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Infoveillance of COVID-19 Infections in Dentistry Using Platform X: Descriptive Study

Infoveillance of COVID-19 Infections in Dentistry Using Platform X: Descriptive Study

Among the different social media platforms, X (previously known as Twitter) is one of the most popular forms used for health care communication [11]. In the field of medicine, platform X (formerly known as Twitter) has been used as a source of social media data within the context of health care–related information [11]. X allows millions of users to send and receive “tweets” or short messages for free.

Alghalia Al-Mansoori, Ola Al Hayk, Sharifa Qassmi, Sarah M Aziz, Fatima Haouari, Tawanda Chivese, Faleh Tamimi, Alaa Daud

J Med Internet Res 2025;27:e54650

Exploring Public Sentiment on the Repurposing of Ivermectin for COVID-19 Treatment: Cross-Sectional Study Using Twitter Data

Exploring Public Sentiment on the Repurposing of Ivermectin for COVID-19 Treatment: Cross-Sectional Study Using Twitter Data

Twitter (now X), a hub for real-time public discourse, has become a fertile ground for divergent views on COVID-19 treatment [4,5]. This sentiment analysis focuses on Twitter discussions about ivermectin, showing public opinion that, while not devoid of misinformation risks, these discussions offer an alternative lens to understand the societal pulse on this contentious topic [6].

Angga Prawira Kautsar, Rano Kurnia Sinuraya, Jurjen van der Schans, Maarten Jacobus Postma, Auliya A Suwantika

JMIR Form Res 2025;9:e50536

Quantifying Public Engagement With Science and Malinformation on COVID-19 Vaccines: Cross-Sectional Study

Quantifying Public Engagement With Science and Malinformation on COVID-19 Vaccines: Cross-Sectional Study

At the time of data extraction, direct Twitter (subsequently rebranded as X) engagement with the paper yielded 165,720 tweets from 81,861 users with an upper bound of 11,094,945 followers, 181 media mentions, and a total altmetric score of 45,844. This investigative piece suggested that Pfizer vaccine trials were compromised by the unblinding of subjects and data falsification.

David Robert Grimes, David H Gorski

J Med Internet Res 2025;27:e64679

Profiling Generalized Anxiety Disorder on Social Networks: Content and Behavior Analysis

Profiling Generalized Anxiety Disorder on Social Networks: Content and Behavior Analysis

Vedula and Parthasarathy [19] studied the behaviors of people with depression on Twitter, whereas Peng et al [42] analyzed the depressive characteristics of Sina Weibo users based on posts and user profiles.

Linah Alhazzaa, Vasa Curcin

J Med Internet Res 2025;27:e53399

Gender Differences in X (Formerly Twitter) Use Among Oncology Physicians at National Cancer Institute–Designated Cancer Centers: Cross-Sectional Study

Gender Differences in X (Formerly Twitter) Use Among Oncology Physicians at National Cancer Institute–Designated Cancer Centers: Cross-Sectional Study

Reference 8: Gender-related and geographic trends in interactions between radiotherapy professionals on Twitter Reference 9: Expanding opportunities for professional development: utilization of Twitter by early career Reference 10: Gender differences in Twitter use and influence among health policy and health servicestwitterGender Differences in X (Formerly Twitter) Use Among Oncology Physicians at National Cancer Institute–Designated

Vivian Tieu, Sungjin Kim, Minji Seok, Leslie Ballas, Mitchell Kamrava, Katelyn M Atkins

J Med Internet Res 2025;27:e66054

Exploring Psychological Trends in Populations With Chronic Obstructive Pulmonary Disease During COVID-19 and Beyond: Large-Scale Longitudinal Twitter Mining Study

Exploring Psychological Trends in Populations With Chronic Obstructive Pulmonary Disease During COVID-19 and Beyond: Large-Scale Longitudinal Twitter Mining Study

For example, Lwin et al [18] used Twitter data to explore global trends in the 4 emotions—fear, anger, sadness, and joy—alongside their relative salience, spanning from January 28 to April 9, 2020. Similarly, through mining large-scale Twitter data, Zhang et al [19] conducted a cross-sectional study on web-based public sentiments in the early phase of the COVID-19 pandemic.

Chunyan Zhang, Ting Wang, Caixia Dong, Duwei Dai, Linyun Zhou, Zongfang Li, Songhua Xu

J Med Internet Res 2025;27:e54543

Leveraging Large Language Models for Infectious Disease Surveillance—Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis

Leveraging Large Language Models for Infectious Disease Surveillance—Using a Web Service for Monitoring COVID-19 Patterns From Self-Reporting Tweets: Content Analysis

To supplement the shortage of clinical data and gain further insights into the development trends and variant tendencies of COVID-19, researchers have turned to social media, specifically Twitter (subsequently rebranded as X). Social media data offer unique advantages, such as rapid updates and a broad geographical reach, which traditional clinical data often lack [5,6].

Jiacheng Xie, Ziyang Zhang, Shuai Zeng, Joel Hilliard, Guanghui An, Xiaoting Tang, Lei Jiang, Yang Yu, Xiufeng Wan, Dong Xu

J Med Internet Res 2025;27:e63190

Identifying Misinformation About Unproven Cancer Treatments on Social Media Using User-Friendly Linguistic Characteristics: Content Analysis

Identifying Misinformation About Unproven Cancer Treatments on Social Media Using User-Friendly Linguistic Characteristics: Content Analysis

Approximately 30% of cancer-related social media posts on Facebook, Reddit, Pinterest, and X (previously known as Twitter) contain misinformation, and a staggering 77% of these posts have the potential to encourage patients to pursue futile and toxic therapies, resulting in physical, psychological, and logistical burdens [7]. Cancer misinformation persists across various cancer types and is more pervasive in more prevalent cancers.

Ilona Fridman, Dahlia Boyles, Ria Chheda, Carrie Baldwin-SoRelle, Angela B Smith, Jennifer Elston Lafata

JMIR Infodemiology 2025;5:e62703

Understanding Citizens’ Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI

Understanding Citizens’ Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI

A study on Twitter examined how users with different ideological views and follower bases expressed vaccine favorability and specific vaccine-related concerns. Users’ perception of the vaccine was classified by a fine-tuned BERT model using training data that were coded by the authors.

Ryuichi Saito, Sho Tsugawa

J Med Internet Res 2025;27:e63824