2022
DOI: 10.2196/37924
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Twitter Trends for Celiac Disease and the Gluten-Free Diet: Cross-sectional Descriptive Analysis

Abstract: Background Few studies have systematically analyzed information regarding chronic medical conditions and available treatments on social media. Celiac disease (CD) is an exemplar of the need to investigate web-based educational sources. CD is an autoimmune condition wherein the ingestion of gluten causes intestinal damage and, if left untreated by a strict gluten-free diet (GFD), can result in significant nutritional deficiencies leading to cancer, bone disease, and death. Adherence to the GFD can b… Show more

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Cited by 3 publications
(2 citation statements)
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“…205 [254] Rising tides or rising stars? : Dynamics of shared attention on twitter during media events 206 [255] Misleading health-related information promoted through video-based social media: Anorexia on youtube 209 [258] Utilising online eye-tracking to discern the impacts of cultural backgrounds on fake and real news decision-making 210 [259] Top 100 #PCOS influencers: Understanding who, why and how online content for PCOS is influenced 211 [260] Twitter Trends for Celiac Disease and the Gluten-Free Diet: Cross-sectional Descriptive Analysis 214 [263] The influence of fake news on face-trait learning 215 [264] COVID-Related Misinformation Migration to BitChute and Odysee 216 [265] Sending News Back Home: Misinformation Lost in Transnational Social Networks 217 [266] Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic 218 [267] Organization and evolution of the UK far-right network on Telegram 219 [268] Predictive modeling for suspicious content identification on Twitter 220 [269] Detection and moderation of detrimental content on social media platforms: current status and future directions 221 [270] Cross-platform information spread during the January 6th capitol riots 222 [271] Combating multimodal fake news on social media: methods, datasets, and future perspective 223 [272] In. Tackling fake news in socially mediated public spheres: A comparison of Weibo and WeChat 249 [298] The Networked Context of COVID-19 Misinformation: Informational Homogeneity on YouTube at the Beginning of the Pandemic 250 [299] Twelve tips to make successful medical infographics 251 [300] TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets 252 [301] Cognitive and affective responses to political disinformation in Facebook 253 [302] Experience: Managing misinformation in social media-insights for policymakers from Twitter analytics 254 [303] Hepatitis E vaccine in China: Public health professional perspectives on vaccine promotion and strategies for control (Continued )…”
Section: Id Document Referencementioning
confidence: 99%
“…205 [254] Rising tides or rising stars? : Dynamics of shared attention on twitter during media events 206 [255] Misleading health-related information promoted through video-based social media: Anorexia on youtube 209 [258] Utilising online eye-tracking to discern the impacts of cultural backgrounds on fake and real news decision-making 210 [259] Top 100 #PCOS influencers: Understanding who, why and how online content for PCOS is influenced 211 [260] Twitter Trends for Celiac Disease and the Gluten-Free Diet: Cross-sectional Descriptive Analysis 214 [263] The influence of fake news on face-trait learning 215 [264] COVID-Related Misinformation Migration to BitChute and Odysee 216 [265] Sending News Back Home: Misinformation Lost in Transnational Social Networks 217 [266] Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic 218 [267] Organization and evolution of the UK far-right network on Telegram 219 [268] Predictive modeling for suspicious content identification on Twitter 220 [269] Detection and moderation of detrimental content on social media platforms: current status and future directions 221 [270] Cross-platform information spread during the January 6th capitol riots 222 [271] Combating multimodal fake news on social media: methods, datasets, and future perspective 223 [272] In. Tackling fake news in socially mediated public spheres: A comparison of Weibo and WeChat 249 [298] The Networked Context of COVID-19 Misinformation: Informational Homogeneity on YouTube at the Beginning of the Pandemic 250 [299] Twelve tips to make successful medical infographics 251 [300] TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets 252 [301] Cognitive and affective responses to political disinformation in Facebook 253 [302] Experience: Managing misinformation in social media-insights for policymakers from Twitter analytics 254 [303] Hepatitis E vaccine in China: Public health professional perspectives on vaccine promotion and strategies for control (Continued )…”
Section: Id Document Referencementioning
confidence: 99%
“…Increased media coverage of gluten-related disorders and endorsements by celebrities and influencers who claim to feel better on a gluten-free diet not only have helped raise awareness or popularize this dietary choice but also created a general belief that gluten is harmful or that removing gluten will automatically lead to improved health. [10]The food industry has responded to the growing demand for gluten-free options by expanding the availability of gluten-free products. Grocery stores, restaurants and cafes now offer a wider range of gluten-free alternatives, making it easier for individuals to adhere to a gluten-free diet.…”
Section: Introductionmentioning
confidence: 99%