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Understanding Loneliness Through Analysis of Twitter and Reddit Data: Comparative Study

Understanding Loneliness Through Analysis of Twitter and Reddit Data: Comparative Study

Twitter data analysis involved the following 3 stages: (1) data collection (tweet collection); (2) division of the collected data into negative and other tweets through preliminary analysis (sentiment analysis of tweets); and (3) further analysis of tweets with negative sentiments through manual coding to find relevant themes and categories (manual coding and analysis of tweets). Pipeline for processing Twitter data.

Hurmat Ali Shah, Mowafa Househ

Interact J Med Res 2025;14:e49464

Cross-Cultural Sense-Making of Global Health Crises: A Text Mining Study of Public Opinions on Social Media Related to the COVID-19 Pandemic in Developed and Developing Economies

Cross-Cultural Sense-Making of Global Health Crises: A Text Mining Study of Public Opinions on Social Media Related to the COVID-19 Pandemic in Developed and Developing Economies

Despite challenges like broad topics and tweet brevity, LDA proved suitable for capturing public discourse. The paper defines “sense-making” as how individuals interpret public health information, especially during crises. The topics derived through LDA reflect public interpretations of COVID-19, and manual labeling by experts ensured accurate insights into public responses. This research was covered by ethical approval gained from Newcastle University (ref: 1067/2020).

Adham Kahlawi, Firas Masri, Wasim Ahmed, Josep Vidal-Alaball

J Med Internet Res 2025;27:e58656

US State Public Health Agencies' Use of Twitter From 2012 to 2022: Observational Study

US State Public Health Agencies' Use of Twitter From 2012 to 2022: Observational Study

A study of health agency use of Twitter across 7 countries during the spring of 2020 found evidence of this trend internationally, with announcements and reporting being the most common tweet theme [12]. At a local level in the US, there have been experiments in using Twitter as a community service tool, such as in the case of the Chicago Department of Public Health’s foodborne illness response program [13].

Samuel R Mendez, Sebastian Munoz-Najar, Karen M Emmons, Kasisomayajula Viswanath

J Med Internet Res 2025;27:e59786

An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach

An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach

Example of a drug-related lyrics tweet by an internet influencer. This tweet references a lyric from Chief Keef’s song “Love No Thotties” and includes mention of drug use (“blunt”). Real-time X data was acquired using the X APIs through the X Crawler [33]. The dataset contains posts gathered from 2015 to 2017, spanning a total of 1.97 billion posts. These posts were organized into hourly batches and stored as text files.

Waylon Luo, Ruoming Jin, Deric Kenne, NhatHai Phan, Tang Tang

JMIR Form Res 2024;8:e49567

Navigating Awareness and Strategies to Support Dementia Advocacy on Social Media During World Alzheimer’s Month: Infodemiology Study

Navigating Awareness and Strategies to Support Dementia Advocacy on Social Media During World Alzheimer’s Month: Infodemiology Study

However, we did not anonymize tweet content regarding celebrities, politicians, and national dementia advocates. For example, a new report on ethics and social media research published at the University of Aberdeen notes that celebrities, politicians, and public figures seeking to share their messages as widely as possible do not require anonymity [38]. Accordingly, we did not anonymize posts about celebrity champions (celebrities or athletes), politicians, or nationally known dementia advocates.

Juanita-Dawne Bacsu, Sarah Anne Fraser, Ali Akbar Jamali, Christine Conanan, Alison L Chasteen, Shirin Vellani, Rory Gowda-Sookochoff, Corinne Berger, Jasmine C Mah, Florriann Fehr, Anila Virani, Zahra Rahemi, Kate Nanson, Allison Cammer, Melissa K Andrew, Karl S Grewal, Katherine S McGilton, Samantha Lautrup, Raymond J Spiteri

JMIR Infodemiology 2024;4:e63464

Concurrent Mentions of Vaping and Alcohol on Twitter: Latent Dirichlet Analysis

Concurrent Mentions of Vaping and Alcohol on Twitter: Latent Dirichlet Analysis

Similar to prior work, retweets (tweets originally composed by a different Twitter user and reshared by another user) and replies (responses to tweets that may or may not include the original tweet) were retained, as they were assumed to reflect an endorsement of the original post [50].

Lynsie R Ranker, David Assefa Tofu, Manyuan Lu, Jiaxi Wu, Aruni Bhatnagar, Rose Marie Robertson, Derry Wijaya, Traci Hong, Jessica L Fetterman, Ziming Xuan

J Med Internet Res 2024;26:e51870

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis

By comparing these 2 models, we determined the effect that the context of a tweet has on predicting the emotionality associated with the tweet. To predict the sentiment labels for each tweet, we used a multinomial multivariate logistic regression model. The purpose of this model was to classify tweets into one of the following three categories: positive (+1), negative (–1), or neutral (0) sentiment tweets. We implemented a classifier that used logistic regression to find the label for each tweet.

Varun K Rao, Danny Valdez, Rasika Muralidharan, Jon Agley, Kate S Eddens, Aravind Dendukuri, Vandana Panth, Maria A Parker

J Med Internet Res 2024;26:e57885

A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study

A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study

The inclusion criteria were twofold: (1) the SA center had an active Twitter account and (2) it had posted at least 1 tweet on its account. To verify the eligibility of these centers, the authors manually searched their home page and Twitter pages and conducted thorough Google searches. Ultimately, the Twitter accounts of 133 SA centers were included as the final sample for this study.

Jia Xue, Micheal L Shier, Junxiang Chen, Yirun Wang, Chengda Zheng, Chen Chen

J Med Internet Res 2024;26:e51698

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

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

Code policy and the law only if the tweet does not contain additional content that would lead you to double-code as awareness or another code. Pharmaceuticals and drugs Text that mentions a generic or brand name drug or a pharmaceutical firm. Prevention and risk information Text that describes cancer risk, behaviors that increase risk (eg, smoking and environmental causes), and behaviors that reduce risk or prevent cancer (eg, healthy diet and smoking cessation).

Nancy Lau, Xin Zhao, Alison O'Daffer, Hannah Weissman, Krysta Barton

JMIR Cancer 2024;10:e52061