Jiawei Zhou ๅ‘จๅ˜‰็Žฎ

Ph.D. Student
Human-Centered Computing
Georgia Institute of Technology

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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 social interactions and individual well-being. Specifically, my work critically interprets AI's generative ability to meet informational needs by examining the pitfalls and communication of large language models (LLMs) capabilities in generating harmful content.

Combining theoretical power with computational methods, my goal is to address real-world challenges, including misinformation and harmful content, responsible use of LLMs, and social support for vulnerable groups.


NEWS


PUBLICATIONS

CHI'23 Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions
๐Ÿ…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]
@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.


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"
}
Read Abstract
Violence-provoking speech โ€“ speech that implicitly or explicitly promotes violence against the members of the targeted community, contributed to a massive surge in anti-Asian crimes during the COVID-19 pandemic. While previous works have characterized and built tools for detecting other forms of harmful speech, like fear speech and hate speech, our work takes a community-centric approach to studying anti-Asian violence-provoking speech. Using data from ~420k Twitter posts spanning a 3-year duration (January 1, 2020 to February 1, 2023), we develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection using state-of-the-art classifiers. We contrast the capabilities of natural language processing classifiers, ranging from BERT-based to LLM-based classifiers, in detecting violence-provoking speech with their capabilities to detect anti-Asian hateful speech. In contrast to prior work that has demonstrated the effectiveness of such classifiers in detecting hateful speech (F1 = 0.89), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task (F1 = 0.69). We discuss the implications of our findings, particularly the need for proactive interventions to support Asian communities during public health crises.


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}
}
Read Abstract
Today, clinicians have limited visibility into the quality of homework exercises that occur outside of the clinical context; however, understanding patient performance in these exercises is essential for guiding patient-centered care. To address this, we present the Clinician Homework Review (CHR), a unique measure and interface that displays similarity ratings calculated using sensor-captured patient-generated data (sPGD; i.e. heart rate, phone usage, ambient noise, and physical activity) for therapeutic exercises outside of the clinical setting within the post-traumatic stress disorder (PTSD) treatment context. Through concept testing sessions with 10 clinicians, we examine how sPGD can be leveraged to measure and investigate what contributes to patient performance in a therapeutic exercise. We also share in-depth information regarding clinician interpretation and planned use of data displayed by CHR in clinical sessions with patients. We frame our results in the context of situated objectivity and propose the notion of "perceived reference weight," which describes the significance attributed to contextualized data. In doing so, we support clinical decision-making in PTSD therapy.


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}
}
Read Abstract
Energy harvesting reduces the burden of power source maintenance and promises to make computing systems genuinely ubiquitous. Researchers have made inroads in this area, but their novel energy harvesting materials and fabrication techniques remain inaccessible to the general maker communities. Therefore, this paper aims to provide a toolkit that makes energy harvesting accessible to novices. In Study 1, we investigate the challenges and opportunities associated with devising energy harvesting technology with experienced researchers and makers (N=9). Using the lessons learned from this investigation, we design a wind energy harvesting toolkit, Exergy, in Study 2. It consists of a simulator, hardware tools, a software example, and ideation cards. We apply it to vehicle environments, which have yet to be explored despite their potential. In Study 3, we conduct a two-phase workshop: hands-on experience and ideation sessions. The results show that novices (N=23) could use Exergy confidently and invent self-sustainable energy harvesting applications creatively.


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}}
Read Abstract
This work studied message communications on patient portals and examined both the longitudinal trends and the correlations with characteristics of message senders. We analyzed over 5.6 million secure messages sent on the Mayo Clinic patient portal between February 18, 2010, and December 31, 2017. We studied the longitudinal changes in the number of portal messages, patient sendersโ€™ demographics and medical conditions (PheCodes), and provider sendersโ€™ care settings (e.g., primary or specialty) and practice roles (e.g., physician, nurse practitioner, and registered nurses). When compared to non-message-senders, patient message senders had a significantly higher proportion of the demographics: age 41-60, female, married, white, and English-speaking. From 2010-2017, an individual patient sent an average of 9.8 messages per person while a provider sent 418.4. The average number of PheCodes for all patients regardless of portal usage increased from 7.5 +/- 6.9 in 2010 to 10.7 +/- 10.1 in 2017. The Pearson correlation coefficient between average PheCodes per patient and average messages per patient was 0.273 (p < 0.0001). Physicians were the largest proportion of message composers in both primary and specialty care (36.20% of primary, 37.54% of specialty). Starting 2013 onwards, specialty providers comprised the majority of portal providers while primary care providers remained stable around 20-22%. Our results show that patient portals are playing an increasingly significant role in supporting patient-provider communications. The longitudinal growth also sheds light on the possible challenge of communication overload for providers and the healthcare system.


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}
}
Read Abstract
Veterans are a unique marginalized group facing multiple vulnerabilities. Current assessments of veteran needs and support largely come from first-person accounts guided by researchers' prompts. Social media platforms not only enable veterans to connect with each other, but also to self-disclose experiences and seek support. This paper addresses the gap in our understanding of veteran needs and their own support dynamics by examining self-initiated and ecologically-valid self-expressions. In particular, we adopt the Veteran Critical Theory (VCT) to conduct a computational study on the Reddit community of veterans. Using topic modeling, we find veteran-friendly gestures with good intentions might not be appreciated in the subreddit. By employing transfer learning methodologies, we find this community has more informational and emotional support behaviors than general online communities and a higher prevalence of informational support than emotional support. Lastly, an examination of support dynamics reveals some contrasts to previous scholarship in military culture and social media. We discover that positive language and author platform tenure have negative relations with posts receiving replies and replies getting votes, and that replies reflecting personal disclosures tend to get more votes. Through the lens of VCT, we discuss how online communities can help uncover veterans' needs and provide more effective social support.


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}
}
Read Abstract
Patient engagement is recognized as a key factor in promoting care quality and experience. Although patient portals as a prevalent information infrastructure provide a viable means to achieve engaging patients, there is still a limited understanding of how to objectively and systematically evaluate engagement levels in the context of patient portals. We develop the Patient Portal Engagement Framework (PPEF) to objectively and systematically evaluate patient portal engagement and demonstrated its utilization and effectiveness in two scenarios: portal utilization and user feedback. Four engagement levels included in the PPEF are - Inform Patients that allows patients to access health information; Involve Patients that encourages patients to take initiatives; Partner with Patients that supports long-term collaboration between patients and providers; and Support Ecology of Care that extends the scope beyond hospitals into personal and social factors.
We find more portal utilization and user feedback focus in lower levels of patient portal engagement (i.e., patients receiving information and taking active actions in managing care). Our thematic analysis of online user reviews reveals four core themes: conflicts between system and user views, evolving benefits and needs towards patient portals, debates about balancing emotional and informational needs, and reconsideration of power, accessibility, and privacy. We discuss how PPEF can help harvest and synthesize data from the system and user levels, as well as the design implications for patient portals. These results show that patient portals can be designed with practical guidance for engaging patients, complementing current efforts that focus on conceptualizing engagement or rely on psychometrics.


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}
}
Read Abstract
Past research has demonstrated that accounts of trusted others can provide additional context into real world behavior relevant to clinical decision-making and patient engagement. Our research investigates the Social Sensing System, a concept which leverages trusted other feedback for veterans in therapy for PTSD. In our two phase study, we work with 10 clinicians to develop text-message queries and realistic scenarios to present to patients and trusted others. We then present the results in the form of a storyboard to 10 veterans with PTSD and 10 trusted others and gather feedback via semi-structured interview and survey. We find that while trusted other feedback may provide a unique and useful perspective, key design features and considerations of underlying relationships must be considered. We present our findings and utilize the mechanisms and conditions framework to assess the power dynamics of systems such as social sensing in the mental health realm.







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