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Themes, Policies, and Attention Shifts Regarding COVID-19 Vaccinations in German-Speaking Regions: Infoveillance Study Using Tweets

Themes, Policies, and Attention Shifts Regarding COVID-19 Vaccinations in German-Speaking Regions: Infoveillance Study Using Tweets

Moreover, this study further advances the methodological literature on extracting, filtering, and analyzing Twitter data for monitoring public debates using web discourse data. We examined tweets within the time span of January 1, 2020, to January 31, 2022, using the Tweets KB [22] pipeline. Tweets KB is a large-scale knowledge base of annotated tweets harvested using the Twitter streaming application programming interface (API).

Katarina Boland, Christopher Starke, Felix Bensmann, Frank Marcinkowski, Stefan Dietze

J Med Internet Res 2025;27:e63909


Information Pathways and Voids in Critical German Online Communities During the COVID-19 Vaccination Discourse: Cross-Platform and Mixed Methods Analysis

Information Pathways and Voids in Critical German Online Communities During the COVID-19 Vaccination Discourse: Cross-Platform and Mixed Methods Analysis

Apart from large social media platforms (eg, Facebook, Meta Platforms Inc or X, formerly Twitter, X Corp) [17], the messaging app Telegram (Telegram FZ-LLC) has shown particular potential for spreading false health information through a combination of circumstances [18-20]. Telegram’s channel function allowed individuals to address a large, public audience. In such channels, creators can post content that is accessible to a potentially unlimited number of subscribers, who can join freely [21].

Silvan Wehrli, Anna-Maria Hartner, T Sonia Boender, Bert Arnrich, Christopher Irrgang

J Med Internet Res 2025;27:e76309


Social Media Discussions About Robotic Total Knee Arthroplasty: Cross-Sectional Analysis

Social Media Discussions About Robotic Total Knee Arthroplasty: Cross-Sectional Analysis

The following data were extracted for further analysis: account location, number of followers, number of tweets, and year joined Twitter. We used Chat GPT-4o [17] to categorize the accounts into different categories. The following prompt was inserted into Chat GPT-4o: I am in the process of writing a scientific article looking at Tweets discussing robotic total knee arthroplasty on Twitter. The excel file included in this message contains the raw data of 2000 tweets.

Charles Desgagné, Jordan J Levett, Lior M Elkaim, John Antoniou

JMIR Infodemiology 2025;5:e69883


Global Influence of Cannabis Legalization on Social Media Discourse: Mixed Methods Study

Global Influence of Cannabis Legalization on Social Media Discourse: Mixed Methods Study

A systematic review that analyzed the advantages and disadvantages of using Twitter in public health research concluded that it is a valuable tool for identifying social concerns and information needs on a given topic [12,13]. Previous studies have demonstrated the effectiveness of content analysis as a public health tool for analyzing and studying issues related to drugs [14-17].

Consuelo Castillo-Toledo, Carolina Donat-Vargas, María Montero-Torres, Francisco J Lara-Abelenda, Fernando Mora, Melchor Alvarez-Mon, Javier Quintero, Miguel Ángel Álvarez-Mon

JMIR Infodemiology 2025;5:e65319


Surveillance of Twitter Data on COVID-19 Symptoms During the Omicron Variant Period: A Sentiment Analysis

Surveillance of Twitter Data on COVID-19 Symptoms During the Omicron Variant Period: A Sentiment Analysis

For instance, Health Map combines formal sources such as the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) with informal data sources such as Twitter to reduce reporting delays [4]. A study on the 2010 cholera outbreak in Haiti assessed the correlation between unofficial data sources (eg, Health Map and Twitter) and government-reported cases. The effective reproduction number was also estimated using these nonofficial sources [5].

Kaiyue Zhang, Zhaojin Guo, Yujie Ai, An-Ran Li, Anlin Li, Ziyu Liu, Yittie Yi Ting Tse, Xinyu Zhou, Taoran Liu, Chuxi Xiong, Jian Huang, Wai-kit Ming

JMIR Form Res 2025;9:e66237


Analyzing Health Care Professionals’ Resilience and Emotional Responses to COVID-19 via Twitter: Retrospective Cohort and Matched Comparison Group Study

Analyzing Health Care Professionals’ Resilience and Emotional Responses to COVID-19 via Twitter: Retrospective Cohort and Matched Comparison Group Study

To address the gaps described between HCPs and the general population throughout the pandemic, we analyze the emotions of a matched sample of HCPs and non-HCPs from a high-quality Twitter Panel dataset [25,26], selecting users who tweeted consistently throughout the study period from January 2019 to May 2022. The prepandemic period enables us to control for baseline differences.

Noa Tal, Idan-Chaim Cohen, Aviad Elyashar, Nir Grinberg, Rami Puzis, Odeya Cohen

J Med Internet Res 2025;27:e72521


Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study

Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study

X (formerly known as Twitter) provides real-time insights into the thoughts, feelings, and conversations of millions of users. Natural language processing (NLP) tools are instrumental in analyzing social media content, offering deeper insights into public perception. NLP methods enable the analysis of public sentiment toward specific topics, the detection of emerging trends, and the identification of demographic groups participating in these discussions.

Mohammed A Almanna, Lior M Elkaim, Mohammed A Alvi, Jordan J Levett, Ben Li, Muhammad Mamdani, Mohammed Al‑Omran, Naif M Alotaibi

JMIR Form Res 2025;9:e60859


Research Dissemination Strategies in Pediatric Emergency Care Using a Professional Twitter (X) Account: A Mixed Methods Developmental Study of a Logic Model Framework

Research Dissemination Strategies in Pediatric Emergency Care Using a Professional Twitter (X) Account: A Mixed Methods Developmental Study of a Logic Model Framework

In particular, Twitter (now rebranded as X, though “Twitter” is used in this article given its widely recognized name) has over 600 million monthly active users [5] and has gained traction across clinical subspecialties, journals, and academia for research dissemination [6-8]. Having a Twitter presence is becoming increasingly important as a means to reach target audiences and affect bibliometric markers of research impact.

Gwendolyn C Hooley, Julia N Magana, Jason M Woods, Shyam Sivasankar, Lauren VonHoltz, Anita R Schmidt, Todd P Chang, Michelle Lin

JMIR Form Res 2025;9:e59481


Public Discourse Toward Older Drivers in Japan Using Social Media Data From 2010 to 2022: Longitudinal Analysis

Public Discourse Toward Older Drivers in Japan Using Social Media Data From 2010 to 2022: Longitudinal Analysis

The number of social media users escalates globally [20], with nearly 50% of Japan’s population using Twitter (now X), a leading text-based social media platform [21]. Social media not only facilitates access to a broad range of information but also serves as a platform for public discussion, allowing individuals to express their views, opinions, and sentiments.

Akito Nakanishi, Masao Ichikawa, Yukie Sano

JMIR Infodemiology 2025;5:e69321


Gender Differences in X (Formerly Twitter) Use, Influence, and Engagement Among Cardiologists From the Top U.S. News Best Hospitals

Gender Differences in X (Formerly Twitter) Use, Influence, and Engagement Among Cardiologists From the Top U.S. News Best Hospitals

Social media platforms, such as X (formerly Twitter), can foster collaboration, mentorship, and promotion of research [5,6]. However, studies examining X’s impact on existing gender gaps are limited. In this study, we aimed to analyze differences between X users and non–X users and differences in X use by gender among adult cardiologists. This cross-sectional study was exempt from ethical approval by the Cedars-Sinai institutional review board due to the use of publicly available data. The top 20 U.S.

Minji Seok, Sungjin Kim, Harper Tzou, Olivia Peony, Mitchell Kamrava, Andriana P Nikolova, Katelyn M Atkins

JMIR Cardio 2025;9:e66308