About Me
I’m Zichen Wang, a Software Engineer at Meta, currently working on the Messaging Sync Infrastructure team, where I support messaging features across the company’s apps. Prior to my industry career, I was actively involved in academic research in Data Mining and Computational Social Science during graduate school. This background continues to shape my approach to building scalable, data-driven systems. I’m open to opportunities including—but not limited to—Software Engineer, Data Engineer, Machine Learning Engineer, and Research Scientist roles. Feel free to reach out if you’re interested in collaborating. Thank you for visiting! 🌟
Experience
Meta Platforms, Inc.
Software Engineer, Feb 2025 - Present
ByteDance Ltd.
Software Engineer, Jan 2024 - Feb 2025
StoreHub
Front-End Developer, August 2020 - May 2022
Trip.com Group
Software Engineer, September 2019 - August 2020
SonicWall Inc.
Associate Software Engineer, July 2018 - August 2019
Education
University of Rochester
M.S. in Computer Science, September 2022 - December 2023
Purdue University, West Lafayette
B.S. in Computer Science, August 2014 - May 2018
Publications
Computational Assessment of Partisanship in News Titles
Hanjia Lyu*, Jinsheng Pan*, Zichen Wang*, Jiebo Luo
AAAI International Conference on Web and Social Media (ICWSM), 2024
We first adopt a human-guided machine learning framework to construct a new dataset for hyperpartisan news title detection with 2,200 manually labeled and 1.8 million machine-labeled titles that were posted from 2014 to 2022 by nine representative media organizations across three media bias groups - Left, Central, and Right. The fine-tuned transformer-based language model achieves an overall accuracy of 0.84 and an F1 score of 0.78 on the validation set. Next, we conduct a computational analysis to quantify the extent and dynamics of partisanship in news titles. While some aspects are as expected, our study reveals new or nuanced differences between the three media groups.
Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets
Jinsheng Pan, Zichen Wang, Weihong Qi, Hanjia Lyu, Jiebo Luo
IEEE International Conference on Big Data (BigData), 2023
Understanding the framing of political issues is of paramount importance as it significantly shapes how individuals perceive, interpret, and engage with these matters. While prior research has independently explored framing within news media and by social media users, there remains a notable gap in our comprehension of the disparities in framing political issues between these two distinct groups. To address this gap, we conduct a comprehensive investigation, focusing on the nuanced distinctions both qualitatively and quantitatively in the framing of social media and traditional media outlets concerning a series of American Supreme Court rulings on affirmative action, student loans, and abortion rights.
Bias or Diversity? Unraveling Semantic Discrepancy in U.S. News Headlines
Jinsheng Pan*, Weihong Qi*, Zichen Wang, Hanjia Lyu, Jiebo Luo
Workshop on News Media and Computational Journalism (MEDIATE), AAAI International Conference on Web and Social Media (ICWSM), 2023
In this study, we analyzed 1.8 million news headlines from major U.S. media outlets between 2014 and 2022 to examine the semantic discrepancy. Multiple correspondence analysis (MCA) was applied, quantifying the semantic discrepancy in domestic politics, economic issues, social issues, and foreign affairs. Additionally, we compare the most frequent n-grams in media headlines to provide further qualitative insights.
Skills
Programming Languages
Golang, Python, JavaScript/TypeScript, HTML/CSS, Java, C++
Frameworks & Libraries
Kitex, Hertz, Eventbus Message Queue, GORM, Flask, LangChain, Pandas, PyTorch, React.js, Redux, Lodash
Data Processing
Redis, MySQL, Thrift, protobuf, Elastic Search
Tools
Git, Docker, Azure, AWS, Jenkins
Others
Distributed system, Asynchronous processing, Data Mining, Agile development