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

Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review

BACKGROUND

The internet community has become a significant source for researchers to conduct qualitative studies analyzing users' views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data.

OBJECTIVE

This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care.

METHODS

We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These studies, limited to the English language, were retrieved to evaluate the rates of reporting ethical approval, informed consent, and privacy issues. We searched 5 databases, that is, PubMed, Web of Science, CINAHL, Cochrane, and Embase. Gray literature was supplemented from Google Scholar and OpenGrey websites. Studies using qualitative methods mining text from the internet community focusing on health care topics were deemed eligible. Data extraction was performed using a standardized data extraction spreadsheet. Findings were reported using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.

RESULTS

After 4674 titles, abstracts, and full texts were screened, 108 studies on mining text from the internet community were included. Nearly half of the studies were published in the United States, with more studies from 2019 to 2022. Only 59.3% (64/108) of the studies sought ethical approval, 45.3% (49/108) mentioned informed consent, and only 12.9% (14/108) of the studies explicitly obtained informed consent. Approximately 86% (12/14) of the studies that reported informed consent obtained digital informed consent from participants/administrators, while 14% (2/14) did not describe the method used to obtain informed consent. Notably, 70.3% (76/108) of the studies contained users' written content or posts: 68% (52/76) contained verbatim quotes, while 32% (24/76) paraphrased the quotes to prevent traceability. However, 16% (4/24) of the studies that paraphrased the quotes did not report the paraphrasing methods. Moreover, 18.5% (20/108) of the studies used aggregated data analysis to protect users' privacy. Furthermore, the rates of reporting ethical approval were different between different countries (P=.02) and between papers that contained users' written content (both direct and paraphrased quotes) and papers that did not contain users' written content (P<.001).

CONCLUSIONS

Our scoping review demonstrates that the reporting of ethical considerations is widely neglected in qualitative research studies using social media data; such studies should be more cautious in citing user quotes to maintain user privacy. Further, our review reveals the need for detailed information on the precautions of obtaining informed consent and paraphrasing to reduce the potential bias. A national consensus of ethical considerations such as ethical approval, informed consent, and privacy issues is needed for qualitative research of health care using social media data of internet communities.

The Impact of Occupational Therapy on the Self-Management of Rheumatoid Arthritis: A Mixed Methods Systematic Review

OBJECTIVE

To determine the impact of occupational therapy (OT) on the self-management of function, pain, fatigue, and lived experience for people living with rheumatoid arthritis (RA).

METHODS

Five databases and gray literature were searched up to June 30, 2022. Three reviewers screened titles and abstracts, with two independently extracting and assessing full texts using the Cochrane risk of bias (quantitative) and Critical Appraisal Skills Programme (qualitative) tools to assess study quality. Studies were categorized into four intervention types. Grading of Recommendations, Assessment, Development and Evaluations (GRADE) (quantitative) and GRADE- Confidence in Evidence from Reviews of Qualitative research (qualitative) were used to assess the quality of evidence for each intervention type.

RESULTS

Of 39 eligible papers, 29 were quantitative (n = 2,029), 4 qualitative (n = 50), and 6 mixed methods (n = 896). Good evidence supports patient education and behavior change programs for improving pain and function, particularly group sessions of joint protection education, but these do not translate to long-term improvements for RA (>24 months). Comprehensive OT had mixed evidence (limited to home OT and an arthritis gloves program), whereas limited evidence was available for qualitative insights, splints and assistive devices, and self-management for fatigue.

CONCLUSION

Although patient education is promising for self-managing RA, no strong evidence was found to support OT programs for self-managing fatigue or patient experience and long-term effectiveness. More research is required on lived experience, and the long-term efficacy of self-management approaches incorporating OT, particularly timing programs to meet the individual's conditional needs (i.e., early or established RA) to build on the few studies to date.

Uncovering emotional and network dynamics in the speech of patients with chronic low back pain

BACKGROUND

Computational linguistics allows an understanding of language structure and different forms of expression of patients' perceptions.

AIMS

The aims of this study were (i) to carry out a descriptive analysis of the discourse of people with chronic low back pain using sentiment analysis (SA) and network analysis; (ii) to verify the correlation between patients' profiles, pain intensity and disability levels with SA and network analysis; and (iii) to identify clusters in our sample according to language and SA using an unsupervised machine learning technique.

METHODS

We performed a secondary analysis of a qualitative study including participants with chronic non-specific low back pain. We used the data related to participants' feelings when they received the diagnosis. The SA and network analysis were performed using the Valence Aware Dictionary and sEntiment Reasoner, and the Speech Graph, respectively. Clustering was performed using the K-means algorithm.

RESULTS

In the SA, the mean composite score was -0.31 (Sd. = 0.58). Most participants presented a negative discourse (n = 41; 72%). Word Count (WC) and Largest Strongly connected Component (LSC) positively correlated with education. No statistically significant correlations were observed between pain intensity, disability levels, SA, and network analysis. Two clusters were identified in our sample.

CONCLUSION

The SA showed that participants reported their feeling when describing the moment of the diagnosis using sentences with negative discourse. We did not find a statistically significant correlation between pain intensity, disability levels, SA, and network analysis. Education level presented positive correlation with WC and LSC.

Content and sentiment analysis of gabapentinoid-related tweets: An infodemiology study

INTRODUCTION

The increasing number of gabapentinoid (pregabalin and gabapentin) harms, including deaths observed across countries is concerning to health-care professionals and policy makers. However, it is unclear if the public shares these concerns. This study aimed to describe posts related to gabapentinoids, conduct a content analysis to identify common themes and describe adverse events or symptoms.

METHODS

Keywords of 'pregabalin' or 'Lyrica' or 'gabapentin' or 'Neurontin' were used to search for related tweets posted by people in the community between 8 March and 7 May 2021. Eligible tweets included a keyword in the post. We extracted de-identified data which included descriptive data of the total number of posts over time; and data on individual tweets including date, number of re-tweets and post content. Data were exported separately for pregabalin- and gabapentin-related tweets. A 20% random sample was used for the thematic analysis.

RESULTS

There were 2931 pregabalin-related tweets and 2736 gabapentin-related tweets. Thematic analysis revealed three themes (sharing positive experiences and benefits of taking gabapentinoids, people voicing their negative experiences, and people seeking opinions and sharing information). Positive experiences of gabapentinoids were related to sharing stories and giving advice. This was contrasted to negative experiences including ineffectiveness, withdrawals, side effects and frustration related to cost and insurance coverage. Brain fog was the most common adverse symptom reported. Gabapentinoid-related deaths were only mentioned in three tweets.

DISCUSSION

The increasing public health concern of gabapentinoid-related deaths was not translated to Twitter discussions.

Exploring the emotional impact of axial Spondyloarthritis: a systematic review and thematic synthesis of qualitative studies and a review of social media

BACKGROUND

The psychological burden in people with inflammatory arthritis is substantial, yet little is known about the disease-related affect experienced by individuals with axial Spondyloarthritis (axial SpA). The aim of this study was to conduct a qualitative evidence synthesis and a review of social media to explore the emotional impact of living with axial SpA.

METHODS

We searched nine databases for studies reporting qualitative data about participants' emotional experience of living with axial SpA. In addition, we searched social media platforms for posts from people with axial SpA based in the UK that offered insights into emotional responses to living with the condition. We employed a thematic approach to synthesise the data.

RESULTS

We included 27 studies (1314 participants; 72% men) in our qualitative evidence synthesis and developed seven descriptive themes from the data: 1) delayed diagnosis: a barrier to emotional wellbeing; 2) disruptive symptoms: a source of mood swings; 3) work disability: a loss of self-esteem; 4) obstacles in interpersonal relationships: a trigger of distress; 5) taking up exercise: personal pride or unwelcomed reminders; 6) anti-TNF therapy: hope reignited despite concerns and 7) a journey of acceptance: worry mixed with hope. Posts extracted from social media fora (537; 48% from women) for the most part supported the seven themes. One additional theme-COVID-19, uncertainty and anxiety during the pandemic, was developed, reflecting common emotions expressed during the UK's first wave of the coronavirus pandemic.

CONCLUSION

This study highlights a preponderance of negative affect experienced by people living with axial SpA, conditioned through existing and anticipated symptoms, failed expectations, and lost sense of self. Given the bidirectional relationships between negative emotions and inflammation, negative emotions and perceptions of pain, and the influence of affect in self-care behaviours, this finding has important implications for treatment and management of people with axial SpA.

Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review

BACKGROUND

Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care.

OBJECTIVE

This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care?

METHODS

A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.

RESULTS

Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education).

CONCLUSIONS

Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.

Exploring the Association Between Suicide Prevention Public Service Announcements and User Comments on YouTube: A Computational Text Analysis Approach

In the United States, suicide rates have increased by 30% over the past few decades. Public service announcements (PSAs) are effective health promotion vehicles and social media can help spread PSAs to hard-to-engage individuals who may benefit from intervention efforts, yet the most meaningful characteristics of PSAs for influencing health promotion attitudes and behaviors are inconclusive. This study applied content and quantitative text analyses to suicide prevention PSAs and comments on YouTube to assess the relationships between message frame, message format, and the level of sentiment and help-seeking language within them. Seventy-two PSAs were analyzed for gain/loss-framing and narrative/argument-format, and 4,335 related comments were analyzed for positive/negative sentiment and frequency of help-seeking language use. Results indicate that a higher ratio of positive comments was more likely to be found on gain-framed and narrative-formatted PSAs, and a higher ratio of comments with help-seeking language was more likely to be found on narrative-formatted PSAs. Implications and future research are discussed.

The role of occupational therapy for the self-management of rheumatoid arthritis: A protocol for a mixed methods systematic review

BACKGROUND

Occupational therapists can support people with rheumatoid arthritis to self-manage their disease symptoms and engage in daily activities. This protocol reports a review to broaden understanding of what is known about the role of occupational therapy in the self-management of rheumatoid arthritis.

METHODS

Studies involving adults with rheumatoid arthritis, having participated in self-management involving occupational therapy, will be included. Patient involvement will help develop the search strategy by identifying patient-centred interventions and outcomes to complement those identified by researchers. An electronic search will be performed using several bibliographic databases, including grey literature from subject-specific, health-related, and social care databases. Searches will run from the database inception until the date that the search is conducted (December 2021-May 2022). Retrieved studies will be de-duplicated, and the remaining titles and abstracts will be screened by three reviewers. Full texts of all eligible studies will be independently reviewed by the reviewers to select papers for data extraction and quality assessment. Outcomes are function, pain, fatigue and lived experience. For quantitative studies, data will be synthesised using descriptive statistics in text and tables, whereas for qualitative studies, data will be synthesised using thematic synthesis.

DISCUSSION

This review will synthesise current evidence on how occupational therapy can help the self-management of rheumatoid arthritis. It will include evidence of best practice, including advice, education and training provided by occupational therapists. These findings can inform future research and the selection of strategies to promote quality of life for people with rheumatoid arthritis.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022302205.

Using Patient-Reported Health Data From Social Media to Identify Diverse Lupus Patients and Assess Their Symptom and Medication Expressions: A Feasibility Study

OBJECTIVE

Patient communities use social media for peer support and information seeking. This study assessed the feasibility of using public patient-generated health data from the social network Twitter to identify diverse lupus patients and gather their perspectives about disease symptoms and medications.

METHODS

We extracted public lupus-related Twitter messages (n = 47,715 tweets) in English posted by users (n = 8,446) in the US between September 1, 2017 and October 31, 2018. We analyzed the data to describe lupus patients and the expressed themes (symptoms and medications). Two independent coders analyzed the data; Cohen's kappa coefficient was used to ensure interrater reliability. Differences in symptom and medication expressions were analyzed using 2-tailed Z tests and a combination of 1-way analysis of variance tests and unpaired t-tests.

RESULTS

We found that lupus patients on Twitter are diverse in gender and race: approximately one-third (34.64%, 62 of 179) were persons of color (POCs), and 85.47% were female. The expressed disease symptoms and medications varied significantly by gender and race. Most of our findings correlated with documented clinical observations, e.g., expressions of general pain (8.39%, 709 of 8,446), flares (6.05%, 511 of 8,446), and fatigue (4.18%, 353 of 8,446). However, our data also revealed less well-known patient observations, e.g., possible racial disparities within ocular manifestations of lupus.

CONCLUSION

Our results indicate that social media surveillance can provide valuable data of clinical relevance from the perspective of lupus patients. The medical community has the opportunity to harness this information to inform the patient-centered care within underrepresented patient groups, such as POCs.

Survey on sentiment analysis: evolution of research methods and topics

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis

OBJECTIVES

The present study aims to examine coronavirus disease 2019 (COVID-19) vaccination discussions on Twitter in Turkey and conduct sentiment analysis.

METHODS

The current study performed sentiment analysis of Twitter data with the artificial intelligence (AI) Natural Language Processing (NLP) method. The tweets were retrieved retrospectively from March 10, 2020, when the first COVID-19 case was seen in Turkey, to April 18, 2022. A total of 10,308 tweets accessed. The data were filtered before analysis due to excessive noise. First, the text is tokenized. Many steps were applied in normalizing texts. Tweets about the COVID-19 vaccines were classified according to basic emotion categories using sentiment analysis. The resulting dataset was used for training and testing ML (ML) classifiers.

RESULTS

It was determined that 7.50% of the tweeters had positive, 0.59% negative, and 91.91% neutral opinions about the COVID-19 vaccination. When the accuracy values of the ML algorithms used in this study were examined, it was seen that the XGBoost (XGB) algorithm had higher scores.

CONCLUSIONS

Three of 4 tweets consist of negative and neutral emotions. The responsibility of professional chambers and the public is essential in transforming these neutral and negative feelings into positive ones.

A Perspective of COVID-19 and Healthcare: Using Social Media Data and an Aspect-based Sentiment Analysis for Usability Evaluation of a Wearable Mixed Reality Headset

Background

Mixed reality (MR) devices provide real-time environments for physical-digital interactions across many domains. Owing to the unprecedented COVID-19 pandemic, MR technologies have supported many new use cases in the health care industry, enabling social distancing practices to minimize the risk of contact and transmission. Despite their novelty and increasing popularity, public evaluations are sparse and often rely on social interactions among users, developers, researchers, and potential buyers.

Objective

The purpose of this study is to use aspect-based sentiment analysis to explore changes in sentiment during the onset of the COVID-19 pandemic as new use cases emerged in the health care industry; to characterize net insights for MR developers, researchers, and users; and to analyze the features of HoloLens 2 (Microsoft Corporation) that are helpful for certain fields and purposes.

Methods

To investigate the user sentiment, we collected 8492 tweets on a wearable MR headset, HoloLens 2, during the initial 10 months since its release in late 2019, coinciding with the onset of the pandemic. Human annotators rated the individual tweets as positive, negative, neutral, or inconclusive. Furthermore, by hiring an interannotator to ensure agreements between the annotators, we used various word vector representations to measure the impact of specific words on sentiment ratings. Following the sentiment classification for each tweet, we trained a model for sentiment analysis via supervised learning.

Results

The results of our sentiment analysis showed that the bag-of-words tokenizing method using a random forest supervised learning approach produced the highest accuracy of the test set at 81.29%. Furthermore, the results showed an apparent change in sentiment during the COVID-19 pandemic period. During the onset of the pandemic, consumer goods were severely affected, which aligns with a drop in both positive and negative sentiment. Following this, there is a sudden spike in positive sentiment, hypothesized to be caused by the new use cases of the device in health care education and training. This pandemic also aligns with drastic changes in the increased number of practical insights for MR developers, researchers, and users and positive net sentiments toward the HoloLens 2 characteristics.

Conclusions

Our approach suggests a simple yet effective way to survey public opinion about new hardware devices quickly. The findings of this study contribute to a holistic understanding of public perception and acceptance of MR technologies during the COVID-19 pandemic and highlight several new implementations of HoloLens 2 in health care. We hope that these findings will inspire new use cases and technological features.

An Exercise and Educational and Self-management Program Delivered With a Smartphone App (CareHand) in Adults With Rheumatoid Arthritis of the Hands: Randomized Controlled Trial

BACKGROUND

Rheumatoid arthritis (RA) is a prevalent autoimmune disease that usually involves problems of the hand or wrist. Current evidence recommends a multimodal therapy including exercise, self-management, and educational strategies. To date, the efficacy of this approach, as delivered using a smartphone app, has been scarcely investigated.

OBJECTIVE

This study aims to assess the short- and medium-term efficacy of a digital app (CareHand) that includes a tailored home exercise program, together with educational and self-management recommendations, compared with usual care, for people with RA of the hands.

METHODS

A single-blinded randomized controlled trial was conducted between March 2020 and February 2021, including 36 participants with RA of the hands (women: 22/36, 61%) from 2 community health care centers. Participants were allocated to use the CareHand app, consisting of tailored exercise programs, and self-management and monitoring tools or to a control group that received a written home exercise routine and recommendations, as per the usual protocol provided at primary care settings. Both interventions lasted for 3 months (4 times a week). The primary outcome was hand function, assessed using the Michigan Hand Outcome Questionnaire (MHQ). Secondary measures included pain and stiffness intensity (visual analog scale), grip strength (dynamometer), pinch strength (pinch gauge), and upper limb function (shortened version of the Disabilities of the Arm, Shoulder, and Hand questionnaire). All measures were collected at baseline and at a 3-month follow-up. Furthermore, the MHQ and self-reported stiffness were assessed 6 months after baseline, whereas pain intensity and scores on the shortened version of the Disabilities of the Arm, Shoulder, and Hand questionnaire were collected at the 1-, 3-, and 6-month follow-ups.

RESULTS

In total, 30 individuals, corresponding to 58 hands (CareHand group: 26/58, 45%; control group: 32/58, 55%), were included in the analysis; 53% (19/36) of the participants received disease-modifying antirheumatic drug treatment. The ANOVA demonstrated a significant time×group effect for the total score of the MHQ (F_1.62,85.67=9.163; P<.001; η²=0.15) and for several of its subscales: overall hand function, work performance, pain, and satisfaction (all P<.05), with mean differences between groups for the total score of 16.86 points (95% CI 8.70-25.03) at 3 months and 17.21 points (95% CI 4.78-29.63) at 6 months. No time×group interaction was observed for the secondary measures (all P>.05).

CONCLUSIONS

Adults with RA of the hands who used the CareHand app reported better results in the short and medium term for overall hand function, work performance, pain, and satisfaction, compared with usual care. The findings of this study suggest that the CareHand app is a promising tool for delivering exercise therapy and self-management recommendations to this population. Results must be interpreted with caution because of the lack of efficacy of the secondary outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04263974; https://clinicaltrials.gov/ct2/show/NCT04263974.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s13063-020-04713-4.

The impact and applications of social media platforms for public health responses before and during COVID-19

Background

 Social media platforms have numerous potential benefits and drawbacks on public health, which have been described in the literature. The COVID-19 pandemic has exposed our limited knowledge regarding the potential health impact of these platforms, which have been detrimental to public health responses in many regions.

Objective

This review aims to highlight a brief history of social media in health care and report its potential negative and positive public health impacts, which have been characterized in the literature.

Methods

 We searched electronic bibliographic databases including PubMed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from December 10, 2015, to December 10, 2020. We screened the title and abstracts and selected relevant reports for review of full text and reference lists. These were analyzed thematically and consolidated into applications of social media platforms for public health.

Results

 The positive and negative impact of social media platforms on public health are catalogued on the basis of recent research in this report. These findings are discussed in the context of improving future public health responses and incorporating other emerging digital technology domains such as artificial intelligence. However, there is a need for more research with pragmatic methodology that evaluates the impact of specific digital interventions to inform future health policy.

Conclusions

 Recent research has highlighted the potential negative impact of social media platforms on population health, as well as potentially useful applications for public health communication, monitoring, and predictions. More research is needed to objectively investigate measures to mitigate against its negative impact while harnessing effective applications for the benefit of public health.

A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic

The COVID-19 pandemic has deeply impacted all aspects of social, professional, and financial life, with concerns and responses being readily published in online social media worldwide. This study employs probabilistic text mining techniques for a large-scale, high-resolution, temporal, and geospatial content analysis of Twitter related discussions. Analysis considered 20,230,833 English language original COVID-19-related tweets with global origin retrieved between January 25, 2020 and April 30, 2020. Fine grain topic analysis identified 91 meaningful topics. Most of the topics showed a temporal evolution with local maxima, underlining the short-lived character of discussions in Twitter. When compared to real-world events, temporal popularity curves showed a good correlation with and quick response to real-world triggers. Geospatial analysis of topics showed that approximately 30% of original English language tweets were contributed by USA-based users, while overall more than 60% of the English language tweets were contributed by users from countries with an official language other than English. High-resolution temporal and geospatial analysis of Twitter content shows potential for political, economic, and social monitoring on a global and national level.

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.

Designing a conceptual framework for misinformation on social media: a qualitative study on COVID-19

OBJECTIVE

This study was aimed to present a conceptual framework about the misinformation surrounding COVID-19 outbreak in Iran. For this purpose, discourse analysis of two of the most common social virtual networks were conducted via a four step approach as follows: defining the research question and selecting the content of analysis, gathering information and theory on the context, content analysis for establishing the themes and patterns and, presenting the results and drawing conclusions.

RESULTS

Cultural factors, demand pressure for information during the crisis, the easiness of information dissemination via social networks, marketing incentives and the poor legal supervision of online content are the main reasons for misinformation dissemination. Disease statistics; treatments and prevention are the main subjective categories of releasing misinformation. The consequences of misinformation dissemination include psychosocial, economic, health status, health system and ethical ones. The most recommended strategies for dealing with the issue could be divided into demand and supply-side strategies.

Understanding the emotional response to Covid-19 information in news and social media: A mental health perspective

The impact of the COVID‐19 pandemic and ensuing social restrictions has been profound, affecting the health, livelihoods, and wellbeing of populations worldwide. Studies have shown widespread effects on mental health, with an increase in stress, loneliness, and depression symptoms related to the pandemic. Media plays a critical role in containing and managing crises, by informing society and fostering positive behavior change. Social restrictions have led to a large increase in reliance on online media channels, and this can influence mental health and wellbeing. Anxiety levels, for instance, may be exacerbated by exposure to COVID‐related content, contagion of negative sentiment among social networks, and “fake news.” In some cases, this may trigger abstinence, leading to isolation and limited access to vital information. To be able to communicate distressing news during crises while protecting the wellbeing of individuals is not trivial; it requires a deeper understanding of people's emotional response to online and social media content. This paper selectively reviews research into consequences of social media usage and online news consumption for wellbeing and mental health, focusing on and discussing their effects in the context of the pandemic. Advances in Artificial Intelligence and Data Science, for example, Natural Language Processing, Sentiment Analysis, and Emotion Recognition, are discussed as useful methods for investigating effects on population mental health as the pandemic situation evolves. We present suggestions for future research, and for using these advances to assess large data sets of users' online content, to potentially inform strategies that enhance the mental health of social media users going forward.

Digital citizens' feelings in national #Covid 19 campaigns in Spain

BACKGROUND

In 2020 Spain launched an official campaign, #EsteVirusLoParamosUnidos, aimed at uniting the entire country through citizen cooperation, in order to combat Covid-19. The objective of this research has been to analyse how this Twitter campaign revealed the feelings expressed by Spanish citizens.

METHODS

The research is based on a composite design that triangulates, from a theoretical model, a quantitative analysis and a qualitative analysis.

RESULTS

Of the 7,357 tweets in the sample, 72.32% were found to be retweets. Four content families were extracted which relate to politics, education, messages to society and the defence of occupational groups. The feelings expressed ranged from those of unity, admiration and support to those of discontent and criticism of issues regarding the health situation.

CONCLUSIONS

The development of networked socio-political and technical measures, which enabled citizen participation, facilitated the development of new patterns of interaction between national or regional governments and digital citizens. This increased citizens' possibilities of influencing the public agenda and, therefore, strengthening citizen engagement regarding specific situations.

Research on the Characteristics and Usefulness of User Reviews of Online Mental Health Consultation Services: A Content Analysis

Online consultation based on Internet technology is gradually becoming the main way to seek health information and professional assistance. Online user reviews, such as content reviews and star ratings, are an important basis for reflecting users' views on the effectiveness of health services. Here, we used user reviews related to online psychological consultation services for content feature mining and usefulness analyses. We used a professional online psychological counseling service platform in China to collect user reviews that were liked by users as a data sample for a content analysis. An LDA topic model, dictionary-based sentiment analysis, and the NRC Word-Emotion Association Lexicon were used to extract the topic, sentiment, and context features of the content of 4254 useful reviews, and the influence of these features on the usefulness of the reviews was verified by a multiple linear regression analysis. Our results show that the content of online reviews by psychological counseling users presented a positive emotional attitude as a whole and expressed more views on the process, effects, and future expectations of counseling than on other topics. There was a significant correlation between the topic, sentiment, and context features of a user review and its usefulness: reviews giving high scores and containing topics such as "ease emotions" and "consulting expectations" received more user likes. However, the usefulness of a review was significantly reduced if it was in existence for too long. This research provides valuable suggestions for understanding the needs and emotional attitudes of users with mental health problems in terms of online psychological consultation; identifying the factors that affect the number of likes a review receives can help platform users write better consultation evaluations and thereby provide greater usefulness. In addition, the use of online reviews generated by users for content analysis effectively supplements the current research on online psychological counseling in terms of data and methods.

COVID-19 pandemic impact on people with rheumatic and musculoskeletal diseases: Insights from patient-generated health data on social media

Objectives

During the COVID-19 pandemic, much communication occurred online, through social media. This study aimed to provide patient perspective data on how the COVID-19 pandemic impacted people with rheumatic and musculoskeletal diseases (RMDs), using Twitter-based patient-generated health data (PGHD).

Methods

A convenience sample of Twitter messages in English posted by people with RMDs was extracted between March 1, and July 12, 2020 and examined using thematic analysis. Included were Twitter messages that mentioned keywords and hashtags related to both COVID-19 (or SARS-CoV-2) and select RMDs. The RMDs monitored included inflammatory-driven (joint) conditions (Ankylosing Spondylitis, Rheumatoid Arthritis, Psoriatic Arthritis, Lupus/Systemic Lupus Erythematosus, and Gout).

Results

The analysis included 569 tweets by 375 Twitter users with RMDs across several countries. Eight themes emerged regarding the impact of the COVID-19 pandemic on people with RMDs: (1) lack of understanding of SARS-CoV-2/COVID-19; (2) critical changes in health behaviour; (3) challenges in healthcare practice and communication with healthcare professionals; (4) difficulties with access to medical care; (5) negative impact on physical and mental health, coping strategies; (6) issues around work participation, (7) negative effects of the media; (8) awareness-raising.

Conclusion

The findings show that Twitter serves as a real-time data source to understand the impact of the COVID-19 pandemic on people with RMDs. The platform provided “early signals” of potentially critical health behaviour changes. Future epidemics might benefit from the real-time use of Twitter-based PGHD to identify emerging health needs, facilitate communication, and inform clinical practice decisions.

The Social Aspects of Sexual Health: A Twitter-Based Analysis of Valentine’s Day Perception

Sentiment analysis (SA) is a technique aimed at extracting opinions and sentiments through the analysis of text, often used in healthcare research to understand patients’ needs and interests. Data from social networks, such as Twitter, can provide useful insights on sexual behavior. We aimed to assess the perception of Valentine’s Day by performing SA on tweets we collected between 28 January and 13 February 2019. Analysis was done using ad hoc software. A total of 883,615 unique tweets containing the word “valentine” in their text were collected. Geo-localization was available for 48,918 tweets; most the tweets came from the US (36,889, 75.41%), the UK (2605, 5.33%) and Canada (1661, 3.4%). The number of tweets increased approaching February 14. “Love” was the most recurring word, appearing in 111,981 tweets, followed by “gift” (55,136), “special” (34,518) and “happy” (33,913). Overall, 7318 tweets mentioned “sex”: among these tweets, the most recurring words were “sexy” (2317 tweets), “love” (1394) and “gift” (679); words pertaining to intimacy and sexual activity, such as “lingerie”, “porn”, and “date” were less common. In conclusion, tweets about Valentine’s Day mostly focus on the emotions, or on the material aspect of the celebration, and the sexual aspect of Valentine’s Day is rarely mentioned.