Publications 

[5] Oak, R., Haroon, M., Jo, W., Wojcieszak, M., & Chhabra, A. (2025). Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms. Proceedings of the ACL Main Conference.

[4] Jo, W., & Wojcieszak, M. (2025). MetaHarm: Harmful YouTube Video Dataset Annotated by Domain Experts, GPT-4-Turbo, and Crowdworkers. Proceedings of the International AAAI Conference on Web and Social Media. 

[3] Kim, S., Kim, K., & Jo, C. W. (2024). Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of HPV Vaccination. Preventive Medicine Reports.

[2] Jo, W., & Lee, J. H. (2023). Measuring toxicity of online news comments on social minority groups by partisan media users: Application of a BERT-based deep-learning classifier. Korean Journal of Broadcasting and Telecommunication Studies (한국방송학보).

[1] Park, J., Jo, W., & Choi, J. (2023). How has the North Korean media viewed the South Korean presidents and governments over the past 20 years? Big data analysis of North Korean news articles. Korean Journal of Journalism & Communication Studies (한국언론학보).

Revision or Under Review

last updated October, 2025

[2] Jo, W., & Wojcieszak, M. Rejection of Cross-cutting News Depends on Content, Source, and Cultural Contexts: Evidence from Online Trace Data. [R&R 2nd Round]

• 🏆 Top Paper Award at the 2025 NCA - Political Communication Division 

[1] Jo, W., Wesołowska, M., & Wojcieszak, M. Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators. [Under Review] 

Working Papers

[4] Jo, W., & Wojcieszak, M. YouTube Algorithm Amplifies Out-Group Hostility than In-Group Favoritism: Especially to Conservative Users

🏆 Top Poster Award at the 2025 UKC - Social Science Tech Group

[3] Jo, W., Bonagiri, S. A., & Wojcieszak, M. Reason over Toxicity: More Users and Deeper, Better Conversations under Reasonable Comments in Political Reply Networks

[2] Ye, M., Jo, W., & Wojcieszak, M. Revealing Hatred Under Humor: Analyzing Political Memes on Reddit

[1] Jo, W., & Wojcieszak, M. YouTube Users Engage with Problematic More than Non-Problematic Videos

RA Teams

I love working with passionate undergraduate RAs and mentoring them in research. I have led wonderful RA teams on my first-author projects at UC Davis. 

1. Harmful Content Detection: Jacob Feinstein, Chushan Huang, Olivia He, Janghee Oh, Katharine Owen, Ashtyn Phan, Ary Christine Quintana, Hong An Tran, Dior Tran, Kristen Ya, Betty Su, Kaitlyn Tan, and CJ Verrengia

2. Cross-Cutting Exposure Analysis Using Digital Trace Data: Alex Tran, Daniel Kim, Grisha Bandodkar

3. Democratic Conversations on Yahoo News: Yumin Jun