About

Sharath Chandra Guntuku is an Associate Professor in the research track in the Department of Computer and Information Science at the University of Pennsylvania, with a secondary appointment in the Annenberg School of Communication. He directs the Computational Social Listening Lab. His research focuses on the intersection of artificial intelligence, public health informatics, and health data science, utilizing large-scale digital data such as social media, electronic health records, online reviews, and smartphone-based interactions to uncover insights that improve health outcomes and reduce health disparities. He develops natural language processing and machine learning methods to monitor and predict health-related behaviors, assess mental health at individual and population levels, stratify risk across diverse populations, and design scalable, culturally informed interventions, including conversational agents for health behavior change. His interdisciplinary research has been supported by over $5.1 million in external funding and has resulted in publications in prominent journals such as PNAS, JAMA Network Open, and npj Digital Medicine. His work has applications ranging from clinical decision support to real-time public health surveillance and has been implemented with partners including state Departments of Health and the World Bank Group. His research has garnered coverage from various media outlets including the American Psychological Association, WIRED, Canadian Broadcasting Company, The Atlantic, and US News.

Research topics

• Medicine
• Psychiatry
• Artificial Intelligence
• Psychology
• Sociology
• Computer Security
• Computer Science
• Virology
• World Wide Web
• Demography
• Social psychology
• Business
• Pathology
• Gerontology
• Advertising
• Internet privacy

Selected publications

• The assessment of psychological richness, meaning, and happiness with social media text data: Predictive accuracy and distinct behavioral correlates

  PLoS ONE · 2026-01-07

  articleOpen accessSenior author

  Assessing well-being with social media text data is a promising method, but besides hedonic well-being, little is known about whether additional well-being dimensions, such as psychological richness and eudaimonic well-being, can be predicted from such data. We compare the predictive accuracy for hedonic well-being, eudaimonic well-being, and the recently proposed construct of psychological richness in a large sample of Facebook users (n = 2,644), and find that the inclusion of language features incrementally improved model prediction accuracy beyond demographic features for psychological richness, but not for hedonic or eudaimonic well-being. Psychological richness had the lowest overall prediction accuracy (r = .21) followed by hedonic well-being (r = .27) and eudeomonic well-being (r = .29). The linguistic features associated with Psychological Richness were face valid, and in many instances the content and direction of the associations were unique to Psychological Richness, which provides discriminant validity evidence.

  PublisherDOI

• Self-Reported Side Effects of Semaglutide and Tirzepatide in Online Communities

  arXiv (Cornell University) · 2026-03-12

  preprintOpen accessSenior author

  Social media can reveal patient experiences with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) that extend beyond clinical trial data. We analyzed 410,198 Reddit posts (May 2019-June 2025) mentioning semaglutide or tirzepatide. A total of 67,008 users self-reported using these medications, and 43.5% described at least one side effect. Gastrointestinal symptoms predominated, including nausea (36.9%), fatigue (16.7%), vomiting (16.3%), constipation (15.3%), and diarrhea (12.6%). Notably, reproductive symptoms (e.g., menstrual irregularities) and temperature-related complaints (e.g., chills, hot flashes) emerged as unrecognized potential effects. These findings highlight patient concerns not well captured in current labeling or trials. Large-scale social media analysis can complement traditional pharmacovigilance by detecting emerging safety signals and expanding understanding of the real-world safety profile of GLP-1 RAs.

  PublisherDOI

• Digital phenotyping and ASCVD risk: An exploratory cross-sectional analysis using online behavioral data

  American Heart Journal Plus Cardiology Research and Practice · 2026-01-09

  articleOpen access

  A retrospective, exploratory cross-sectional analysis exploring whether social media data is associated with cardiovascular disease (CVD) risk beyond traditional clinical models. While social media data may capture behavioral and social markers relevant to CVD, their associations with CVD risk remains uncertain. • Associations between social media data, like Facebook wall posts and ASCVD risk warrants further exploration and validation. • Combined Facebook wall posts and electronic health records to examine cardiovascular disease risk factors. • Our analysis found no associations between Facebook wall posts and ASCVD risk, while prior work suggests stronger associations between Facebook language and mental health conditions.

  PublisherDOI

• A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents

  ArXiv.org · 2026-01-15

  articleOpen access

  Style features such as friendly, helpful, or concise are widely used in prompts to steer the behavior of Large Language Model (LLM) conversational agents, yet their unintended side effects remain poorly understood. In this work, we present the first systematic study of cross-feature stylistic side effects. We conduct a comprehensive survey of 127 conversational agent papers from ACL Anthology and identify 12 frequently used style features. Using controlled, synthetic dialogues across task-oriented and open domain settings, we quantify how prompting for one style feature causally affects others via a pairwise LLM as a Judge evaluation framework. Our results reveal consistent and structured side effects, such as prompting for conciseness significantly reduces perceived expertise. They demonstrate that style features are deeply entangled rather than orthogonal. To support future research, we introduce CASSE (Conversational Agent Stylistic Side Effects), a dataset capturing these complex interactions. We further evaluate prompt based and activation steering based mitigation strategies and find that while they can partially restore suppressed traits, they often degrade the primary intended style. These findings challenge the assumption of faithful style control in LLMs and highlight the need for multi-objective and more principled approaches to safe, targeted stylistic steering in conversational agents.

  PublisherOA PDF

• Self-Reported Side Effects of Semaglutide and Tirzepatide in Online Communities

  medRxiv · 2026-03-13

  articleOpen accessSenior author

  ABSTRACT Social media can reveal patient experiences with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) that extend beyond clinical trial data. We analyzed 410,198 Reddit posts (May 2019–June 2025) mentioning semaglutide or tirzepatide. A total of 67,008 users self-reported using these medications, and 43.5% described at least one side effect. Gastrointestinal symptoms predominated, including nausea (36.9%), fatigue (16.7%), vomiting (16.3%), constipation (15.3%), and diarrhea (12.6%). Notably, reproductive symptoms (e.g., menstrual irregularities) and temperature-related complaints (e.g., chills, hot flashes) emerged as unrecognized potential effects. These findings highlight patient concerns not well captured in current labeling or trials. Large-scale social media analysis can complement traditional pharmacovigilance by detecting emerging safety signals and expanding understanding of the real-world safety profile of GLP-1 RAs.

  PublisherOA PDFDOI

• A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents

  arXiv (Cornell University) · 2026-01-15

  preprintOpen access

  Style features such as friendly, helpful, or concise are widely used in prompts to steer the behavior of Large Language Model (LLM) conversational agents, yet their unintended side effects remain poorly understood. In this work, we present the first systematic study of cross-feature stylistic side effects. We conduct a comprehensive survey of 127 conversational agent papers from ACL Anthology and identify 12 frequently used style features. Using controlled, synthetic dialogues across task-oriented and open domain settings, we quantify how prompting for one style feature causally affects others via a pairwise LLM as a Judge evaluation framework. Our results reveal consistent and structured side effects, such as prompting for conciseness significantly reduces perceived expertise. They demonstrate that style features are deeply entangled rather than orthogonal. To support future research, we introduce CASSE (Conversational Agent Stylistic Side Effects), a dataset capturing these complex interactions. We further evaluate prompt based and activation steering based mitigation strategies and find that while they can partially restore suppressed traits, they often degrade the primary intended style. These findings challenge the assumption of faithful style control in LLMs and highlight the need for multi-objective and more principled approaches to safe, targeted stylistic steering in conversational agents.

  PublisherDOI

• Introduction to the Special Issue on Large Language Models, Conversational Systems, and Generative AI in Health - Part 2

  ACM Transactions on Computing for Healthcare · 2025-03-20

  articleOpen access

  Dialogue systems are designed to offer human users social support or functional services through natural language interactions. Traditional conversation research has put significant emphasis on a system’s response-ability, including its capacity to understand dialogue context and generate appropriate responses. However, the key element of proactive behavior—a crucial aspect of intelligent conversations—is often overlooked in these studies. Proactivity empowers conversational agents to lead conversations towards achieving pre-defined targets or fulfilling specific goals on the system side. Proactive dialogue systems are equipped with advanced techniques to handle complex tasks, requiring strategic and motivational interactions, thus representing a significant step towards artificial general intelligence. Motivated by the necessity and challenges of building proactive dialogue systems, we provide a comprehensive review of various prominent problems and advanced designs for implementing proactivity into different types of dialogue systems, including open-domain dialogues, task-oriented dialogues, and information-seeking dialogues. We also discuss real-world challenges that require further research attention to meet application needs in the future, such as proactivity in dialogue systems that are based on large language models, proactivity in hybrid dialogues, evaluation protocols and ethical considerations for proactive dialogue systems. By providing a quick access and overall picture of the proactive dialogue systems domain, we aim to inspire new research directions and stimulate further advancements towards achieving the next level of conversational AI capabilities, paving the way for more dynamic and intelligent interactions within various application domains.

  PublisherOA PDFDOI

• Designing Mental-Health Chatbots for Indian Adolescents: Mixed-Methods Evidence, a Boundary-Object Lens, and a Design-Tensions Framework

  ArXiv.org · 2025-11-11

  preprintOpen accessSenior author

  Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. We report a mixed-methods study of Indian adolescents (survey n=362; interviews n=14) examining how they navigate mental-health challenges and engage with digital tools. Quantitative results highlight low self-stigma but significant social stigma, a preference for text over voice interactions, and low utilization of mental health apps but high smartphone access. Our qualitative findings reveal that while adolescents value privacy, emotional support, and localized content in mental health tools, existing chatbots lack personalization and cultural relevance. We contribute (1) a Design-Tensions framework; (2) an artifact-level probe; and (3) a boundary-objects account that specifies how chatbots mediate adolescents, peers, families, and services. This work advances culturally sensitive chatbot design by centering on underrepresented populations, addressing critical gaps in accessibility and support for adolescents in India.

  PublisherOA PDFDOI

• Understanding gender and age differences in language use: cross-cultural insights from Weibo and Facebook

  Humanities and Social Sciences Communications · 2025-10-31 · 2 citations

  articleOpen access

  This study integrates social role theory and socioemotional selectivity theory to investigate the cultural universalities and differences in language use among male and female users across different age groups on Weibo and Facebook. By analyzing social media language, we aim to understand how gender and age influence linguistic patterns and reflect broader cultural norms and societal values. Aggregated language from Weibo and Facebook users (N = 8728 per platform; 665,377 and 742,418 posts, respectively) was analyzed by both a top-down closed-vocabulary (Linguistic Inquiry and Word Count) approach and a data-driven open-vocabulary (Differential Language Analysis) approach. Our findings support and extend social role theory, showing that female users on both platforms use more communal and relational language, while male users focus on agentic and task-oriented content. Cultural dimensions, such as collectivism and individualism, modulate the expression of social roles, with Weibo users adhering more closely to traditional gender norms compared to Facebook users. Our findings also validate and extend the socioemotional selectivity theory by demonstrating how cultural frameworks shape the specific ways aging individuals pursue emotional and social goals. For example, on both platforms, age-related language patterns reveal a U-shaped trend in positive emotions, with a decline in middle age and an increase in older adulthood, reflecting a universal shift toward emotionally meaningful goals. Additionally, older users on Weibo engage more in collectivistic themes, while their Facebook counterparts focus on personal well-being and social ties. These results highlight the complex interplay between culture, gender, and age in shaping language use on social media, providing valuable insights into the cultural and societal influences on communication.

  PublisherOA PDFDOI

• Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination

  2025-01-01 · 5 citations

  articleOpen accessSenior author

  While affective expressions on social media have been extensively studied, most research has focused on the Western context.This paper explores cultural differences in affective expressions by comparing valence and arousal on Twitter/X (geolocated to the US) and Sina Weibo (in Mainland China).Using the NRC-VAD lexicon to measure valence and arousal, we identify distinct patterns of emotional expression across both platforms.Our analysis reveals a functional representation between valence and arousal, showing a negative offset in contrast to traditional lab-based findings which suggest a positive offset.Furthermore, we uncover significant cross-cultural differences in arousal, with US users displaying higher emotional intensity than Chinese users, regardless of the valence of the content.Finally, we conduct a comprehensive language analysis correlating n-grams and LDA topics with affective dimensions to deepen our understanding of how language and culture shape emotional expression.These findings contribute to a more nuanced understanding of affective communication across cultural and linguistic contexts on social media. 1

  PublisherOA PDFDOI

Frequent coauthors

• Lyle Ungar

  California University of Pennsylvania

  110 shared

• Raina M. Merchant

  University of Pennsylvania

  103 shared

• David A. Asch

  59 shared

• Lauren Southwick

  University of Pennsylvania Health System

  39 shared

• Brenda Curtis

  National Institute on Drug Abuse

  35 shared

• Elissa V. Klinger

  University of Pennsylvania Health System

  31 shared

• Salvatore Giorgi

  University of Pennsylvania

  25 shared

• Johannes C. Eichstaedt

  24 shared