About

Dhiraj Murthy is a Professor of Media Studies, Sociology, and Information at the University of Texas at Austin. His research explores the intersections of social media and artificial intelligence with issues of race and ethnicity, health, virtual organizations, organizational communication, and natural disasters. He employs a diverse range of methodologies including qualitative, mixed methods, and computational approaches such as machine learning. Dr. Murthy has made significant scholarly contributions, having edited three journal special issues and authored over 100 articles, book chapters, and papers. He is the author of the book Social Media Cultures and the first scholarly book about Twitter, with the second edition published by Polity Press in 2018. His work has been recognized internationally, including being named one of the World’s Top 2% of Scientists in Stanford University’s 2023 list for Communication & Media Studies and Sociology.

Research topics

• Computer Science
• Political Science
• World Wide Web
• Business
• Natural Language Processing
• Computer Security
• Medicine
• Internet privacy
• Artificial Intelligence
• Machine Learning
• Medical education
• Psychology
• Public relations
• Nursing
• Database

Selected publications

• ‘Muslim Punk' Music Online

  2026-01-22

  book-chapter1st authorCorresponding
  PublisherDOI

• 74 Use and evaluation of generative artificial intelligence for qualitative analysis in health sciences: A scoping review

  Journal of Clinical and Translational Science · 2026-04-01

  articleOpen access

  Objectives/Goals: This is a scoping review of the use of generative AI (GenAI) for qualitative analysis in the health sciences. The primary objectives are to summarize the methods used for qualitative analysis based on the approach (e.g., inductive vs. deductive), metrics for examining GenAI’s performance by approach type, and best practices for improving performance. Methods/Study Population: We searched six databases (PubMed, EMBASE, CINAHL, Scopus, Web of Science, and PsycINFO) using a comprehensive search string tailored to each database. We identified 5,853 unique results, of which 223 were identified as potentially relevant after review of abstracts. We will conduct a full manuscript review for each potentially relevant result to confirm inclusion, including use of GenAI for qualitative analysis of physical or mental health text data where a full-length paper is available. Two study team members will review each manuscript to confirm inclusion prior to data extraction and coding. We will also enter each manuscript into Yale Clarity, a secure GenAI platform powered by multiple large language models (e.g., ChatGPT, Claude), to examine GenAI’s capacity to facilitate manuscript screening and coding. Results/Anticipated Results: We will code included articles for: the research topic area; type of qualitative data analyzed (interview, focus group, medical chart, text-based digital or social media content); type of study (original or secondary data, review); type of qualitative analysis (thematic analysis, content analysis, grounded theory); type of approach (inductive, deductive); steps in analytic process that generative AI was used (initial open coding and sense making, final coding based on established codebook, identification of overarching themes and narratives); GenAI platform(s) and large language model(s) used; types of GenAI prompts used to facilitate analyses; processes and measures/metrics to evaluate the accuracy and quality of GenAI’s results; outcomes and methods used to increase GenAI’s accuracy and/or quality. Discussion/Significance of Impact: Qualitative research is essential to understand patient perspectives and increase treatment effectiveness and accessibility for all. To actualize GenAI’s potential to facilitate rapid and large-scale qualitative analysis, we first need to understand its strengths, weaknesses, and best practices for maximizing GenAI’s accuracy and quality.

  PublisherOA PDFDOI

• Correction: Vaping, Acculturation, and Social Media Use Among Mexican American College Students: Protocol for a Mixed Methods Web-Based Cohort Study (Preprint)

  2026-03-18

  article

  <sec> <title>UNSTRUCTURED</title> . </sec>

  PublisherDOI

• Correction: Vaping, Acculturation, and Social Media Use Among Mexican American College Students: Protocol for a Mixed Methods Web-Based Cohort Study

  JMIR Research Protocols · 2026-04-02

  articleOpen access

  [This corrects the article DOI: 10.2196/63584.].

  PublisherDOI

• Quantifying the spread of racist content on fringe social media: A case study of Parler

  Big Data & Society · 2025-04-29

  articleOpen accessCorresponding

  Few studies characterize the diffusion of racist content on fringe social media platforms. We demonstrate how racism spread on Parler, a far right, un(der)-moderated social media platform, and that a single comment to a racist post increases the likelihood a person will generate and propagate new racist content. We found that racism on Parler was a social “contagion.” Using 50,375 posts from 2018 to 2021 that contained racist remarks, we quantified the spread of racism from the posts-to-comments (micro) and user-to-user (macro) levels. Comments on racist posts were 21 times more likely to be racist than comments on non-racist posts. On an average, Parler users posted 166% more racist content after an engagement with a racist post. At the posts-to-comments level, the spread of racist sentiment is alarming within ethnic subgroups (e.g. anti-Jewish-specific comments were 191 times more likely to appear on “anti-Jewish” posts; anti-Black-specific comments were 227 times more likely to appear on “anti-Black” posts). The spread of racist rhetoric between subgroups is also significant, albeit lower than within subgroups, suggesting a spillover effect. Aggregate user posting patterns also suggest that the spread of racist rhetoric between subgroups is lower, but significant. Our findings therefore suggest interventions should target users actively engaging with racist content. Furthermore, our study provides evidence that Parler served as a gateway platform that radicalized users toward racist content production, underscoring the urgency of intervention. Given the drawbacks of traditional moderation strategies, we propose additional interventions, policies, and structural changes for social media platforms to mitigate racism online.

  PublisherDOI

• Examining hurricane–related social media topics longitudinally and at scale: A transformer-based approach

  PLoS ONE · 2025-01-24 · 4 citations

  articleOpen access1st authorCorresponding

  Instead of turning to emergency phone systems, social media platforms, such as Twitter, have emerged as alternative and sometimes preferred venues for members of the public in the US to communicate during hurricanes and other natural disasters. However, relevant posts are likely to be missed by responders given the volume of content on platforms. Previous work successfully identified relevant posts through machine-learned methods, but depended on human annotators. Our study indicates that a GPU-accelerated version of BERTopic, a transformer-based topic model, can be used without human training to successfully discern topics during multiple hurricanes. We use 1.7 million tweets from four US hurricanes over seven years and categorize identified topics as temporal constructs. Some of the more prominent topics related to disaster relief, user concerns, and weather conditions. Disaster managers can use our model, data, and constructs to be aware of the types of themes social media users are producing and consuming during hurricanes.

  PublisherOA PDFDOI

• Humanizing digital governance: human-centered sociological scholarship into digital governance

  Information Communication & Society · 2025-12-08

  articleSenior author
  PublisherDOI

• Exploring The Obesity Risks of Ultra-Processed Diets

  International Journal of Pharma and Bio Sciences · 2025-01-17 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• A Dashboard Approach to Monitoring Mpox-Related Discourse and Misinformation on Social Media

  ArXiv.org · 2025-05-26

  preprintOpen accessSenior author

  Mpox (formerly monkeypox) is a zoonotic disease caused by an orthopoxvirus closely related to variola and remains a significant global public health concern. During outbreaks, social media platforms like X (formerly Twitter) can both inform and misinform the public, complicating efforts to convey accurate health information. To support local response efforts, we developed a researcher-focused dashboard for use by public health stakeholders and the public that enables searching and visualizing mpox-related tweets through an interactive interface. Following the CDC's designation of mpox as an emerging virus in August 2024, our dashboard recorded a marked increase in tweet volume compared to 2023, illustrating the rapid spread of health discourse across digital platforms. These findings underscore the continued need for real-time social media monitoring tools to support public health communication and track evolving sentiment and misinformation trends at the local level.

  PublisherOA PDFDOI

• Automating remix: generative AI, creative labor, and the decay of aura

  Information Communication & Society · 2025-12-29

  article

  The widespread adoption of Generative Artificial Intelligence Technology (GenAI) is prompting researchers to re-evaluate the relationship between technology and human creativity. While the pervading assumption is that GenAI will transform and not replace human creativity, this prediction overlooks the longstanding disempowerment of many creative workers in technological assemblages. We examine GenAI by contextualizing it as technology that accelerates and newly interarticulates two continuing capitalist trends. First, GenAI intensifies distantiation – the process by which representational technologies introduce physical distance between creators, artifacts, and audiences – eroding the unique quality of presence Walter Benjamin referred to as ‘aura.’ Second, GenAI automates remix practices, deriving its content from already increasingly varied datasets of human creative outputs. Though GenAI is a new technology, the zeitgeist surrounding it obscures how it contributes to these ongoing processes. We argue that when GenAI continues task automation by hyper-accelerating both distantiation and remix, it also intensifies the precarity of creative labor by prioritizing hyper-efficiency over human creative work.

  PublisherDOI

Recent grants

• RAPID: Rural Loss Estimates of Hurricane Florence Enabled by Citizen Scientists

  NSF · $50k · 2018–2020

• RUI: VOSS: The Potential for Social Networking Cyberinfrastructure to Facilitate Virtual Organization Breeding Grounds

  NSF · $400k · 2010–2014

Frequent coauthors

• Alexander Gross

  17 shared

• Grace Kong

  Yale University

  12 shared

• Keri K. Stephens

  11 shared

• Juhan Lee

  9 shared

• Hassan Dashtian

  9 shared

• Brett W. Robertson

  University of South Carolina

  7 shared

• William Roth Smith

  University of Tennessee at Knoxville

  6 shared

• Ishank Arora

  University of Southern California

  6 shared

Labs

• Computational Media LabPI

Education

• PhD

  University of Cambridge