Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis.

BACKGROUND: During the COVID-19 pandemic, Twitter (recently rebranded as "X") was the most widely used social media platform with over 2 million cancer-related tweets. The increasing use of social media among patients and family members, providers, and organizations has allowed for novel methods of studying cancer communication. OBJECTIVE: This study aimed to examine pediatric cancer-related tweets to capture the experiences of patients and survivors of cancer, their caregivers, medical providers, and other stakeholders. We assessed the public sentiment and content of tweets related to pediatric cancer over a time period representative of the COVID-19 pandemic. METHODS: All English-language tweets related to pediatric cancer posted from December 11, 2019, to May 7, 2022, globally, were obtained using the Twitter application programming interface. Sentiment analyses were computed based on Bing, AFINN, and NRC lexicons. We conducted a supplemental nonlexicon-based sentiment analysis with ChatGPT (version 3.0) to validate our findings with a random subset of 150 tweets. We conducted a qualitative content analysis to manually code the content of a random subset of 800 tweets. RESULTS: A total of 161,135 unique tweets related to pediatric cancer were identified. Sentiment analyses showed that there were more positive words than negative words. Via the Bing lexicon, the most common positive words were support, love, amazing, heaven, and happy, and the most common negative words were grief, risk, hard, abuse, and miss. Via the NRC lexicon, most tweets were categorized under sentiment types of positive, trust, and joy. Overall positive sentiment was consistent across lexicons and confirmed with supplemental ChatGPT (version 3.0) analysis. Percent agreement between raters for qualitative coding was 91%, and the top 10 codes were awareness, personal experiences, research, caregiver experiences, patient experiences, policy and the law, treatment, end of life, pharmaceuticals and drugs, and survivorship. Qualitative content analysis showed that Twitter users commonly used the social media platform to promote public awareness of pediatric cancer and to share personal experiences with pediatric cancer from the perspective of patients or survivors and their caregivers. Twitter was frequently used for health knowledge dissemination of research findings and federal policies that support treatment and affordable medical care. CONCLUSIONS: Twitter may serve as an effective means for researchers to examine pediatric cancer communication and public sentiment around the globe. Despite the public mental health crisis during the COVID-19 pandemic, overall sentiments of pediatric cancer-related tweets were positive. Content of pediatric cancer tweets focused on health and treatment information, social support, and raising awareness of pediatric cancer.

Social Network Analysis of e-Cigarette-Related Social Media Influencers on Twitter/X: Observational Study.

BACKGROUND: An e-cigarette uses a battery to heat a liquid that generates an aerosol for consumers to inhale. e-Cigarette use (vaping) has been associated with respiratory disease, cardiovascular disease, and cognitive functions. Recently, vaping has become increasingly popular, especially among youth and young adults. OBJECTIVE: The aim of this study was to understand the social networks of Twitter (now rebranded as X) influencers related to e-cigarettes through social network analysis. METHODS: Through the Twitter streaming application programming interface, we identified 3,617,766 unique Twitter accounts posting e-cigarette-related tweets from May 3, 2021, to June 10, 2022. Among these, we identified 33 e-cigarette influencers. The followers of these influencers were grouped according to whether or not they post about e-cigarettes themselves; specifically, the former group was defined as having posted at least five e-cigarette-related tweets in the past year, whereas the latter group was defined as followers that had not posted any e-cigarette-related tweets in the past 3 years. We randomly sampled 100 user accounts among each group of e-cigarette influencer followers and created corresponding social networks for each e-cigarette influencer. We compared various network measures (eg, clustering coefficient) between the networks of the two follower groups. RESULTS: Major topics from e-cigarette-related tweets posted by the 33 e-cigarette influencers included advocating against vaping policy (48.0%), vaping as a method to quit smoking (28.0%), and vaping product promotion (24.0%). The follower networks of these 33 influencers showed more connections for those who also post about e-cigarettes than for followers who do not post about e-cigarettes, with significantly higher clustering coefficients for the former group (0.398 vs 0.098; P=.005). Further, networks of followers who post about e-cigarettes exhibited substantially more incoming and outgoing connections than those of followers who do not post about e-cigarettes, with significantly higher in-degree (0.273 vs 0.084; P=.02), closeness (0.452 vs 0.137; P=.04), betweenness (0.036 vs 0.008; P=.001), and out-of-degree (0.097 vs 0.014; P=.02) centrality values. The followers who post about e-cigarettes also had a significantly (P<.001) higher number of followers (n=322) than that of followers who do not post about e-cigarettes (n=201). The number of tweets in the networks of followers who post about e-cigarettes was significantly higher than that in the networks of followers who do not post about e-cigarettes (93 vs 43; P<.001). Two major topics discussed in the networks of followers who post about e-cigarettes included promoting e-cigarette products or vaping activity (55.7%) and vaping being a help for smoking cessation and harm reduction (44.3%). CONCLUSIONS: Followers of e-cigarette influencers who also post about e-cigarettes have more closely connected networks than those of followers who do not themselves post about e-cigarettes. These findings provide a potentially practical intervention approach for future antivaping campaigns.

A Content Analysis of Indoor Tanning Twitter Chatter During COVID-19 Shutdowns: Cross-Sectional Qualitative Study.

BACKGROUND: Indoor tanning is a preventable risk factor for skin cancer. Statewide shutdowns during the COVID-19 pandemic resulted in temporary closures of tanning businesses. Little is known about how tanners reacted to losing access to tanning businesses. OBJECTIVE: This study aimed to analyze Twitter (subsequently rebranded as X) chatter about indoor tanning during the statewide pandemic shutdowns. METHODS: We collected tweets from March 15 to April 30, 2020, and performed a directed content analysis of a random sample of 20% (1165/5811) of tweets from each week. The 2 coders independently rated themes (κ=0.67-1.0; 94%-100% agreement). RESULTS: About half (589/1165, 50.6%) of tweets were by people unlikely to indoor tan, and most of these mocked tanners or the act of tanning (562/589, 94.9%). A total of 34% (402/1165) of tweets were posted by users likely to indoor tan, and most of these (260/402, 64.7%) mentioned missing tanning beds, often citing appearance- or mood-related reasons or withdrawal. Some tweets by tanners expressed a desire to purchase or use home tanning beds (90/402, 22%), while only 3.9% (16/402) mentioned tanning alternatives (eg, self-tanner). Very few tweets (29/1165, 2.5%) were public health messages about the dangers of indoor tanning. CONCLUSIONS: Findings revealed that during statewide shutdowns, half of the tweets about indoor tanning were mocking tanning bed users and the tanned look, while about one-third were indoor tanners reacting to their inability to access tanning beds. Future work is needed to understand emerging trends in tanning post pandemic.

#OtoTwitter: The Top 75 Twitter Influencers in Otolaryngology and Association With Academic Impact.

OBJECTIVES: Social media has enabled discussion of relevant topics within otolaryngology. With increasing academic discourse occurring on virtual platforms, it is important to examine who is influencing these discussions. This study thus aims to: (1) identify the top Twitter influencers in otolaryngology and (2) assess the relationship between Twitter influence and academic impact. STUDY DESIGN: Cross-sectional analysis. SETTING: Twitter. METHODS: The Right Relevance program was used to identify and rank the top 75 Twitter influencers, excluding organizations, according to the search terms "otolaryngology," "head and neck surgery," "ear nose throat," "rhinology," "head and neck," "laryngology," "facial plastics," and "otology." Demographic data and h-index were collected for each influencer. Correlational analyzes were performed to assess the relationships between Twitter rank and geographic location, sex, subspecialty, and h-index. RESULTS: The majority of the top 75 influencers were otolaryngologists (87%), female (68%), and located in the United States (61%). General otolaryngology (n = 20, 31%) was more well-represented than any individual subspecialty including facial plastics (n = 10, 15%), rhinology (n = 10, 15%), and neurotology (n = 9, 14%). There was a significant relationship between Twitter rank and h-index (Spearman ρ value of -0.32; 95% confidence interval: -0.51 to -0.01; P = .006). Twitter rank was not significantly correlated with subspecialty, sex, or geographic location (P > .05). CONCLUSION: The majority of Twitter influencers within otolaryngology were otolaryngologists, female, and located in the United States. Social media influence is positively associated with academic impact among otolaryngologists.

A quantitative analysis of Twitter ("X") trends in the discussion of rhinoplasty.

Introduction: Rhinoplasty is one of the most common cosmetic surgical procedures performed globally. Twitter, also known as "X," is used by both patients and physicians and has been studied as a useful tool for analyzing trends in healthcare. The public social media discourse of rhinoplasty has not been previously reported in the field of otolaryngology. The goal of this study was to characterize the most common user type, sentiment, and temporal trends in the discussion of rhinoplasty on Twitter to guide facial plastic surgeons in their clinical and social media practices. Methods: A total of 1,427,015 tweets published from 2015 to 2020 containing the keywords "rhinoplasty" or "nose job" were extracted using Twitter Academic API. Tweets were standardized and filtered for spam and duplication. Natural language processing (NLP) algorithms and data visualization techniques were applied to characterize tweets. Results: Significantly more "nose job" tweets (80.8%) were published compared with "rhinoplasty" (19.2%). Annual tweet frequency increased over the 5 years, with "rhinoplasty" tweets peaking in January and "nose job" tweets peaking in the summer and winter months. Most "rhinoplasty" tweets were linked to a surgeon or medical practice source, while most "nose job" tweets were from isolated laypersons. While discussion was positive in sentiment overall (M = +0.08), "nose job" tweets had lower average sentiment scores (P < .001) and over twice the proportion of negative tweets. The top 20 most prolific accounts contributed to 14,758 (10.6%) of total "rhinoplasty" tweets. Exactly 90% (18/20) of those accounts linked to non-academic surgeons compared with 10% (2/20) linked to academic surgeons. Conclusions: Rhinoplasty-related posts on Twitter were cumulatively positive in sentiment and tweet volume is steadily increasing over time, especially during popular holiday months. The search term "nose job" yields significantly more results than "rhinoplasty," and is the preferred term of non-healthcare users. We found a large digital contribution from surgeons and medical practices, particularly in the non-academic and private practice sector, utilizing Twitter for promotional purposes.

Assessing the public discourse on Twitter: Reactions to the JUUL e-cigarettes ban in the United States.

INTRODUCTION: JUUL is a high-nicotine pod-based vaping device that is popular among adolescents and young adults. On 23 June 2022, the US Food and Drug Administration (FDA) denied authorization to market JUUL, and ordered JUUL Labs to remove products from the US market. The next day, a US federal appeals court temporarily suspended the ban. The mixed public discourse surrounding the FDA ban warrants further investigation. METHODS: This study examined Twitter data to describe public reaction to these announcements. Posts containing terms 'JUUL' and/or '#JUUL' (N=97548 unique tweets) were collected from 23 June to 3 July 2022, from Twitter's Streaming Application Programming Interface (API). After removing retweets, we used an inductive approach to become familiar with the data, generated a codebook, and conducted a content analysis on a random sample of n=4000 tweets. RESULTS: A total of 2755 (68.9%) tweets discussed JUUL in the context of the FDA ban. News (n=1425/2755; 51.7%) about the JUUL ban, government distrust (n=588; 21.3%), and individual rights (n=253; 9.2%) were the most prevalent themes. Less commonly discussed themes included inconsistencies between policies (n=174; 6.3%), mentions of switching to other products (n=162; 5.9%), smoking cessation (n=99; 3.6%), and craving for JUUL (n=94; 3.4%). Sentiment analysis of JUUL ban-related posts (n=2755) demonstrated that 1989 (72.2%) tweets were categorized as neutral, while anti-ban posts (n=566; 20.5%) were more prevalent than pro-ban posts (n=200; 7.3%). CONCLUSIONS: Besides straightforward announcements of the JUUL ban and its suspension, Twitter posts discussed government distrust, individual rights, and policy inconsistencies. While most posts conveyed neutral sentiments, anti-ban posts were almost three times more prevalent than pro-ban posts. Our findings suggest that text-based social media platforms like Twitter may be an effective instrument to understand opinions, attitudes, and beliefs regarding the FDA's JUUL ban.

Oral Cancer and Twitter: An Analysis of Oral Cancer Awareness Month Tweets.

OBJECTIVE: The objective of this research was to assess Twitter usage during Oral Cancer Awareness Month and explore the content and engagement related to oral cancer. METHODS: A comprehensive search was performed using relevant hashtags and keywords related to oral cancer on Twitter throughout the oral cancer awareness month, April 2022. All extracted tweets that match the inclusion criteria were analyzed for content, users were classified, and their countries were identified. RESULT: A total of 5551 English tweets were identified during Oral Cancer Awareness Month, and 5543 were included in the analysis covering a wide range of oral cancer-related topics. The analyzed tweets encompassed a diverse range of topics, from cancer and oral health to oncology, cancer research, cancer awareness, and even discussions related to alcohol. We found that the majority of users who post on Twitter were individuals. The most common tweets were posted from the USA. CONCLUSIONS: This study provides an analysis of Twitter activity during Oral Cancer Awareness Month, highlighting the diverse range of content being shared, offering valuable insights. The findings demonstrate the importance of leveraging social media platforms to disseminate information and raise awareness. With a strategic approach to social media, organizations and individuals worldwide have the power to amplify their message, attract attention, and effectively advocate for oral cancer awareness.

Why don't we follow influencers in obstetrics and gynaecology on Twitter to obtain information on the most recent international congresses?

We proposed following influencers in obstetrics and gynecology on Twitter. We believe that the use of Twitter may help obstetrics and gynecology doctors in low-income countries as well as stimulate fellows and residents by following actual discussions at international conferences.

Proper Tweeting Etiquette Guidelines for Pathologists, Trainees, and Medical Students on #PathTwitter.

The use of social media in pathology has broadly had a positive impact on pathology education and outreach with the frequent posting of high-quality educational material of potential value to trainees, practicing pathologists, and other clinical and laboratory specialists. These posts are also of potential utility and interest to members of the public, who are now more than ever able to gain a window into the field and the role of pathologists in their medical care. There can be a lighthearted aspect to teaching material with the use of food items/analogies, emojis, or other descriptors, which may cross over into the classroom. However, when pathology discussion is taken to a public forum, such as on Twitter (parent company: X Corp.), there is the potential for posted material to be misunderstood, such as when certain emojis or adjectives may be used to describe a human disease state or patient sample. The authors present examples of potential areas of caution, suggestions of how to create a positive impact, and brief guidelines for social media etiquette on #PathTwitter that may apply to other social media platforms widely used by pathologists (including, but not limited to, Facebook, Instagram, YouTube, and KiKo). While the points discussed here may be common knowledge and well-known to pathologists who use social media for virtual medical education, the concerns mentioned here (such as using language like "beautiful" to describe abnormal mitotic figures and cancer cells) still exist and, henceforth, bear reinforcing.

The Readability of Patient-Facing Social Media Posts on Common Otolaryngologic Diagnoses.

OBJECTIVE: To assess the readability of patient-facing educational information about the most common otolaryngology diagnoses on popular social media platforms. STUDY DESIGN: Cross-sectional study. SETTING: Social media platforms. METHODS: The top 5 otolaryngologic diagnoses were identified from the National Ambulatory Medical Care Survey Database. Facebook, Twitter, TikTok, and Instagram were searched using these terms, and the top 25 patient-facing posts from unique accounts for each search term and poster type (otolaryngologist, other medical professional, layperson) were identified. Captions, text, and audio from images and video, and linked articles were extracted. The readability of each post element was calculated with multiple readability formulae. Readability was summarized and was compared between poster types, platforms, and search terms via Kruskal-Wallis testing. RESULTS: Median readability, by grade level, by grade level, was greater than 10 for captions, 5 for image-associated text, and 9 for linked articles. Captions and images in posts by laypeople were significantly more readable than captions by otolaryngologists or other medical professionals, but there was no difference for linked articles. All post components were more readable in posts about cerumen than those about other search terms. CONCLUSIONS: When examining the readability of posts on social media regarding the most common otolaryngology diagnoses, we found that many posts are less readable than recommended for patients, and found that posts by laypeople were significantly more readable than those by medical professionals. Medical professionals should work to make educational social media posts more readable to facilitate patient comprehension.

Twitter Misinformation Discourses About Vaping: Systematic Content Analysis.

BACKGROUND: While there has been substantial analysis of social media content deemed to spread misinformation about electronic nicotine delivery systems use, the strategic use of misinformation accusations to undermine opposing views has received limited attention. OBJECTIVE: This study aims to fill this gap by analyzing how social media users discuss the topic of misinformation related to electronic nicotine delivery systems, notably vaping products. Additionally, this study identifies and analyzes the actors commonly blamed for spreading such misinformation and how these claims support both the provaping and antivaping narratives. METHODS: Using Twitter's (subsequently rebranded as X) academic application programming interface, we collected tweets referencing #vape and #vaping and keywords associated with fake news and misinformation. This study uses systematic content analysis to analyze the tweets and identify common themes and actors who discuss or possibly spread misinformation. RESULTS: This study found that provape users dominate the platform regarding discussions about misinformation about vaping, with provaping tweets being more frequent and having higher overall user engagement. The most common narrative for provape tweets surrounds the conversation of vaping being perceived as safe. On the other hand, the most common topic from the antivape narrative is that vaping is indeed harmful. This study also points to a general distrust in authority figures, with news outlets, public health authorities, and political actors regularly accused of spreading misinformation, with both placing blame. However, specific actors differ depending on their positionalities. The vast number of accusations from provaping advocates is found to shape what is considered misinformation and works to silence other narratives. Additionally, allegations against reliable and proven sources, such as public health authorities, work to discredit assessments about the health impacts, which is detrimental to public health overall for both provaping and antivaping advocates. CONCLUSIONS: We conclude that the spread of misinformation and the accusations of misinformation dissemination using terms such as "fact check," "misinformation," "fake news," and "disinformation" have become weaponized and co-opted by provaping actors to delegitimize criticisms about vaping and to increase confusion about the potential health risks. The study discusses the mixed types of impact of vaping on public health for both smokers and nonsmokers. Additionally, we discuss the implications for effective health education and communication about vaping and how misinformation claims can affect evidence-based discourse on Twitter as well as informed vaping decisions.

Public perceptions of the FDA's marketing authorization of Vuse on Twitter/X.

Introduction: On October 12, 2021, the FDA issued its first marketing granted orders for Vuse, the e-cigarette product by R.J. Reynolds Vapor Company. The public perceptions and reactions to the FDA's Vuse authorization are prevalent on social media platforms such as Twitter/X. We aim to understand public perceptions of the FDA's Vuse authorization in the US using Twitter/X data. Methods: Through the Twitter/X streaming API (Application Programming Interface), 3,852 tweets between October 12, 2021, and October 23, 2021, were downloaded using the keyword of Vuse. With the elimination of retweets, irrelevant tweets, and tweets from other countries, the final dataset consisted of 523 relevant tweets from the US. Based on their attitudes toward the FDA authorization on Vuse, these tweets were coded into three major categories: positive, negative, and neutral. These tweets were further manually classified into different categories based on their contents. Results: There was a large peak on Twitter/X mentioning FDA's Vuse authorization on October 13, 2021, just after the authorization was announced. Of the 523 US tweets related to FDA's Vuse authorization, 6.12% (n=32) were positive, 26.77% (n=140) were negative, and 67.11% (n=351) were neutral. In positive tweets, the dominant subcategory was Cessation Claims (n=18, 56.25%). In negative tweets, the topics Health Risk (n=43, 30.71%), Criticize Authorization (n=42, 30.00%), and Big Tobacco (n=40, 38.57%) were the major topics. News (n=271, 77.21%) was the most prevalent topic among neutral tweets. In addition, tweets with a positive attitude tend to have more likes. Discussion: Public perceptions and discussions on Twitter/X regarding the FDA's Vuse authorization in the US showed that Twitter/X users were more likely to show a negative than a positive attitude with a major concern about health risks.

Spanish-language tobacco-related posts on Twitter: content analysis.

INTRODUCTION: Twitter data has been used to surveil public sentiment about tobacco products, however, most tobacco-related Twitter research has been conducted with English-language posts. There is a gap in the literature on tobacco-related discussions on Twitter in languages other than English. This study summarized tobacco-related discussions in Spanish on Twitter. METHODS: A set of Spanish terms reflecting electronic cigarettes (e.g., cigarillos electrónicos), cigarettes (e.g., "pitillo"), and cigars (e.g., "cigaro") were identified. A content analysis of tweets (n=1,352) drawn from 2021 was performed to examine themes and sentiment. An initial codebook was developed in English then translated to Spanish and then translated back to English by a bilingual (Spanish and English) member of the research team. Two bilingual members of the research team coded the tweets into themes and sentiment. RESULTS: Themes in the tweets included 1) product promotion (n=168, 12.4%), 2) health warnings (n=161, 11.9%), 3) tobacco use (n=136, 10.1%), 4) health benefits of vaping (n=58, 4.3%), 5) cannabis use (n=50, 3.7%), 6) cessation (n=47, 3.5%), 7) addiction (n=33, 2.4%), 8) policy (n=27, 2.0%), and 9) polysubstance use (n=12, 0.9%). Neutral (n=955, 70.6%) was the most common category of sentiment observed in the data. CONCLUSIONS: Tobacco products are discussed in multiple languages on Twitter and can be summarized by bilingual research teams. Future research should determine if Spanish-speaking individuals are frequently exposed to pro-tobacco content on social media and if such exposure increases susceptibility to use tobacco among never users or sustained use among current users. IMPLICATIONS: Spanish-language pro-tobacco content exists on Twitter, which has implications for Spanish-speaking individuals who may be exposed to this content. Spanish-language pro-tobacco-related posts may help normalize tobacco use among Spanish-speaking populations. As a result, anti-tobacco tweets in Spanish may be necessary to counter areas of the online environment that can be considered pro-tobacco.

Public perception of the resumption of HPV vaccine recommendation in Japan: Twitter content analysis.

On 12 November 2021, after a stagnation of 8.5 years, Japan decided to resume proactive recommendations for HPV vaccines. However, it is not known how the public reacted to the resumption of proactive recommendations for HPV vaccines, which are key elements in health communication that promote HPV vaccination. This study aimed to capture discussions on HPV vaccination on Twitter and to identify user characteristics, sentiments, discussed themes and their potential reach before and after 12 November 2021, when the Ministry of Health, Labour, and Welfare's decision to resume proactive recommendation for HPV vaccines was first reported by the media in Japan. This study conducted a content analysis of tweets related to HPV vaccines posted on 11 November and 12 November 2021. Tweets were coded in terms of user characteristics, sentiments (i.e. positive, negative and neutral) and themes. Reach was measured by the number of retweets, likes and followers. A total of 3623 tweets were identified. The results showed that approximately 50% of health professionals and researchers tweeted neutral content about HPV vaccines. The most frequently discussed theme was the safety and side effects of HPV vaccines, which was accompanied by mainly negative sentiments. Although health professionals and researchers are influential on Twitter, half of them tweeted neutrally about HPV vaccines. Influential professionals, such as health professionals and researchers, are expected to disseminate accurate information to correct misinformation and recommend HPV vaccination on Twitter to overcome the HPV vaccination crisis, which is characterized by the low vaccination rates.

Social media and surgery.

During the last years, social media use has increased in the surgical community. Social Media in surgery has created new challenges such as surgical education, patient privacy, professionalism, and the difference between the private and public virtual life. Facebook, YouTube or WebSurg are some of the main social media in the surgical field. Nevertheless, Twitter is the most common and relevant Media for surgeons. Some Twitter Hashtag such as #SoMe4Surgery or #colorectalsurgery went viral and had a significant influence in the surgical community. Some of the uses of social media in surgery are education of younger surgeons, surgical research, and relationship between surgeons. However, not everything in social media is positive. Some negative issues of social media use in surgery are, for example, lack of privacy, intellectual property conflicts, conflicts of interest and mistakes in the published information. In this article, the main social media, the use of these media, the advantages and the possible risks and negative issues of social media are discussed.

Public Health Surveillance of Behavioral Cancer Risk Factors During the COVID-19 Pandemic: Sentiment and Emotion Analysis of Twitter Data.

BACKGROUND: The COVID-19 pandemic and its associated public health mitigation strategies have dramatically changed patterns of daily life activities worldwide, resulting in unintentional consequences on behavioral risk factors, including smoking, alcohol consumption, poor nutrition, and physical inactivity. The infodemic of social media data may provide novel opportunities for evaluating changes related to behavioral risk factors during the pandemic. OBJECTIVE: We explored the feasibility of conducting a sentiment and emotion analysis using Twitter data to evaluate behavioral cancer risk factors (physical inactivity, poor nutrition, alcohol consumption, and smoking) over time during the first year of the COVID-19 pandemic. METHODS: Tweets during 2020 relating to the COVID-19 pandemic and the 4 cancer risk factors were extracted from the George Washington University Libraries Dataverse. Tweets were defined and filtered using keywords to create 4 data sets. We trained and tested a machine learning classifier using a prelabeled Twitter data set. This was applied to determine the sentiment (positive, negative, or neutral) of each tweet. A natural language processing package was used to identify the emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) based on the words contained in the tweets. Sentiments and emotions for each of the risk factors were evaluated over time and analyzed to identify keywords that emerged. RESULTS: The sentiment analysis revealed that 56.69% (51,479/90,813) of the tweets about physical activity were positive, 16.4% (14,893/90,813) were negative, and 26.91% (24,441/90,813) were neutral. Similar patterns were observed for nutrition, where 55.44% (27,939/50,396), 15.78% (7950/50,396), and 28.79% (14,507/50,396) of the tweets were positive, negative, and neutral, respectively. For alcohol, the proportions of positive, negative, and neutral tweets were 46.85% (34,897/74,484), 22.9% (17,056/74,484), and 30.25% (22,531/74,484), respectively, and for smoking, they were 41.2% (11,628/28,220), 24.23% (6839/28,220), and 34.56% (9753/28,220), respectively. The sentiments were relatively stable over time. The emotion analysis suggests that the most common emotion expressed across physical activity and nutrition tweets was trust (69,495/320,741, 21.67% and 42,324/176,564, 23.97%, respectively); for alcohol, it was joy (49,147/273,128, 17.99%); and for smoking, it was fear (23,066/110,256, 20.92%). The emotions expressed remained relatively constant over the observed period. An analysis of the most frequent words tweeted revealed further insights into common themes expressed in relation to some of the risk factors and possible sources of bias. CONCLUSIONS: This analysis provided insight into behavioral cancer risk factors as expressed on Twitter during the first year of the COVID-19 pandemic. It was feasible to extract tweets relating to all 4 risk factors, and most tweets had a positive sentiment with varied emotions across the different data sets. Although these results can play a role in promoting public health, a deeper dive via qualitative analysis can be conducted to provide a contextual examination of each tweet.

The impact of a dedicated social media strategy on enhancing surgical education.

PURPOSE: Social media (SoMe) is increasingly important in surgical education and may be necessary in the current learning environment. Whilst expanding in use and applications, few studies detail the impact of SoMe on measurable outcomes. The goal of this study was to quantify the impact of a dedicated SoMe strategy on engagement metrics for surgical research. METHODS: A retrospective review of a peer-reviewed surgical journal's Twitter microblog platform (@ColorectalDis) was performed from 6/2015 to 4/2021. A formal SoMe strategy was introduced in September 2018. Data were stratified into 2 time periods: pre-intervention (6-2015 to 9-2018) and post-SoMe intervention (9-2018 to 4-2021). The main outcome was the impact of the SoMe strategy on user engagement with the Twitter platform, journal, and traditional journal metrics. Twitter Analytics and Twitonomy were used to analyse engagement. RESULTS: From conception to analysis, the microblog published 1198 original tweets, generating 5 million impressions and 231,000 engagements. Increased account activity (increased tweets published per month-5.51 vs 28.79; p < 0.01) was associated with significant engagement growth, including new monthly followers (213 vs 38; p < 0.01) and interactions with posted articles (4,096,167 vs 269,152; p < 0.01). Article downloads increased twenty-fold post-SoMe intervention (210,449 vs 10,934; p < 0.01), with significant increases in traditional journal metrics of new subscribers (+11%), article submissions (+24%), and impact factor (+0.9) (all p < 0.01). CONCLUSION: SoMe directly impacts traditional journal metrics in surgical research. By examining the patterns of user engagement between SoMe and journal sites, the growing beneficial impact of a structured social media strategy and SoMe as an educational tool is demonstrated.

Microvascular Decompression and Trigeminal Neuralgia: Patient Sentiment Analysis Using Natural Language Processing.

OBJECTIVE: Microvascular decompression (MVD) as a treatment for trigeminal neuralgia (TGN) has high success rate but is associated with risks of complication. This study analyzes Twitter to provide insights into discussions surrounding MVD for patients with TGN. METHODS: A Twitter search performed in April 2022 yielded 491 tweets from 426 accounts. Tweets and accounts were classified thematically, and descriptive statistics were used for various social media metrics. Using a natural language processing machine learning algorithm, sentiment analysis (SA) was performed to evaluate patient perspectives before and after surgery, and a multivariate regression model was used to identify predictors of higher engagement metrics (likes, retweets, quote tweets, replies). RESULTS: Most accounts were patients, caregivers, and other members of the public (70%). The most encountered themes were research (47%) and personal experiences (33.4%). SA of tweets about patient experiences showed that 40.2% of tweets were positive, 31.1% were neutral and 28.7% were negative. Negative tweets decreased significantly in postoperative tweets and mostly discussed complications or failure of surgery (63%). On multivariate analysis, only inclusion of media (photo or video) in a Tweet was associated with higher engagement metrics. CONCLUSIONS: This study provides a comprehensive review of Twitter use discussing MVD in TGN and is the first to assess patient satisfaction after treatment using SA. The data presented on patient perspectives on social media could help physicians establish direct lines of communication with patients, fostering a more patient-focused care.

Fact-Checking Cancer Information on Social Media in Japan: Retrospective Study Using Twitter.

BACKGROUND: The widespread use of social media has made it easier for patients to access cancer information. However, a large amount of misinformation and harmful information that could negatively impact patients' decision-making is also disseminated on social media platforms. OBJECTIVE: We aimed to determine the actual amount of misinformation and harmful information as well as trends in the dissemination of cancer-related information on Twitter, a representative social media platform. Our findings can support decision-making among Japanese patients with cancer. METHODS: Using the Twitter app programming interface, we extracted tweets containing the term "cancer" in Japanese that were posted between August and September of 2022. The eligibility criteria were the cancer-related tweets with the following information: (1) reference to the occurrence or prognosis of cancer, (2) recommendation or nonrecommendation of actions, (3) reference to the course of cancer treatment or adverse events, (4) results of cancer research, and (5) other cancer-related knowledge and information. Finally, we selected the top 100 tweets with the highest number of "likes." For each tweet, 2 independent reviewers evaluated whether the information was factual or misinformation, and whether it was harmful or safe with the reasons for the decisions on the misinformation and harmful tweets. Additionally, we examined the frequency of information dissemination using the number of retweets for the top 100 tweets and investigated trends in the dissemination of information. RESULTS: The extracted tweets totaled 69,875. Of the top 100 cancer-related tweets with the most "likes" that met the eligibility criteria, 44 (44%) contained misinformation, 31 (31%) contained harmful information, and 30 (30%) contained both misinformation and harmful information. Misinformation was described as Unproven (29/94, 40.4%), Disproven (19/94, 20.2%), Inappropriate application (4/94, 4.3%), Strength of evidence mischaracterized (14/94, 14.9%), Misleading (18/94, 18%), and Other misinformation (1/94, 1.1%). Harmful action was described as Harmful action (9/59, 15.2%), Harmful inaction (43/59, 72.9%), Harmful interactions (3/59, 5.1%), Economic harm (3/59, 5.1%), and Other harmful information (1/59, 1.7%). Harmful information was liked more often than safe information (median 95, IQR 43-1919 vs 75.0 IQR 43-10,747; P=.04). The median number of retweets for the leading 100 tweets was 13.5 (IQR 0-2197). Misinformation was retweeted significantly more often than factual information (median 29.0, IQR 0-502 vs 7.5, IQR 0-2197; P=.01); harmful information was also retweeted significantly more often than safe information (median 35.0, IQR 0-502 vs 8.0, IQR 0-2197; P=.002). CONCLUSIONS: It is evident that there is a prevalence of misinformation and harmful information related to cancer on Twitter in Japan and it is crucial to increase health literacy and awareness regarding this issue. Furthermore, we believe that it is important for government agencies and health care professionals to continue providing accurate medical information to support patients and their families in making informed decisions.

Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling.

BACKGROUND: Social networks have become one of the main channels for obtaining health information. However, they have also become a source of health-related misinformation, which seriously threatens the public's physical and mental health. Governance of health-related misinformation can be implemented through topic identification of rumors on social networks. However, little attention has been paid to studying the types and routes of dissemination of health rumors on the internet, especially rumors regarding health-related information in Chinese social media. OBJECTIVE: This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends and to analyze the modeling results of the text by using the Latent Dirichlet Allocation model. METHODS: We used a web crawler tool to capture health rumor-dispelling articles on WeChat rumor-dispelling public accounts. We collected information from health-debunking articles posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model called Latent Dirichlet Allocation was used to identify and generalize the most common topics. The proportion distribution of the themes was calculated, and the negative impact of various health rumors in different periods was analyzed. Additionally, the prevalence of health rumors was analyzed by the number of health rumors generated at each time point. RESULTS: We collected 9366 rumor-refuting articles from January 1, 2016, to August 31, 2022, from WeChat official accounts. Through topic modeling, we divided the health rumors into 8 topics, that is, rumors on prevention and treatment of infectious diseases (1284/9366, 13.71%), disease therapy and its effects (1037/9366, 11.07%), food safety (1243/9366, 13.27%), cancer and its causes (946/9366, 10.10%), regimen and disease (1540/9366, 16.44%), transmission (914/9366, 9.76%), healthy diet (1068/9366, 11.40%), and nutrition and health (1334/9366, 14.24%). Furthermore, we summarized the 8 topics under 4 themes, that is, public health, disease, diet and health, and spread of rumors. CONCLUSIONS: Our study shows that topic modeling can provide analysis and insights into health rumor governance. The rumor development trends showed that most rumors were on public health, disease, and diet and health problems. Governments still need to implement relevant and comprehensive rumor management strategies based on the rumors prevalent in their countries and formulate appropriate policies. Apart from regulating the content disseminated on social media platforms, the national quality of health education should also be improved. Governance of social networks should be clearly implemented, as these rapidly developed platforms come with privacy issues. Both disseminators and receivers of information should ensure a realistic attitude and disseminate health information correctly. In addition, we recommend that sentiment analysis-related studies be conducted to verify the impact of health rumor-related topics.