Jiawei Zhou

ABOUT ME

I am a PhD student in Human-Centered Computing at Georgia Tech. My research broadly focuses on Human-AI Interaction, Social Computing, and Health & Wellbeing. I am a member of the Social Dynamics and Wellbeing (SocWeB) Lab under the advisory of Dr. Munmun De Choudhury.

I adopt a theory-guided approach using both quantitative and qualitative methods to understand the role of technology in addressing or exacerbating problems in the information environment, social interactions, and individual well-being. My work examines and responds to the risk of AI’s generative ability in creating low-quality information with high scalability and persuasiveness. Specifically, I evaluate the presence and communication of AI risks in the information ecosystem and high-stakes domain applications through analyses of content compositions and audience perception.

My research has been published at premier HCI venues, such as CHI, CSCW, UbiComp, and ACL. I have received the Best Paper Honorable Mention at CHI and the Foley Scholar Award (the highest award for PhD student research contributions to computing at Georgia Tech).

NEWS

🏅 [Nov 2024]Honored to receive the Foley Scholar award (the highest award for PhD student research contributions to computing at Georgia Tech)
📄 [May 2024] Our paper "A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech" is accepted to ACL
🏅 [Apr 2023] Our paper "Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions" received Honorable Mention!

HIGHLIGHTED WORK

Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions
Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, Munmun De Choudhury
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-20).

[ PDF] [ DOI] [ BIB]

@inproceedings{zhou2023synthetic,
title={Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions},
author={Zhou, Jiawei and Zhang, Yixuan and Luo, Qianni and Parker, Andrea G and De Choudhury, Munmun},
booktitle={Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems},
pages={1--20},
year={2023},
isbn = {9781450394215},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3544548.3581318},
doi = {10.1145/3544548.3581318},
series = {CHI '23}
}

Read Abstract

Large language models have abilities in creating high-volume human-like texts and can be used to generate persuasive misinformation. However, the risks remain under-explored. To address the gap, this work first examined characteristics of AI-generated misinformation (AI-misinfo) compared with human creations, and then evaluated the applicability of existing solutions. We compiled human-created COVID-19 misinformation and abstracted it into narrative prompts for a language model to output AI-misinfo. We found significant linguistic differences within human-AI pairs, and patterns of AI-misinfo in enhancing details, communicating uncertainties, drawing conclusions, and simulating personal tones. While existing models remained capable of classifying AI-misinfo, a significant performance drop compared to human-misinfo was observed. Results suggested that existing information assessment guidelines had questionable applicability, as AI-misinfo tended to meet criteria in evidence credibility, source transparency, and limitation acknowledgment. We discuss implications for practitioners, researchers, and journalists, as AI can create new challenges to the societal problem of misinformation.

🏅Best Paper Honorable Mention
🎤 Invited talk @CSIRO Human Centric AI Seminars, Amazon AWS Responsible AI Science Meeting
📝 Covered by UK government’s AI Foundation Models Initial Review, CNET, Georgia Tech College of Computing Press

Harm in Layers: Compositions of Misinformative Hate in Anti-Asian Speech and Impacts on Perceived Harmfulness
Jiawei Zhou, Gaurav Verma, Lei Zhang, Nicholas Chang, Munmun De Choudhury
Proc. ACM Hum.-Comput. Interact. 9, 2, Article CSCW092 (April 2025), 22 pages.

[ PDF] [ DOI] [ BIB]

@article{zhou2025harm,
author = {Zhou, Jiawei and Verma, Gaurav and Zhang, Lei and Chang, Nicholas and De Choudhury, Munmun},
title = {Harm in Layers: Compositions of Misinformative Hate in Anti-Asian Speech and Impacts on Perceived Harmfulness},
year = {2025},
month = {apr},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {9},
number = {2},
url = {https://doi.org/10.1145/3710990},
journal = {Proc. ACM Hum.-Comput. Interact.},
articleno = {92},
numpages = {22}
}

Read Abstract

During times of crisis, heightened anxiety and fear create fertile ground for hate speech and misinformation, as people are more likely to fall for and be influenced by it. This paper looks into the interwoven relationship between anti-Asian hatred and COVID-19 misinformation amid the pandemic. By analyzing 785,798 Asian hate tweets and surveying 308 diverse participants, this empirical study explores how hateful content portrays the Asian community, including its truthfulness and targets, as well as what makes such portrayals harmful. We observed a high prevalence of misinformative hate speech that was lengthier, less emotional, and expressed more motivational drives than general hate speech. Overall, we found that anti-Asian rhetoric was characterized by an antagonism and inferiority framing, with misinformative hate underscoring antagonism and general hate emphasizing calls for action. Among all entities being explicitly criticized, China and the Chinese were constantly named to assign blame, with misinformative hate more likely to finger-point than general hate. Our survey results indicated that hateful messages with misinformation, demographic targeting, or divisive references were perceived as significantly more damaging. Individuals who placed less importance on free speech, had personal encounters with hate speech, or believed in the natural origin of COVID-19 were more likely to perceive higher severity. Taken together, this work highlights the distinct compositions of hate within misinformative hate speech that influences perceived harmfulness and adds to the complexity of defining and moderating harmful content. We discuss the implications for designing more context- and culture-sensitive counter-strategies and building more adaptive and explainable moderation approaches.

PUBLICATIONS

CSCW'25 Harm in Layers: Compositions of Misinformative Hate in Anti-Asian Speech and Impacts on Perceived Harmfulness
Jiawei Zhou, Gaurav Verma, Lei Zhang, Nicholas Chang, Munmun De Choudhury
Proc. ACM Hum.-Comput. Interact. 9, 2, Article CSCW092 (April 2025), 22 pages.

[ PDF] [ DOI] [ BIB]

ACL'24 A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech
Gaurav Verma, Rynaa Grover, Jiawei Zhou, Binny Mathew, Jordan Kraemer, Munmun De Choudhury, Srijan Kumar
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp.12672-12684).

[ PDF] [ DOI] [ Webpage] [ BIB]

@inproceedings{verma24violence,
title={A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech},
author={Verma, Gaurav and Grover, Rynaa and Zhou, Jiawei and Mathew, Binny and Kraemer, Jordan and Choudhury, Munmun and Kumar, Srijan},
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.684",
pages = "12672--12684"
}

CSCW'24 Using Sensor-Captured Patient-Generated Data to Support Clinical Decision-making in PTSD Therapy
Hayley I. Evans, Myeonghan Ryu, Theresa Hsieh, Jiawei Zhou, Kefan Xu, Kenneth W Akers, Andrew M. Sherrill, Rosa I. Arriaga
Proceedings of the ACM on Human-Computer Interaction 8, no. CSCW1 (2024): 1-28.

[ PDF] [ DOI] [ BIB]

@article{evans2024using,
author = {Evans, Hayley I. and Ryu, Myeonghan and Hsieh, Theresa and Zhou, Jiawei and Xu, Kefan and Akers, Kenneth W. and Sherrill, Andrew M. and Arriaga, Rosa I.},
title = {Using Sensor-Captured Patient-Generated Data to Support Clinical Decision-making in PTSD Therapy},
year = {2024},
month = {apr},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {8},
number = {CSCW1},
url = {https://doi.org/10.1145/3637426},
doi = {10.1145/3637426},
journal = {Proc. ACM Hum.-Comput. Interact.},
articleno = {149},
numpages = {28}
}

CHI'23 Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions
🏅Best Paper Honorable Mention
Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, Munmun De Choudhury
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-20).

[ PDF] [ DOI] [ BIB]

Ubicomp'23 Exergy: A Toolkit to Simplify Creative Applications of Wind Energy Harvesting
Jung Wook Park, Sienna Xin Sun, Tingyu Cheng, Dong Whi Yoo, Jiawei Zhou, Youngwook Do, Gregory D. Abowd, Rosa I. Arriaga
Proceedings of the ACM Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 1 (2023): 1-28.

[ PDF] [ DOI] [ BIB]

@article{park2023exergy,
author = {Park, Jung Wook and Sun, Sienna Xin and Cheng, Tingyu and Yoo, Dong Whi and Zhou, Jiawei and Do, Youngwook and Abowd, Gregory D. and Arriaga, Rosa I.},
title = {Exergy: A Toolkit to Simplify Creative Applications of Wind Energy Harvesting},
journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
year = {2023},
volume = {7},
number = {1},
pages = {1--28},
publisher={ACM New York, NY, USA},
url = {https://doi.org/10.1145/3580814}
}

ICHI'23 Characterizing the Users of Patient Portal Messaging: A Single Institutional Cohort Study
Ming Huang*, Aditya Khurana*, George Mastorakos*, Jiawei Zhou, Nansu Zong, Yue Yu, Julie E. Prigge, Christi A. Patten, Hongfang Liu, Brian A. Costello
IEEE 11th International Conference on Healthcare Informatics (ICHI), pp. 381-387, IEEE, 2023.

[ PDF] [ DOI] [ BIB]

@INPROCEEDINGS{huang2023characterizing,
author={Huang, Ming and Khurana, Aditya and Mastorakos, George and Zhou, Jiawei and Zong, Nansu and Yu, Yue and Prigge, Julie E. and Patten, Christi A. and Liu, Hongfang and Costello, Brian A.},
booktitle={2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)},
title={Characterizing the Users of Patient Portal Messaging: A Single Institutional Cohort Study},
year={2023},
pages={381-387},
doi={10.1109/ICHI57859.2023.00057}}

CSCW'22 Veteran Critical Theory as a Lens to Understand Veterans' Needs and Support on Social Media
Jiawei Zhou, Koustuv Saha, Irene Michelle Lopez Carron, Dong Whi Yoo, Catherine R. Deeter, Munmun De Choudhury, Rosa I. Arriaga
Proceedings of the ACM on Human-Computer Interaction 6, no. CSCW1 (2022): 1-28.

[ PDF] [ DOI] [ BIB]

@article{zhou2022veteran,
author = {Zhou, Jiawei and Saha, Koustuv and Lopez Carron, Irene Michelle and Yoo, Dong Whi and Deeter, Catherine R. and De Choudhury, Munmun and Arriaga, Rosa I.},
title = {Veteran Critical Theory as a Lens to Understand Veterans' Needs and Support on Social Media},
journal={Proceedings of the ACM on Human-Computer Interaction},
volume={6},
number={CSCW1},
pages={1--28},
year={2022},
publisher={ACM New York, NY, USA},
url = {https://doi.org/10.1145/3512980}
}

ICHI'22 A Tale of Two Perspectives: Harvesting System Views and User Views to Understand Patient Portal Engagement
Jiawei Zhou, Rosa I. Arriaga, Hongfang Liu, Ming Huang
IEEE 10th International Conference on Healthcare Informatics (ICHI), pp. 373-383. IEEE, 2022.

[ PDF] [ DOI] [ BIB]

@inproceedings{zhou2022tale,
title={A Tale of Two Perspectives: Harvesting System Views and User Views to Understand Patient Portal Engagement},
author={Zhou, Jiawei and Arriaga, Rosa I and Liu, Hongfang and Huang, Ming},
booktitle={2022 IEEE 10th International Conference on Healthcare Informatics (ICHI)},
pages={373--383},
year={2022},
organization={IEEE}
}

CHI'22 Perspectives on Integrating Trusted Other Feedback in Therapy for Veterans with PTSD
Hayley I. Evans, Catherine R. Deeter, Jiawei Zhou, Kimberly Do, Andrew M. Sherrill, Rosa I. Arriaga
In CHI Conference on Human Factors in Computing Systems (pp. 1-16).

[ PDF] [ DOI] [ BIB]

@inproceedings{evans2022perspectives,
title={Perspectives on Integrating Trusted Other Feedback in Therapy for Veterans with PTSD},
author={Evans, Hayley Irene and Deeter, Catherine R and Zhou, Jiawei and Do, Kimberly and Sherrill, Andrew M and Arriaga, Rosa I},
booktitle={Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems},
pages={1--16},
year={2022}
}

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