About

Jingbo Meng is an Associate Professor at the School of Communication at The Ohio State University. His research concerns the processes and effects of social networks and communication technologies on promoting physical and mental health. Specifically, his work aims to understand and design online social networks that enhance exchanges of social support, facilitate the spread of credible health information, and influence health behaviors and psychological well-being. Recently, he has begun exploring the use of artificial intelligence in online social networks for social support and combating health misinformation. His research has been published in journals such as the Journal of Computer-Mediated Communication, Journal of Communication, Communication Research, and Health Communication. He is also involved with the Communication and Computation for Well-Being Lab (CWell).

Research topics

• Computer Science
• Political Science
• Sociology
• Psychology
• Medicine
• Social psychology
• Nursing
• Media studies
• Internet privacy
• Developmental psychology
• Medical education
• Art
• Public relations
• Law
• World Wide Web
• Clinical psychology
• Literature

Selected publications

• Do AI Fact-Checkers Benefit from Providing Explanations? Effects on Trust and Adoption Based on Need for Cognition

  2026-04-13

  article
  PublisherDOI

• Label, Debunk, or Nudge? The Effectiveness of AI Decision Makers and AI Decision Assistants in Reducing Perceived Health Misinformation Credibility

  Journal of Broadcasting & Electronic Media · 2026-05-11

  articleOpen access
  PublisherDOI

• AI-mediated social support: the prospect of human–AI collaboration

  Journal of Computer-Mediated Communication · 2025-05-30 · 8 citations

  articleOpen access1st authorCorresponding

  Abstract The rise of large language models (LLMs) expands opportunities for creating interpersonal messages. Building on artificial intelligent (AI)-mediated communication (AI-MC), this study examines how people use LLM-based chatbots to generate support messages and how patterns of human–AI collaboration shape message features and influence message evaluations of helpfulness and authenticity. We propose the process-adoption model, categorizing message generation into four patterns: human-only, AI-only, modified-AI, and AI-guided. Results showed that AI-only and modified-AI messages were more likely than human-only messages to include informational and emotional support, which in turn, enhanced viewers’ evaluations of message helpfulness and authenticity. AI-guided messages were more likely to provide reciprocal self-disclosure than AI-only messages, which enhanced perceived authenticity. Lastly, AI-guided messages were rated as more authentic than AI-only messages even after accounting for the mediating effects of message features. These findings provide a nuanced understanding of the AI-MC spectrum, and discussions are provided about human–AI collaboration in support provision.

  PublisherOA PDFDOI

• How Do Lay Users Seek Information with ChatGPT: An In-Situ Interview Study

  International Journal of Human-Computer Interaction · 2025-11-19 · 2 citations

  articleOpen access
  PublisherDOI

• Support Network Typology and Psychological Well-Being Among Young Adults

  Health Communication · 2025-03-25 · 1 citations

  articleOpen accessSenior author

  The psychological well-being of young adults is a growing concern in the United States. Social networks, comprising relationships that provide various types of support, are crucial predictors of their psychological well-being. A network typology offers a pattern-centered approach to classify relational compositions into subgroups with similar patterns. Using longitudinal data from the UC Berkeley Social Networks Study (UCNets), this study identified a network typology for young adults' support networks and examined its relationship with psychological well-being across four functional support networks: confidant, advice-seeking, practical support, and companionship networks. Clustering analysis identified six support network types: family-focused, friends-focused, partner-friends, friends-family, partner-family, and peers-focused (friends and schoolmates) networks. Multilevel regression analyses indicated that family-focused networks were the most beneficial confidant and companionship network type for improving young adults' psychological well-being. In addition, relying on peers-focused networks for advice-seeking and practical support was associated with lower psychological well-being compared to family-focused networks. The findings provide important practical implications for developing health interventions.

  PublisherOA PDFDOI

• Effectiveness of Communication Competence in AI Conversational Agents for Health: Systematic Review and Meta-Analysis

  Journal of Medical Internet Research · 2025-11-03 · 7 citations

  reviewOpen accessSenior author

  Background: With advancements in artificial intelligence and large language models, researchers and designers have increasingly focused on enhancing the conversational capacity of health-related conversational agents (CAs). Communication competence, a key concept in interpersonal communication influencing relational and health outcomes, has been extended to human-machine communication to emphasize the CAs' ability to demonstrate appropriate communicative behaviors in managing relationships with humans. Objective: This review aims to summarize the operationalization of communication competence in health CAs and assess its impact on 4 primary outcomes: users' evaluations of CA, use of CA, psychological outcomes, and health outcomes. Methods: A systematic literature search was conducted in 7 databases (ACM Digital Library, APA PsycInfo, Communication and Mass Media Complete, ProQuest Dissertations & Theses, Scopus, Web of Science Core Collection, and PubMed). Studies were included if they adopted experimental designs to manipulate CAs' communication competence in health-related conversations, recruited human participants, and reported at least 1 relevant outcome. Risk of bias was assessed using the revised Cochrane risk-of-bias tool. The systematic review summarized commonly used communication competence strategies. Three-level random-effects meta-analytic models were used to estimate pooled effect sizes for 4 primary outcomes. Moderator analyses were conducted to assess whether effect sizes varied across publication year, participants' average age, type of interaction with CAs, health topics, and publication outlet. Results: Of the 8309 identified papers, 31 independent experimental studies were included in the systematic review. Eleven strategies were identified to enhance CAs' communication competence: empathetic response, contingency, humor, small talk, emotional expressiveness, self-disclosure, personalization, social etiquette, explanation, open-ended questions, and partnership. Of the 31 studies, 25 met the criteria for meta-analysis, which involved 4525 participants with a mean age of 29.7 (SD 9.2) years. The meta-analytic findings showed that communication competence has a significant small-to-medium effect on users' evaluations of CAs (Hedges g=0.45, 95% CI 0.24-0.66) and psychological outcomes (Hedges g=0.49, 95% CI 0.19-0.78). The effect sizes on the use of CA (Hedges g=0.11, 95% CI -0.05 to 0.26) and health outcomes (Hedges g=0.18, 95% CI -0.13 to 0.50) are not significant. Moderator analyses showed that the effects remain stable across participants' age, type of interaction, and health topics. Conclusions: This review highlights communication competence as a critical component in the design of health care CAs, particularly in improving users' evaluations and psychological outcomes. However, the limited number of studies examining health outcomes restricts the robustness of its effectiveness on this outcome. Future research is encouraged to directly evaluate the effects on tangible health outcomes.

  PublisherDOI

• Examining the Content and Form of Supportive Conversations with Chatbots

  International Journal of Human-Computer Interaction · 2025-02-24 · 6 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• The role of communication in health-focused conversational AI: a review of systematic reviews examining conversational features, design characteristics, and interaction functions

  Behaviour and Information Technology · 2025-10-17

  reviewOpen access
  PublisherDOI

• When a Chatbot Disappoints You: Expectancy Violation in Human-Chatbot Interaction in a Social Support Context

  Communication Research · 2024-01-25 · 60 citations

  article

  Although users’ expectations of a chatbot’s performance could greatly shape their interaction experience, they have been underexplored in the context of social support where chatbots are gaining popularity. A 2 × 2 experiment created expectancy violation and confirmation conditions by matching or mismatching a chatbot’s expertise label (expert vs. non-expert) and its interactional contingency (contingent vs. generic feedback to users). Contingent feedback from chatbots was found to have positive effects on participants’ evaluation of the bot and their perceived emotional validation, regardless of the bot’s expertise label. When providing generic feedback to participants, a bot received worse evaluation and induced less emotional validation on participants when it was labeled as an expert, rather than a non-expert, highlighting the detrimental effect of negative expectancy violation than negative expectancy confirmation in interactions with a social support chatbot. Theoretical and practical implications are discussed.

  PublisherDOI

• Navigating persuasive strategies in online health misinformation: An interview study with older adults on misinformation management

  PLoS ONE · 2024-07-25 · 6 citations

  articleOpen accessCorresponding

  Online health misinformation commonly includes persuasive strategies that can easily deceive lay people. Yet, it is not well understood how individuals respond to misinformation with persuasive strategies at the moment of exposure. This study aims to address the research gap by exploring how and why older adults fall into the persuasive trap of online health misinformation and how they manage their encounters of online health misinformation. Using a think-aloud protocol, semi-structured interviews were conducted with twenty-nine older adults who were exposed to articles employing twelve groups of common persuasive strategies in online health misinformation. Thematic analysis of the transcripts revealed that some participants fell for the persuasive strategies, yet the same strategies were detected by others as cues to pin down misinformation. Based on the participants' own words, informational and individual factors as well as the interplay of these factors were identified as contributors to susceptibility to misinformation. Participants' strategies to manage misinformation for themselves and others were categorized. Implications of the findings are discussed.

  PublisherOA PDFDOI

Frequent coauthors

• Wei Peng

  Collaborative Innovation Center of Chemistry for Energy Materials

  19 shared

• Wei Peng

  Michigan State University

  11 shared

• Cuihua Shen

  University of California, Davis

  8 shared

• Anfan Chen

  Hong Kong Baptist University

  7 shared

• Janet E. Squires

  7 shared

• Kaiping Chen

  University of Wisconsin–Madison

  6 shared

• Zheng An

  University of Hawaii at Hilo

  6 shared

• Margaret McLaughlin

  Novartis (United States)

  5 shared

Education

• PhD, Annenberg School for Communication

  University of Southern California

  2014