Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis.

BACKGROUND: This research extends prior studies by the Finnish Institute for Health and Welfare on pandemic-related risk perception, concentrating on the role of trust in health authorities and its impact on public health outcomes. OBJECTIVE: The paper aims to investigate variations in trust levels over time and across social media platforms, as well as to further explore 12 subcategories of political mistrust. It seeks to understand the dynamics of political trust, including mistrust accumulation, fluctuations over time, and changes in topic relevance. Additionally, the study aims to compare qualitative research findings with those obtained through computational methods. METHODS: Data were gathered from a large-scale data set consisting of 13,629 Twitter and Facebook posts from 2020 to 2023 related to COVID-19. For analysis, a fine-tuned FinBERT model with an 80% accuracy rate was used for predicting political mistrust. The BERTopic model was also used for superior topic modeling performance. RESULTS: Our preliminary analysis identifies 43 mistrust-related topics categorized into 9 major themes. The most salient topics include COVID-19 mortality, coping strategies, polymerase chain reaction testing, and vaccine efficacy. Discourse related to mistrust in authority is associated with perceptions of disease severity, willingness to adopt health measures, and information-seeking behavior. Our findings highlight that the distinct user engagement mechanisms and platform features of Facebook and Twitter contributed to varying patterns of mistrust and susceptibility to misinformation during the pandemic. CONCLUSIONS: The study highlights the effectiveness of computational methods like natural language processing in managing large-scale engagement and misinformation. It underscores the critical role of trust in health authorities for effective risk communication and public compliance. The findings also emphasize the necessity for transparent communication from authorities, concluding that a holistic approach to public health communication is integral for managing health crises effectively.

Patient perception of spinal cord injury through social media: An analysis of 703 Instagram and 117 Twitter posts.

Introduction: Social media has developed exponentially over the last decade as a means for individuals and patients to connect to others and has provided a unique opportunity for physicians to provide broader information to the general public to attempt to positively modify health behavior. The purpose of this study was to assess the patient's perception of spinal cord injury (SCI) on social media. Methods: Instagram and Twitter social media platforms were analyzed to determine posts written by patients with SCI. The initial search for Instagram posts tagged with "#spinalcordinjury" yielded over 270,000 posts in April 2021. Posts pertaining to the patient's experience were retrospectively collected from January 2020 to April 2021. Twitter posts that included "#spinalcordinjury," "@spinalcordinjury," and "spinal cord injury" were retrospectively collected in April 2021. One hundred seventeen tweets were found that were directly from a patient with SCI. Themes associated with patients' experiences living with SCI were coded. Results: The most common theme on Instagram was spreading positivity and on Twitter was the appearance of the wheelchair (75.8% and 37.3%, respectively). Other common themes on Instagram were the appearance of a wheelchair (71.8%), recovery or rehabilitation (29.9%), and life satisfaction (29.0%). Prevalent themes on Twitter included spreading positivity (23.2%) and recovery or rehabilitation (21.3%). Conclusion: The prevalence of themes of positivity and awareness may indicate the utilization of social media as a support mechanism for patients living with SCI. Identification of prevalent themes is important for the holistic treatment of SCI survivors.

Characterizing Twitter Content About HIV Pre-exposure Prophylaxis (PrEP) for Women: Qualitative Content Analysis.

BACKGROUND: HIV remains a persistent health problem in the United States, especially among women. Approved in 2012, HIV pre-exposure prophylaxis (PrEP) is a daily pill or bimonthly injection that can be taken by individuals at increased risk of contracting HIV to reduce their risk of new infection. Women who are at risk of HIV face numerous barriers to HIV services and information, underscoring the critical need for strategies to increase awareness of evidence-based HIV prevention methods, such as HIV PrEP, among women. OBJECTIVE: We aimed to identify historical trends in the use of Twitter hashtags specific to women and HIV PrEP and explore content about women and PrEP shared through Twitter. METHODS: This was a qualitative descriptive study using a purposive sample of tweets containing hashtags related to women and HIV PrEP from 2009 to 2022. Tweets were collected via Twitter's API. Each Twitter user profile, tweet, and related links were coded using content analysis, guided by the framework of the Health Belief Model (HBM) to generate results. We used a factor analysis to identify salient clusters of tweets. RESULTS: A total of 1256 tweets from 396 unique users were relevant to our study focus of content about PrEP specifically for women (1256/2908, 43.2% of eligible tweets). We found that this sample of tweets was posted mostly by organizations. The 2 largest groups of individual users were activists and advocates (61/396, 15.4%) and personal users (54/396, 13.6%). Among individual users, most were female (100/166, 60%) and American (256/396, 64.6%). The earliest relevant tweet in our sample was posted in mid-2014 and the number of tweets significantly decreased after 2018. We found that 61% (496/820) of relevant tweets contained links to informational websites intended to provide guidance and resources or promote access to PrEP. Most tweets specifically targeted people of color, including through the use of imagery and symbolism. In addition to inclusive imagery, our factor analysis indicated that more than a third of tweets were intended to share information and promote PrEP to people of color. Less than half of tweets contained any HBM concepts, and only a few contained cues to action. Lastly, while our sample included only tweets relevant to women, we found that the tweets directed to lesbian, gay, bisexual, transgender, queer (LGBTQ) audiences received the highest levels of audience engagement. CONCLUSIONS: These findings point to several areas for improvement in future social media campaigns directed at women about PrEP. First, future posts would benefit from including more theoretical constructs, such as self-efficacy and cues to action. Second, organizations posting on Twitter should continue to broaden their audience and followers to reach more people. Lastly, tweets should leverage the momentum and strategies used by the LGBTQ community to reach broader audiences and destigmatize PrEP use across all communities.

How an Interest in Mindfulness Influences Linguistic Markers in Online Microblogging Discourse.

Objectives: This study aimed to investigate the linguistic markers of an interest in mindfulness. Specifically, it examined whether individuals who follow mindfulness experts on Twitter use different language in their tweets compared to a random sample of Twitter users. This is a first step which may complement commonly used self-report measures of mindfulness with quantifiable behavioural metrics. Method: A linguistic analysis examined the association between an interest in mindfulness and linguistic markers in 1.87 million Twitter entries across 19,732 users from two groups, (1) a mindfulness interest group (n = 10,347) comprising followers of five mindfulness experts and (2) a control group (n = 9385) of a random selection of Twitter users. Text analysis software (Linguistic Inquiry and Word Count) was used to analyse linguistic markers associated with the categories and subcategories of mindfulness, affective processes, social orientation, and "being" mode of mind. Results: Analyses revealed an association between an interest in mindfulness and lexical choice. Specifically, tweets from the mindfulness interest group contained a significantly higher frequency of markers associated with mindfulness, positive emotion, happiness, and social orientation, and a significantly lower frequency of markers associated with negative emotion, past focus, present focus, future focus, family orientation, and friend orientation. Conclusions: Results from this study suggest that an interest in mindfulness is associated with more frequent use of certain language markers on Twitter. The analysis opens possible pathways towards developing more naturalistic methods of understanding and assessing mindfulness which may complement self-reporting methods.

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses.

Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment; however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives: Drugs and Treatments, Causes and Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined six macro-parameters (Diseases and Disorders, Individual Factors, Social and Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for a social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

A two-year follow-up: Twitter activity regarding misinformation about spinal manipulation, chiropractic care and boosting immunity during the COVID-19 pandemic.

BACKGROUND: Spinal manipulative therapy (SMT) is offered by many health professions, most often by chiropractors. While SMT can be effective for some musculoskeletal disorders, there is no evidence that SMT improves human immunity in a clinically meaningful way. Despite this, we showed previously that Twitter misinformation about chiropractic/SMT  improving immunity increased sharply at the start of the COVID-19 pandemic. Here, we perform a two-year follow-up. METHODS: We previously employed specialized software (i.e. Talkwalker) to search the entirety of Twitter activity in the  months before and after the COVID-19 pandemic was declared (March 11, 2020). In this paper, we conducted follow-up searches over two successive 12 month periods using terms related to SMT, immunity and chiropractic. The resulting tweets were then coded into those promoting/refuting a relation between SMT and immunity (tone) and messaging about chiropractic/interventions (content). Further analyses were performed to subcategorize tweet content, tally likes, retweets and followers, and evaluate refuting tweets and the country of origin. Finally, we created a chronology of Twitter activity superimposed with dates of promoting or refuting activities undertaken by chiropractic organizations. RESULTS: Over the 27 month study period, Twitter activity peaked on March 31, 2020 then declined continuously. As in our first paper, our follow-up data showed that (1) the ratio of refuting/promoting tweets remained constant and (2) tweets that refuted a relationship between SMT and immunity were substantially more liked, retweeted and followed than those promoting. We also observed that promoting tweets suggesting that SMT improves immunity decreased more rapidly. Overwhelmingly, promoting tweets originated in the USA while refuting tweets originated in Canada, Europe and Australia. The timing of the decline in peak Twitter activity, together with a parallel decline in tweets claiming that SMT improves immunity, was coincident with initiatives by chiropractic organizations and regulators targeting misinformation. CONCLUSION: Overwhelmingly, Twitter activity during the COVID-19 pandemic focussed on refuting a relation between chiropractic/SMT and immunity. A decline in Twitter activity promoting a relation between SMT and immunity was observed to coincide with initiatives from chiropractic organizations and regulators to refute these claims. The majority of misinformation about this topic is generated in the United States.

Can Twitter posts serve as early indicators for potential safety signals? A retrospective analysis.

BACKGROUND: As Twitter has gained significant popularity, tweets can serve as large pool of readily available data to estimate the adverse events (AEs) of medications. OBJECTIVE: This study evaluated whether tweets were an early indicator for potential safety warnings. Additionally, the trend of AEs posted on Twitter was compared with AEs from the Yellow Card system in the United Kingdom. METHODS: English Tweets for 35 drug-event pairs for the period 2017-2019, two years prior to the date of EMA Pharmacovigilance Risk Assessment Committee (PRAC) meeting, were collected. Both signal and non-signal AEs were manually identified and encoded using the MedDRA dictionary. AEs from Yellow Card were also gathered for the same period. Descriptive and inferential statistical analysis was conducted using Fisher's exact test to assess the distribution and proportion of AEs from the two data sources. RESULTS: Of the total 61,661 English tweets, 1,411 had negative or neutral sentiment and mention of at least one AE. Tweets for 15 out of the 35 drugs (42.9%) contained AEs associated with the signals. On pooling data from Twitter and Yellow Card, 24 out of 35 drug-event pairs (68.6%) were identified prior to the respective PRAC meetings. Both data sources showed similar distribution of AEs based on seriousness, however, the distribution based on labelling was divergent. CONCLUSION: Twitter cannot be used in isolation for signal detection in current pharmacovigilance (PV) systems. However, it can be used in combination with traditional PV systems for early signal detection, as it can provide a holistic drug safety profile.

The International Natural Product Sciences Taskforce (INPST) and the power of Twitter networking exemplified through #INPST hashtag analysis.

BACKGROUND: The development of digital technologies and the evolution of open innovation approaches have enabled the creation of diverse virtual organizations and enterprises coordinating their activities primarily online. The open innovation platform titled "International Natural Product Sciences Taskforce" (INPST) was established in 2018, to bring together in collaborative environment individuals and organizations interested in natural product scientific research, and to empower their interactions by using digital communication tools. METHODS: In this work, we present a general overview of INPST activities and showcase the specific use of Twitter as a powerful networking tool that was used to host a one-week "2021 INPST Twitter Networking Event" (spanning from 31st May 2021 to 6th June 2021) based on the application of the Twitter hashtag #INPST. RESULTS AND CONCLUSION: The use of this hashtag during the networking event period was analyzed with Symplur Signals (https://www.symplur.com/), revealing a total of 6,036 tweets, shared by 686 users, which generated a total of 65,004,773 impressions (views of the respective tweets). This networking event's achieved high visibility and participation rate showcases a convincing example of how this social media platform can be used as a highly effective tool to host virtual Twitter-based international biomedical research events.

Twitter Poll as a Medium for Questionnaire-Based Health Survey: An Experience of a Pilot Study on the Preference of Systems of Medicine for Various Health Conditions.

Background The easy accessibility of smartphones and internet connections enables people to stay virtually connected to communities via social media. However, social media is also being explored for health care education and dissemination of health-related information. Twitter (Twitter, Inc., San Francisco, California) is one of the popular social media used for spreading health-related information. Twitter enables users to create polls to get opinions from their users. The Twitter poll is a less-explored avenue for health surveys. Objective In this pilot study, we aimed to explore the feasibility of conducting a questionnaire-based health survey (on the preference of different systems of medicine for the treatment of various health problems) as a Twitter poll. Methods This observational study was conducted on Twitter for five consecutive days starting from May 31, 2021. We posted five Twitter polls, one poll each day, for five days in a #INPST unique Twitter campaign. Preferences on the use of modern medicine, traditional medicine, a combination of these two systems, and self-medication were collected on five health conditions. We collected the data from the landing poll page and Tweet Analytics (insight about the engagement of tweets provided free by Twitter). The Chi-square test, binomial test, and one-way Analysis of Variance were used to compare data, and Spearman's rank correlation coefficient was used to find a correlation between categorical variables. Results We had a mean 4358.6±590.3 poll reach with the engagement of 108.2±36.87 Twitter users and 67.6±28.06 votes. Most of the responses were received on the first day of posting the poll. The participation then gradually decreased. Modern medicine was the first choice for emergency medical care (85.1%, P <0.0001), treatment of cancer (43.6%, P <0.0001), and sexual disorder or transmitted diseases (48.9%, P <0.0001). Traditional medicine was the first choice (37.5%, P = 0.63) for the treatment of common illnesses, and a combination of modern and traditional medicine was the first choice (37.5%, P = 0.01) for the treatment of chronic diseases. Conclusion A medical survey with short questions with a maximum of four response options can be conducted on Twitter. Survey results can be obtained without any third-party analytic service. The response rate is highest on the first day and participation may decrease when multiple polls are posted within a Twitter campaign. Preference for systems of medicine found in this study can be used for designing large-scale surveys in the future.

Social Media Users' Perceptions of a Wearable Mixed Reality Headset During the COVID-19 Pandemic: Aspect-Based Sentiment Analysis.

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.

Popular and Scientific Discourse on Autism: Representational Cross-Cultural Analysis of Epistemic Communities to Inform Policy and Practice.

BACKGROUND: Social media provide a window onto the circulation of ideas in everyday folk psychiatry, revealing the themes and issues discussed both by the public and by various scientific communities. OBJECTIVE: This study explores the trends in health information about autism spectrum disorder within popular and scientific communities through the systematic semantic exploration of big data gathered from Twitter and PubMed. METHODS: First, we performed a natural language processing by text-mining analysis and with unsupervised (machine learning) topic modeling on a sample of the last 10,000 tweets in English posted with the term #autism (January 2021). We built a network of words to visualize the main dimensions representing these data. Second, we performed precisely the same analysis with all the articles using the term "autism" in PubMed without time restriction. Lastly, we compared the results of the 2 databases. RESULTS: We retrieved 121,556 terms related to autism in 10,000 tweets and 5.7x109 terms in 57,121 biomedical scientific articles. The 4 main dimensions extracted from Twitter were as follows: integration and social support, understanding and mental health, child welfare, and daily challenges and difficulties. The 4 main dimensions extracted from PubMed were as follows: diagnostic and skills, research challenges, clinical and therapeutical challenges, and neuropsychology and behavior. CONCLUSIONS: This study provides the first systematic and rigorous comparison between 2 corpora of interests, in terms of lay representations and scientific research, regarding the significant increase in information available on autism spectrum disorder and of the difficulty to connect fragments of knowledge from the general population. The results suggest a clear distinction between the focus of topics used in the social media and that of scientific communities. This distinction highlights the importance of knowledge mobilization and exchange to better align research priorities with personal concerns and to address dimensions of well-being, adaptation, and resilience. Health care professionals and researchers can use these dimensions as a framework in their consultations to engage in discussions on issues that matter to beneficiaries and develop clinical approaches and research policies in line with these interests. Finally, our study can inform policy makers on the health and social needs and concerns of individuals with autism and their caregivers, especially to define health indicators based on important issues for beneficiaries.

Fine-Tuning BERT Models to Classify Misinformation on Garlic and COVID-19 on Twitter.

Garlic-related misinformation is prevalent whenever a virus outbreak occurs. With the outbreak of COVID-19, garlic-related misinformation is spreading through social media, including Twitter. Bidirectional Encoder Representations from Transformers (BERT) can be used to classify misinformation from a vast number of tweets. This study aimed to apply the BERT model for classifying misinformation on garlic and COVID-19 on Twitter, using 5929 original tweets mentioning garlic and COVID-19 (4151 for fine-tuning, 1778 for test). Tweets were manually labeled as 'misinformation' and 'other.' We fine-tuned five BERT models (BERTBASE, BERTLARGE, BERTweet-base, BERTweet-COVID-19, and BERTweet-large) using a general COVID-19 rumor dataset or a garlic-specific dataset. Accuracy and F1 score were calculated to evaluate the performance of the models. The BERT models fine-tuned with the COVID-19 rumor dataset showed poor performance, with maximum accuracy of 0.647. BERT models fine-tuned with the garlic-specific dataset showed better performance. BERTweet models achieved accuracy of 0.897-0.911, while BERTBASE and BERTLARGE achieved accuracy of 0.887-0.897. BERTweet-large showed the best performance with maximum accuracy of 0.911 and an F1 score of 0.894. Thus, BERT models showed good performance in classifying misinformation. The results of our study will help detect misinformation related to garlic and COVID-19 on Twitter.

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a novel infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite the paucity of evidence, various complementary, alternative and integrative medicines (CAIMs) have been being touted as both preventative and curative. We conducted sentiment and emotion analysis with the intent of understanding CAIM content related to COVID-19 being generated on Twitter across 9 months. METHODS: Tweets relating to CAIM and COVID-19 were extracted from the George Washington University Libraries Dataverse Coronavirus tweets dataset from March 03 to November 30, 2020. We trained and tested a machine learning classifier using a large, pre-labelled Twitter dataset, which was applied to predict the sentiment of each CAIM-related tweet, and we used a natural language processing package to identify the emotions based on the words contained in the tweets. RESULTS: Our dataset included 28 713 English-language Tweets. The number of CAIM-related tweets during the study period peaked in May 2020, then dropped off sharply over the subsequent three months; the fewest CAIM-related tweets were collected during August 2020 and remained low for the remainder of the collection period. Most tweets (n = 15 612, 54%) were classified as positive, 31% were neutral (n = 8803) and 15% were classified as negative (n = 4298). The most frequent emotions expressed across tweets were trust, followed by fear, while surprise and disgust were the least frequent. Though volume of tweets decreased over the 9 months of the study, the expressed sentiments and emotions remained constant. CONCLUSION: The results of this sentiment analysis enabled us to establish key CAIMs being discussed at the intersection of COVID-19 across a 9-month period on Twitter. Overall, the majority of our subset of tweets were positive, as were the emotions associated with the words found within them. This may be interpreted as public support for CAIM, however, further qualitative investigation is warranted. Such future directions may be used to combat misinformation and improve public health strategies surrounding the use of social media information.

Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study.

BACKGROUND: Failure to find and attract clinical trial participants remains a persistent barrier to clinical research. Researchers increasingly complement recruitment methods with social media-based methods. We hypothesized that user-generated data from cancer survivors and their family members and friends on the social network Twitter could be used to identify, engage, and recruit cancer survivors for cancer trials. OBJECTIVE: This pilot study aims to examine the feasibility of using user-reported health data from cancer survivors and family members and friends on Twitter in Los Angeles (LA) County to enhance clinical trial recruitment. We focus on 6 cancer conditions (breast cancer, colon cancer, kidney cancer, lymphoma, lung cancer, and prostate cancer). METHODS: The social media intervention involved monitoring cancer-specific posts about the 6 cancer conditions by Twitter users in LA County to identify cancer survivors and their family members and friends and contacting eligible Twitter users with information about open cancer trials at the University of Southern California (USC) Norris Comprehensive Cancer Center. We reviewed both retrospective and prospective data published by Twitter users in LA County between July 28, 2017, and November 29, 2018. The study enrolled 124 open clinical trials at USC Norris. We used descriptive statistics to report the proportion of Twitter users who were identified, engaged, and enrolled. RESULTS: We analyzed 107,424 Twitter posts in English by 25,032 unique Twitter users in LA County for the 6 cancer conditions. We identified and contacted 1.73% (434/25,032) of eligible Twitter users (127/434, 29.3% cancer survivors; 305/434, 70.3% family members and friends; and 2/434, 0.5% Twitter users were excluded). Of them, 51.4% (223/434) were female and approximately one-third were male. About one-fifth were people of color, whereas most of them were White. Approximately one-fifth (85/434, 19.6%) engaged with the outreach messages (cancer survivors: 33/85, 38% and family members and friends: 52/85, 61%). Of those who engaged with the messages, one-fourth were male, the majority were female, and approximately one-fifth were people of color, whereas the majority were White. Approximately 12% (10/85) of the contacted users requested more information and 40% (4/10) set up a prescreening. Two eligible candidates were transferred to USC Norris for further screening, but neither was enrolled. CONCLUSIONS: Our findings demonstrate the potential of identifying and engaging cancer survivors and their family members and friends on Twitter. Optimization of downstream recruitment efforts such as screening for digital populations on social media may be required. Future research could test the feasibility of the approach for other diseases, locations, languages, social media platforms, and types of research involvement (eg, survey research). Computer science methods could help to scale up the analysis of larger data sets to support more rigorous testing of the intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT03408561; https://clinicaltrials.gov/ct2/show/NCT03408561.

The migraine landscape on YouTube: A review of YouTube as a source of information on migraine.

INTRODUCTION: YouTube is the most widely used video hosting website in the world; however, the quality and reliability of information is unknown. The aim of this study is to evaluate the content and distribution of the most popular videos on YouTube about migraine. METHODS: We searched for migraine-related videos on the online video hosting resource YouTube (http://youtube.com/). Two authors screened the titles and video descriptions independently for all videos with a view count of ≥ 10,000 views. For each video we recorded descriptive data, the source/author and the primary purpose/content. RESULTS: We identified 351 eligible videos. In total, there was more than 3 days of content viewed more than 163 million times. Only 9% of these videos were authored by healthcare professionals. The majority (44%) of videos focused on complementary and alternative medicine. DISCUSSION: YouTube provides a wide array of easily accessible information on migraine, ranging from authoritative sources to potentially questionable content. If used uncritically, this may result in inadequate clinical management. Peer-reviewed information on migraine mechanisms and treatment is needed to provide the best available evidence for the public and patients. Ideally, a professional society or foundation such as the International Headache Society would develop, curate, and distribute content.

Misinformation about spinal manipulation and boosting immunity: an analysis of Twitter activity during the COVID-19 crisis.

BACKGROUND: Social media has become an increasingly important tool in monitoring the onset and spread of infectious diseases globally as well monitoring the spread of information about those diseases. This includes the spread of misinformation, which has been documented within the context of the emerging COVID-19 crisis. Understanding the creation, spread and uptake of social media misinformation is of critical importance to public safety. In this descriptive study, we detail Twitter activity regarding spinal manipulative therapy (SMT) and claims it increases, or "boosts", immunity. Spinal manipulation is a common intervention used by many health professions, most commonly by chiropractors. There is no clinical evidence that SMT improves human immunity. METHODS: Social media searching software (Talkwalker Quick Search) was used to describe Twitter activity regarding SMT and improving or boosting immunity. Searches were performed for the 3 months and 12 months before March 31, 2020 using terms related to 1) SMT, 2) the professions that most often provide SMT and 3) immunity. From these searches, we determined the magnitude and time course of Twitter activity then coded this activity into content that promoted or refuted a SMT/immunity link. Content themes, high-influence users and user demographics were then stratified as either promoting or refuting this linkage. RESULTS: Twitter misinformation regarding a SMT/immunity link increased dramatically during the onset of the COVID crisis. Activity levels (number of tweets) and engagement scores (likes + retweets) were roughly equal between content promoting or refuting a SMT/immunity link, however, the potential reach (audience) of tweets refuting a SMT/immunity link was 3 times higher than those promoting a link. Users with the greatest influence on Twitter, as either promoters or refuters, were individuals, not institutions or organizations. The majority of tweets promoting a SMT/immunity link were generated in the USA while the majority of refuting tweets originated from Canada. CONCLUSION: Twitter activity about SMT and immunity increased during the COVID-19 crisis. Results from this work have the potential to help policy makers and others understand the impact of SMT misinformation and devise strategies to mitigate its impact.

A Twitter campaign against pseudoscience: The sceptical discourse on complementary therapies in Spain.

The main objective of this article is to analyse the sceptical movement's discourse on complementary therapies in Spain, as well as comprehend its mobilisation against these therapies. Over the past 2 years, the Spanish sceptical movement, constituted by citizen's associations against unconventional therapies and in favour of evidence-based medicine, has increased its activism which, as a result, is now more familiar to the public. To perform this study, three sources of information were selected: (a) the #StopPseudociencias campaign, with a corpus of 6252 tweets; (b) 153 news articles published during the study timeline and (c) 7 interviews with members of the sceptical movement, journalists and politicians. The results of the content and discourse analyses have shown that the sceptical movement occupies a dominant discursive position on Twitter, while the perspective is more balanced in digital dailies.

Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis.

BACKGROUND: Health-related social media data are increasingly used in disease-surveillance studies, which have demonstrated moderately high correlations between the number of social media posts and the number of patients. However, there is a need to understand the causal relationship between the behavior of social media users and the actual number of patients in order to increase the credibility of disease surveillance based on social media data. OBJECTIVE: This study aimed to clarify the causal relationships among pollen count, the posting behavior of social media users, and the number of patients with seasonal allergic rhinitis in the real world. METHODS: This analysis was conducted using datasets of pollen counts, tweet numbers, and numbers of patients with seasonal allergic rhinitis from Kanagawa Prefecture, Japan. We examined daily pollen counts for Japanese cedar (the major cause of seasonal allergic rhinitis in Japan) and hinoki cypress (which commonly complicates seasonal allergic rhinitis) from February 1 to May 31, 2017. The daily numbers of tweets that included the keyword "kafunsho" (or seasonal allergic rhinitis) were calculated between January 1 and May 31, 2017. Daily numbers of patients with seasonal allergic rhinitis from January 1 to May 31, 2017, were obtained from three healthcare institutes that participated in the study. The Granger causality test was used to examine the causal relationships among pollen count, tweet numbers, and the number of patients with seasonal allergic rhinitis from February to May 2017. To determine if time-variant factors affect these causal relationships, we analyzed the main seasonal allergic rhinitis phase (February to April) when Japanese cedar trees actively produce and release pollen. RESULTS: Increases in pollen count were found to increase the number of tweets during the overall study period (P=.04), but not the main seasonal allergic rhinitis phase (P=.05). In contrast, increases in pollen count were found to increase patient numbers in both the study period (P=.04) and the main seasonal allergic rhinitis phase (P=.01). Increases in the number of tweets increased the patient numbers during the main seasonal allergic rhinitis phase (P=.02), but not the overall study period (P=.89). Patient numbers did not affect the number of tweets in both the overall study period (P=.24) and the main seasonal allergic rhinitis phase (P=.47). CONCLUSIONS: Understanding the causal relationships among pollen counts, tweet numbers, and numbers of patients with seasonal allergic rhinitis is an important step to increasing the credibility of surveillance systems that use social media data. Further in-depth studies are needed to identify the determinants of social media posts described in this exploratory analysis.

Nursing in an age of multimorbidity.

BACKGROUND: A changing sociodemographic landscape has seen rising numbers of people with two or more long-term health conditions. Multimorbidity presents numerous challenges for patients and families and those who work in healthcare services. Therefore, the nursing profession needs to understand the issues involved in supporting people with multiple chronic conditions and how to prepare the future workforce to care for them. METHODS: A descriptive, exploratory study was used to examine the future of nursing in an age of multimorbidity. An hour-long Twitter chat was organised and run by the Florence Nightingale Foundation Chairs of Clinical Nursing Practice Research to discuss this important area of practice and identify what needs to be done to adequately upskill and prepare the nursing profession to care for individuals with more than one long-term illness. Questions were formulated in advance to provide some structure to the online discussion. Data were collected and analysed from the social media platform using NVivo and an analytics tool called Keyhole. Descriptive statistics were used to describe participants and thematic analysis aided the identification of key themes. RESULTS: Twenty-four people, from a range of nursing backgrounds and organisations, took part in the social media discussion. Five themes encompassing coping with treatment burden, delivering holistic care, developing an evidence base, stimulating learning and redesigning health services were seen as key to ensuring nurses could care for people with multimorbidity and prevent others from developing chronic health conditions. CONCLUSIONS: Multimorbidity is a pressing health issue in today's society. Changes in nursing research, education and practice are required to help the profession work collaboratively with patients, families and multidisciplinary teams to better manage and prevent chronic illness now and in the future.

Monitoring Twitter Conversations for Targeted Recruitment in Cancer Trials in Los Angeles County: Protocol for a Mixed-Methods Pilot Study.

BACKGROUND: Insufficient recruitment of participants remains a critical roadblock to successful clinical research, particularly clinical trials. Social media provide new ways for connecting potential participants with research opportunities. Researchers suggest that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues and increasing enrollment in cancer clinical trials. However, there is a lack of evidence that Twitter offers practical utility and impact. OBJECTIVE: This pilot study aimed to examine the feasibility and impact of using Twitter monitoring data (ie, user activity and their conversations about cancer-related conditions and concerns expressed by Twitter users in Los Angeles County) as a tool for enhancing clinical trial recruitment at a comprehensive cancer center. METHODS: We will conduct a mixed-methods interrupted time series study design with a before-and-after social media recruitment intervention. On the basis of a preliminary analysis of eligible trials, we plan to onboard at least 84 clinical trials across 6 disease categories: breast cancer, colon cancer, kidney cancer, lymphoma, non-small cell lung cancer, and prostate cancer that are open to accrual at the University of Southern California (USC) Norris Comprehensive Cancer Center. We will monitor messages about these 6 cancer conditions posted by Twitter users in Los Angeles County. Recruitment for the trials will occur through the Twitter account (@USCTrials). Primary study outcomes-feasibility and acceptance of the social media intervention among targeted Twitter users and the study teams of the onboarded trials-will be assessed using qualitative interviews and the 4-point Likert scale and by calculating the proportion of targeted Twitter users who engaged with outreach messages. Second, impact of the social media intervention will be measured by calculating the proportion of enrollees in trials. The enrollment rate will be compared between the active intervention period and the prior 10 months as historical control for each disease trial group. This study has been funded by the National Center for Advancing Translational Science through a Clinical and Translational Science Award. Study approval was obtained from the clinical investigations committee at USC Norris and the institutional review board at USC. RESULTS: Recruitment on Twitter started in February 2018. Data collection will be completed in November 2018. CONCLUSIONS: This pilot project will provide preliminary data and practical insight into the application of publicly available Twitter data to identify and recruit clinical trial participants across 6 cancer disease types. We will shed light on the acceptance of the social media intervention among Twitter users and study team members of the onboarded trials. If successful, the findings will inform a multisite randomized controlled trial to determine the efficacy of the social media intervention across different locations and populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT03408561; https://clinicaltrials.gov/ct2/show/NCT03408561 (Archived by WebCite at http://www.webcitation.org/72LihauzW). REGISTERED REPORT IDENTIFIER: RR1-10.2196/9762.