matter of controversy. We have investigated the effect of the Eating Disorder Awareness Week and Wake Up Weight Watchers campaigns on Twitter.

Objective

We aimed to examine whether the Eating Disorder Awareness Week and Wake Up Weight Watchers initiatives increased the volume and dissemination of Twitter conversations related to eating disorders and investigate what content generates the most interest on Twitter.

Methods

Over a period of 12 consecutive days in 2018, we collected tweets containing the hashtag #wakeupweightwatchers and hashtags related to Eating Disorder Awareness Week (#eatingdisorderawarenessweek, #eatingdisorderawareness, or #EDAW), with the hashtag #eatingdisorder as a control. The content of each tweet was rated as medical, testimony, help offer, awareness, pro-ana, or anti-ana. We analyzed the number of retweets and favorites generated, as well as the potential reach and impact of the hashtags and the characteristics of contributors.

Results

The number of #wakeupweightwatchers tweets was higher than that of Eating Disorder Awareness Week and #eatingdisorder tweets (3900, 2056, and 1057, respectively). The content of tweets was significantly different between the hashtags analyzed (P<.001). Medical content was lower in the awareness campaigns. Awareness and help offer content were lower in #wakeupweightwatchers tweets. Retweet and favorite ratios were highest in #wakeupweightwatchers tweets. Eating Disorder Awareness Week achieved the highest impact, and very influential contributors participated.

Conclusions

Both awareness campaigns effectively promoted tweeting about eating disorders. The majority of tweets did not promote any specific preventive or help-seeking behaviors.

A machine learning approach predicts future risk to suicidal ideation from social media data

Machine learning analysis of social media data represents a promising way to capture longitudinal environmental influences contributing to individual risk for suicidal thoughts and behaviors. Our objective was to generate an algorithm termed "Suicide Artificial Intelligence Prediction Heuristic (SAIPH)" capable of predicting future risk to suicidal thought by analyzing publicly available Twitter data. We trained a series of neural networks on Twitter data queried against suicide associated psychological constructs including burden, stress, loneliness, hopelessness, insomnia, depression, and anxiety. Using 512,526 tweets from N = 283 suicidal ideation (SI) cases and 3,518,494 tweets from 2655 controls, we then trained a random forest model using neural network outputs to predict binary SI status. The model predicted N = 830 SI events derived from an independent set of 277 suicidal ideators relative to N = 3159 control events in all non-SI individuals with an AUC of 0.88 (95% CI 0.86-0.90). Using an alternative approach, our model generates temporal prediction of risk such that peak occurrences above an individual specific threshold denote a ~7 fold increased risk for SI within the following 10 days (OR = 6.7 ± 1.1, P = 9 × 10-71). We validated our model using regionally obtained Twitter data and observed significant associations of algorithm SI scores with county-wide suicide death rates across 16 days in August and in October, 2019, most significantly in younger individuals. Algorithmic approaches like SAIPH have the potential to identify individual future SI risk and could be easily adapted as clinical decision tools aiding suicide screening and risk monitoring using available technologies.

Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis

BACKGROUND

Mental disorders have become a major concern in public health, and they are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts, and feelings of people's daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English.

OBJECTIVE

The study aimed to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users who generate them, which could suggest signs of depression.

METHODS

This study was developed in 2 steps. In the first step, the selection of users and the compilation of tweets were performed. A total of 3 datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mentioned that they suffer from depression), a depressive tweets dataset (a manual selection of tweets from the previous users, which included expressions indicative of depression), and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the 3 datasets of tweets were carried out.

RESULTS

In comparison with the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 (P<.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users (P<.001). By contrast, the use of verbs is more common in the depressive users dataset (P<.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80%), and the first- and the second-person plural pronouns were the least frequent (0.4% in both cases), this distribution being different from that of the control dataset (P<.001). Emotions related to sadness, anger, and disgust were more common in the depressive users and depressive tweets datasets, with significant differences when comparing these datasets with the control dataset (P<.001). As for negation words, they were detected in 34% and 46% of tweets in among depressive users and in depressive tweets, respectively, which are significantly different from the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control dataset (43.5%; P<.001).

CONCLUSIONS

Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. On the basis of these changes, these users can be monitored and supported, thus introducing new opportunities for studying depression and providing additional health care services to people with this disorder.

Exploring the Extent of the Hikikomori Phenomenon on Twitter: Mixed Methods Study of Western Language Tweets

Background

Hikikomori is a severe form of social withdrawal, originally described in Japan but recently reported in other countries. Debate exists as to what extent hikikomori is viewed as a problem outside of the Japanese context.

Objective

We aimed to explore perceptions about hikikomori outside Japan by analyzing Western language content from the popular social media platform, Twitter.

Methods

We conducted a mixed methods analysis of all publicly available tweets using the hashtag #hikikomori between February 1 and August 16, 2018, in 5 Western languages (Catalan, English, French, Italian, and Spanish). Tweets were first classified as to whether they described hikikomori as a problem or a nonproblematic phenomenon. Tweets regarding hikikomori as a problem were then subclassified in terms of the type of problem (medical, social, or anecdotal) they referred to, and we marked if they referenced scientific publications or the presence of hikikomori in countries other than Japan. We also examined measures of interest in content related to hikikomori, including retweets, likes, and associated hashtags.

Results

A total of 1042 tweets used #hikikomori, and 656 (62.3%) were included in the content analysis. Most of the included tweets were written in English (44.20%) and Italian (34.16%), and a majority (56.70%) discussed hikikomori as a problem. Tweets referencing scientific publications (3.96%) and hikikomori as present in countries other than Japan (13.57%) were less common. Tweets mentioning hikikomori outside Japan were statistically more likely to be retweeted (P=.01) and liked (P=.01) than those not mentioning it, whereas tweets with explicit scientific references were statistically more retweeted (P=.01) but not liked (P=.10) than those without that reference. Retweet and like figures were not statistically significantly different among other categories and subcategories. The most associated hashtags included references to Japan, mental health, and the youth.

Conclusions

Hikikomori is a repeated word in non-Japanese Western languages on Twitter, suggesting the presence of hikikomori in countries outside Japan. Most tweets treat hikikomori as a problem, but the ways they post about it are highly heterogeneous.

Google Trends in Infodemiology and Infoveillance: Methodology Framework

Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.

Areas of Interest and Stigmatic Attitudes of the General Public in Five Relevant Medical Conditions: Thematic and Quantitative Analysis Using Twitter

BACKGROUND

Twitter is an indicator of real-world performance, thus, is an appropriate arena to assess the social consideration and attitudes toward psychosis.

OBJECTIVE

The aim of this study was to perform a mixed-methods study of the content and key metrics of tweets referring to psychosis in comparison with tweets referring to control diseases (breast cancer, diabetes, Alzheimer, and human immunodeficiency virus).

METHODS

Each tweet's content was rated as nonmedical (NM: testimonies, health care products, solidarity or awareness and misuse) or medical (M: included a reference to the illness's diagnosis, treatment, prognosis, or prevention). NM tweets were classified as positive or pejorative. We assessed the appropriateness of the medical content. The number of retweets generated and the potential reach and impact of the hashtags analyzed was also investigated.

RESULTS

We analyzed a total of 15,443 tweets: 8055 classified as NM and 7287 as M. Psychosis-related tweets (PRT) had a significantly higher frequency of misuse 33.3% (212/636) vs 1.15% (853/7419; P<.001) and pejorative content 36.2% (231/636) vs 11.33% (840/7419; P<.001). The medical content of the PRT showed the highest scientific appropriateness 100% (391/391) vs 93.66% (6030/6439; P<.001) and had a higher frequency of content about disease prevention. The potential reach and impact of the tweets related to psychosis were low, but they had a high retweet-to-tweet ratio.

CONCLUSIONS

We show a reduced number and a different pattern of contents in tweets about psychosis compared with control diseases. PRT showed a predominance of nonmedical content with increased frequencies of misuse and pejorative tone. However, the medical content of PRT showed high scientific appropriateness aimed toward prevention.

Seasonality Patterns of Internet Searches on Mental Health: Exploratory Infodemiology Study

BACKGROUND

The study of seasonal patterns of public interest in psychiatric disorders has important theoretical and practical implications for service planning and delivery. The recent explosion of internet searches suggests that mining search databases yields unique information on public interest in mental health disorders, which is a significantly more affordable approach than population health studies.

OBJECTIVE

This study aimed to investigate seasonal patterns of internet mental health queries in Ontario, Canada.

METHODS

Weekly data on health queries in Ontario from Google Trends were downloaded for a 5-year period (2012-2017) for the terms "schizophrenia," "autism," "bipolar," "depression," "anxiety," "OCD" (obsessive-compulsive disorder), and "suicide." Control terms were overall search results for the terms "health" and "how." Time-series analyses using a continuous wavelet transform were performed to isolate seasonal components in the search volume for each term.

RESULTS

All mental health queries showed significant seasonal patterns with peak periodicity occurring over the winter months and troughs occurring during summer, except for "suicide." The comparison term "health" also exhibited seasonal periodicity, while the term "how" did not, indicating that general information seeking may not follow a seasonal trend in the way that mental health information seeking does.

CONCLUSIONS

Seasonal patterns of internet search volume in a wide range of mental health terms were observed, with the exception of "suicide." Our study demonstrates that monitoring internet search trends is an affordable, instantaneous, and naturalistic method to sample public interest in large populations and inform health policy planners.

Analyzing Twitter as a Platform for Alzheimer-Related Dementia Awareness: Thematic Analyses of Tweets

Background

Dementia is a prevalent disorder among adults and often subjects an individual and his or her family. Social media websites may serve as a platform to raise awareness for dementia and allow researchers to explore health-related data.

Objective

The objective of this study was to utilize Twitter, a social media website, to examine the content and location of tweets containing the keyword “dementia” to better understand the reasons why individuals discuss dementia. We adopted an approach that analyzed user location, user category, and tweet content subcategories to classify large publicly available datasets.

Methods

A total of 398 tweets were collected using the Twitter search application programming interface with the keyword “dementia,” circulated between January and February 2018. Twitter users were categorized into 4 categories: general public, health care field, advocacy organization, and public broadcasting. Tweets posted by “general public” users were further subcategorized into 5 categories: mental health advocate, affected persons, stigmatization, marketing, and other. Placement into the categories was done through thematic analysis.

Results

A total of 398 tweets were written by 359 different screen names from 28 different countries. The largest number of Twitter users were from the United States and the United Kingdom. Within the United States, the largest number of users were from California and Texas. The majority (281/398, 70.6%) of Twitter users were categorized into the “general public” category. Content analysis of tweets from the “general public” category revealed stigmatization (113/281, 40.2%) and mental health advocacy (102/281, 36.3%) as the most common themes. Among tweets from California and Texas, California had more stigmatization tweets, while Texas had more mental health advocacy tweets.

Conclusions

Themes from the content of tweets highlight the mixture of the political climate and the supportive network present on Twitter. The ability to use Twitter to combat stigma and raise awareness of mental health indicates the benefits that can potentially be facilitated via the platform, but negative stigmatizing tweets may interfere with the effectiveness of this social support.