What's New
- [07/13/2023] In our new studies (ICWSM 2024, ICWSMW 2023), we uncover media bias in 1.8 million headlines across the political spectrum. Our findings suggest that media bias is growing. The work is also covered by StudyFinds, and shared by ScienceBlog, RealClearScience, Nation, Mirage.News, Phys.org, and the American Association for the Advancement of Science (AAAS) via EurekAlert!
- [03/21/2023] Our survey entitled Human Behavior in the Time of COVID-19: Learning from Big Data, is accepted for publication in the Horizons in Big Data 2022 article collection of Frontiers in Big Data.
- [03/09/2023] I was awarded the 2023 Arts, Sciences and Engineering Donald M. and Janet C. Barnard Fellowship. 🎉🎉
- [03/25/2022] Our study on the scale and scope of the influence of misinformation and fact-based news about COVID-19 vaccines on social media platforms on the vaccine uptake is covered by multiple news outlets including Medical Economics, News Medical, News Wise, UK Today News, and Medical Xpress.
- [07/06/2021] I received the Graduate Student Scholarship of SBP-BRiMS 2021.
- [05/19/2021] In a new study, we dissect the public responses to the #StopAsianHate movement using large scale Twitter data and provide findings that can help design better ways of reducing tension and misunderstandings between ethnic groups. The work is also covered by Rochester Beacon and Futurity.
- [08/12/2020] We won the first place of the University of Rochester Biomedical Data Science Hackathon Summer 2020.
- [07/23/2020] Tech Xplore reports a series of our recent studies that show Twitter mirrors our attitudes and feelings about COVID-19. These findings are shared in a lightning presentation for chairs of computer science PhD-granting departments and heads of major industrial research labs at the Computing Research Association (CRA) Virtual Conference.
- [05/30/2020] Our work on analyzing the use of controversial terms of COVID-19 on Twitter is highlighted by IEEE Spectrum, the flagship magazine and website of the IEEE. Incidentally, this story shares the headlines with the story about the successful launch of the SpaceX Dragon 2 spacecraft. Cool! This work is also covered by Tech Xplore.
- [03/26/2020] Our work on analyzing the performance of the crowdfunding campaigns on GoFundMe over a wide variety of funding categories is covered by The New York Times.
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Research
Representative works are highlighted.
* indicates co-authorship.
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2024
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Computational Assessment of Hyperpartisanship in News Titles
Hanjia Lyu*, Jinsheng Pan*, Zichen Wang*, Jiebo Luo
ICWSM, 2024
[PDF]
[Poster]
[Bibtex]
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.
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2023
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Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking
Mohammad Mahdi Kamani, Yuhang Yao, Hanjia Lyu, Zhongwei Cheng, Lin Chen, Liangju Li, Carlee Joe-Wong, Jiebo Luo
NeurIPS, 2023
[PDF]
[Bibtex]
We present Wyze Rule Dataset, a large-scale dataset designed specifically for smart home rule recommendation research.
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Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets
Jinsheng Pan, Zichen Wang*, Weihong Qi*, Hanjia Lyu, Jiebo Luo
arXiv, 2023
[PDF]
[Bibtex]
We conduct a comprehensive investigation, focusing on the nuanced distinctions 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.
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LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Hanjia Lyu, Song Jiang, Hanqing Zeng, Qifan Wang, Si Zhang, Ren Chen, Chris Leung, Jiajie Tang, Yinglong Xia, Jiebo Luo
arXiv, 2023
[PDF]
[Bibtex]
We investigate various prompting strategies for enhancing personalized content recommendation performance with large language models (LLMs) through input augmentation. Our proposed approach, is termed LLM-Rec. Our empirical experiments show that combining the original content description with the augmented input text generated by LLM using these prompting strategies leads to improved recommendation performance.
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Excitements and Concerns in the Post-ChatGPT Era: Deciphering Public Perception of AI through Social Media Analysis
Weihong Qi, Jinsheng Pan, Hanjia Lyu, Jiebo Luo
arXiv, 2023
[PDF]
[Bibtex]
In this study, we investigate how mass social media users perceive the recent rise of AI frameworks such as ChatGPT.
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Dismantling Hate: Understanding Hate Speech Trends Against NBA Athletes
Edinam Kofi Klutse, Samuel Nuamah-Amoabeng, Hanjia Lyu, Jiebo Luo
SBP-BRiMS, 2023
[PDF]
[Bibtex]
A deep learning classification model is implemented, effectively identifying tweets that genuinely exhibit hate against NBA players.
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Learning to Evaluate the Artness of AI-generated Images
Junyu Chen, Jie An, Hanjia Lyu, Jiebo Luo
arXiv, 2023
[PDF]
[Bibtex]
This paper presents ArtScore, a metric designed to evaluate the degree to which an image resembles authentic artworks by artists (or conversely photographs).
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Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines
Jinsheng Pan*, Weihong Qi*, Zichen Wang, Hanjia Lyu, Jiebo Luo
MEDIATE, 2023
[PDF]
[Bibtex]
In this study, we collect a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022 to thoroughly track and dissect the fine-grained thematic discrepancy in U.S. news media.
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Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning
Jingyang Lin, Junyu Chen, Hanjia Lyu, Igor Khodak, Divya Chhabra, Colby L Day Richardson, Irina Prelipcean, Andrew M Dylag, Jiebo Luo
ICDH, 2023
[PDF]
[Bibtex]
We propose an MTL (multi-task learning) framework to jointly predict multiple adverse neonatal outcomes.
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Applying Machine Learning to Predict Esophageal Cancer Recurrence After Esophagectomy
Hanjia Lyu*, Kevin C. Kapcio*, Kyle C. Purrman, Christian G. Peyre, Carolyn E. Jones, Michal J. Lada, Jiebo Luo
ICDH, 2023
[PDF]
[Bibtex]
We conducted a retrospective study of 260 consecutive patients who underwent esophagectomy for esophageal cancer from 2009 through 2018.
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Human Behavior in the Time of COVID-19: Learning from Big Data
Hanjia Lyu, Arsal Imtiaz, Yufei Zhao, Jiebo Luo
Frontiers in Big Data, 2023
[PDF]
[Bibtex]
In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic.
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ML-SD Modeling: How Machine Learning Can Support Scientific Discovery Learning for K-12 STEM Education
Xiaofei Zhou, Hanjia Lyu, Jiebo Luo, Zhen Bai
AI4Edu, 2023
[PDF]
[Video@YouTube]
[Video@Bilibili]
[Bibtex]
This work proposes research ideas and initial modeling results on the connection between Machine Learning components and young learners' scientific behaviors.
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2022
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Improving Visual-textual Sentiment Analysis by Fusing Expert Features
Junyu Chen, Jie An, Hanjia Lyu, Jiebo Luo
arXiv, 2022
[PDF]
[Bibtex]
We propose a new method that improves visual-textual sentiment analysis by introducing powerful expert visual features.
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Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy among Healthcare Workers
Sajid Hussain Rafi Ahamed, Shahid Shakil, Hanjia Lyu, Xinping Zhang, Jiebo Luo
Special Session on Intelligent Data Mining, IEEE Big Data Conference, 2022
[PDF]
[Bibtex]
Reportedly, a considerably higher proportion of vaccine hesitancy is observed among nurses, compared to doctors. We intend to verify and study this phenomenon at a much larger scale and in fine grain using social media data.
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Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications
Junda Wang, Weijian Li, Han Wang, Hanjia Lyu, Caroline Thirukumaran, Addisu Mesfin, Jiebo Luo
arXiv, 2022
[PDF]
[Bibtex]
We introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding.
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A Fine-Grained Analysis of Public Opinion toward Chinese Technology Companies on Reddit
Enting Zhou*, Yurong Liu*, Hanjia Lyu, Jiebo Luo
arXiv, 2022
[PDF]
[Bibtex]
This study aims to fill in the gap of understanding the public opinion toward Chinese technology companies using Reddit data.
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American Twitter Users Revealed Social Determinants-related Oral Health Disparities amid the COVID-19 Pandemic
Yangxin Fan, Hanjia Lyu, Jin Xiao, Jiebo Luo
Quintessence International, 2022
[PDF]
[Bibtex]
By conducting logistic regression, we find that discussions about oral health vary across user characteristics. More importantly, we find social disparities in oral health during the pandemic.
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Understanding Political Polarization via Jointly Modeling Users, Connections and Multimodal Contents on Heterogeneous Graphs
Hanjia Lyu, Jiebo Luo
ACM MM, 2022
[PDF]
[Video]
[Poster]
[Bibtex]
We adopt a heterogeneous graph neural network to jointly model user characteristics, multimodal post contents as well as user-item relations in a bipartite graph to learn a comprehensive and effective user embedding without requiring ideology labels.
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Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Dissemination
Hanjia Lyu, Junda Wang, Wei Wu, Viet Duong, Xiyang Zhang, Timothy D. Dye, Jiebo Luo
Intelligent Medicine, 2022
[PDF]
[Bibtex]
We adopt a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2.
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Taking sides: Public Opinion over the Israel-Palestine Conflict in 2021
Arsal Imtiaz, Danish Khan, Hanjia Lyu, Jiebo Luo
SocialSens, 2022
[PDF]
[Bibtex]
We devise an observational study to understand the friendliness of countries, agglomerated by the sentiments of tweets.
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Look behind the Censorship: Reposting-User Characterization and Muted-Topic Restoration
Yichi Qian, Qiyi Shan, Hanjia Lyu, Jiebo Luo
SocialSens, 2022
[PDF]
[Bibtex]
We 1) create a web-scraping pipeline and collect a large dataset solely focusing on the reposts from Weibo; 2) discover the characteristics of users whose reposts contain censored information, in terms of gender, device, and account type; and 3) conduct a thematic analysis by extracting and analyzing topic information.
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Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis
Wei Zhu, Zihe Zheng, Haitian Zheng, Hanjia Lyu, Jiebo Luo
ICPR, 2022
[PDF]
[Bibtex]
Our method relies on an external memory to aggregate and filter noisy labels during training and thus can prevent the model from overfitting the noisy cases.
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Challenges and Design Opportunities in Data Analysis for ML-Empowered Scientific Inquiry-Insights from a Teacher Professional Development Study
Xiaofei Zhou, Jingwan Tang, Beilei Guo, Hanjia Lyu, Zhen Bai
ICLS, 2022
[PDF]
[Bibtex]
We investigated the pattern recognition and interpretation behaviors of 18 K-12 STEM teachers when engaged in ML-empowered scientific inquiry during a professional development workshop.
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Misinformation versus Facts: Understanding the Influence of News Regarding COVID-19 Vaccines on Vaccine Uptake
Hanjia Lyu, Zihe Zheng, Jiebo Luo
Health Data Science, 2022
[PDF]
[Bibtex]
We conducted the Fama-MacBeth regression with the Newey-West adjustment to understand the influence of both misinformation and fact-based news on Twitter on the COVID-19 vaccine uptake.
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2021
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Understanding Public Opinion Toward the #StopAsianHate Movement and the Relation With Racially Motivated Hate Crimes in the US
Hanjia Lyu, Yangxin Fan, Ziyu Xiong, Mayya Komisarchik, Jiebo Luo
IEEE TCSS, 2021
[PDF]
[Bibtex]
We conduct a social media study of public opinion on the #StopAsianHate and #StopAAPIHate movement based on 46,058 Twitter users across 30 states in the United States ranging from March 18 to April 11, 2021.
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From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors
Tanqiu Jiang, Sidhant K. Bendre, Hanjia Lyu, Jiebo Luo
Special Session on Intelligent Data Mining, IEEE Big Data Conference, 2021
[PDF]
[Bibtex]
[Video]
We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
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Understanding the Hoarding Behaviors during the COVID-19 Pandemic using Large Scale Social Media Data
Xupin Zhang, Hanjia Lyu, Jiebo Luo
Special Session on Intelligent Data Mining, IEEE Big Data Conference, 2021
[PDF]
[Bibtex]
To investigate the hoarding behaviors in response to the pandemic, we propose a novel computational framework using large scale social media data.
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Social Media Opinions on Working From Home in the United States During the COVID-19 Pandemic: Observational Study
Ziyu Xiong, Pin Li, Hanjia Lyu, Jiebo Luo
JMIR: Medical Informatics, 2021
[PDF]
[Bibtex]
We conducted a large-scale social media study using Twitter data to portray different groups of individuals who have positive or negative opinions on WFH.
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Characterizing Discourse about COVID-19 Vaccines: A Reddit Version of the Pandemic Story
Wei Wu, Hanjia Lyu, Jiebo Luo
Health Data Science, 2021
[PDF]
[Bibtex]
This study aims to offer a clear understanding about different population groups’ underlying concerns when they talk about COVID-19 vaccines, particular those active on Reddit.
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What Contributes to a Crowdfunding Campaign’s Success? Evidence and Analyses from GoFundMe Data
Xupin Zhang, Hanjia Lyu, Jiebo Luo
IEEE JSC, 2021
[PDF]
[Bibtex]
We focus on the performance of the crowdfunding campaigns on GoFundMe over a wide variety of funding categories.
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The Influence of COVID-19 on people’s Well-Being: Big Data Methods for Capturing Working Adults’ Well-being and Protective Factors Nationwide
Xiyang Zhang, Yu Wang, Hanjia Lyu, Yipeng Zhang, Yubao Liu, Jiebo Luo
Frontiers in Psychology, 2021
[PDF]
[Bibtex]
We found that pandemic severity influenced working adults’ negative affect rather than positive affect.
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Fine-Grained Analysis of the Use of Neutral and Controversial Terms for COVID-19 on Social Media
Long Chen, Hanjia Lyu, Tongyu Yang, Yu Wang, Jiebo Luo
SBP-BRiMS, 2021
[PDF]
[Bibtex]
To model the substantive difference of tweets with controversial terms and those with non-controversial terms with regard to COVID-19, we apply topic modeling and LIWC-based sentiment analysis.
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How Political is the Spread of COVID-19 in the United States? An Analysis using Transportation and Weather Data
Karan Vombatkere, Hanjia Lyu, Jiebo Luo
SBP-BRiMS, 2021
[PDF]
[Bibtex]
We investigate the difference in the spread of COVID-19 between the states won by Donald Trump (Red) and the states won by Hillary Clinton (Blue) in the 2016 presidential election.
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Monitoring Depression Trend on Twitter during the COVID-19 Pandemic: Observational Study
Yipeng Zhang, Hanjia Lyu*, Yubao Liu*, Xiyang Zhang, Yu Wang, Jiebo Luo
JMIR: Infodemiology, 2021
[PDF]
[Bibtex]
We create a fusion classifier that combines deep learning model scores with psychological text features and users’ demographic information and investigate these features’ relations to depression signals in the context of COVID-19.
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2020
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InsurTech development: Evidence from Chinese media reports
Siqing Cao, Hanjia Lyu, Xian Xu
Technological Forecasting and Social Change, 2020
[PDF]
[Bibtex]
This paper uses text mining technology and Python to analyze the word frequency and term frequency-inverse document frequency (TFIDF) of 25,662 InsurTech-related news reports from 2015 to 2019 in China.
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Sense and Sensibility: Characterizing Social Media Users Regarding the Use of Controversial Terms for COVID-19
Hanjia Lyu, Long Chen, Yu Wang, Jiebo Luo
IEEE TBD, 2020
[PDF]
[Bibtex]
We characterize the Twitter users who use controversial terms and those who use non-controversial terms for COVID-19.
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Meta AI, Menlo Park, CA
May - Aug 2023. Advisors: Hanqing Zeng,
Yinglong Xia.
Project: Machine Learning on Graphs. Personalized Recommendation via Prompting Large Language Models.
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Meta AI, Menlo Park, CA
May - Sep 2022. Advisors: Fiona Tang,
Ren Chen,
Yinglong Xia.
Project: Recommender systems for short videos on Instagram Reels.
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Wyze Labs, Kirkland, WA
Jan - May 2022. Advisors: Zhongwei Cheng,
Mohammad Mahdi Kamani,
Lin Chen.
Project: Trigger-Action Rule Recommendation in Smart Home Devices.
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Publications
* indicates co-authorship.
Survey
2024
- Hanjia Lyu*, Jinsheng Pan*, Zichen Wang*, and Jiebo Luo, “Computational Assessment of Hyperpartisanship in News Titles,” AAAI International Conference on Web and Social Media (ICWSM), Buffalo, NY, June 2024.
2023
- Mohammad Mahdi Kamani, Yuhang Yao, Hanjia Lyu, Zhongwei Cheng, Lin Chen, Liangju Li, Carlee Joe-Wong, and Jiebo Luo, "Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking," Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), New Orleans, LA, December 2023.
- Junyu Chen, Jie An, Hanjia Lyu, and Jiebo Luo, “How Art-like are AI-generated Images? An Exploratory Study,” International Workshop on Multimedia Content Generation and Evaluation: New Methods and Practice (McGE), ACM Multimedia Conference (ACM MM), Ottawa, Ontario, Canada, October 2023.
- Edinam Kofi Klutse, Samuel Nuamah-Amoabeng, Hanjia Lyu, and Jiebo Luo, “Dismantling Hate: Understanding Hate Speech Trends Against NBA Athletes,” International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), Pittsburgh, PA, September 2023.
- Jingyang Lin, Junyu Chen, Hanjia Lyu, Igor Khodak, Divya Chhabra, Colby L Day Richardson, Irina Prelipcean, Andrew M Dylag, and Jiebo Luo, “Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning,” IEEE International Conference on Digital Health (ICDH), Chicago, IL, July 2023.
- Hanjia Lyu*, Kevin C. Kapcio*, Kyle C. Purrman, Christian G. Peyre, Carolyn E. Jones, Michal J. Lada, and Jiebo Luo “Applying Machine Learning to Predict Esophageal Cancer Recurrence After Esophagectomy,” IEEE International Conference on Digital Health (ICDH), Chicago, IL, July 2023.
- Jinsheng Pan*, Weihong Qi*, Zichen Wang, Hanjia Lyu, and Jiebo Luo, “Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines,” Workshop on News Media and Computational Journalism (MEDIATE), AAAI International Conference on Web and Social Media (ICWSM), Limassol, Cyprus, June 2023.
- Hanjia Lyu, Arsal Imtiaz, Yufei Zhao, and Jiebo Luo, “Human Behavior in the Time of COVID-19: Learning from Big Data,” Frontiers in Big Data, 2023.
- Xiaofei Zhou, Hanjia Lyu, Jiebo Luo, and Zhen Bai, “ML-SD Modeling: How Machine Learning Can Support Scientific Discovery Learning for K-12 STEM Education,” Artificial Intelligence for Education Workshop (AI4Edu), AAAI Conference on Artificial Intelligence (AAAI), Washington DC, February 2023.
2022
- Sajid Hussain Rafi Ahamed, Shahid Shakil, Hanjia Lyu, Xinping Zhang, and Jiebo Luo, “Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy among Healthcare Workers,” Special Session on Intelligent Data Mining, IEEE Big Data Conference, Osaka, Japan, December 2022.
- Timothy Dye, Lisette Alcántara, Hanjia Lyu, Shazia Siddiqi, Saloni Sharma, Eva Pressman, and Jiebo Luo, “Oppression, COVID Vaccination, and Vaccine Sentiments in a Global Sample,” Annals of Epidemiology (ACE Abstracts), 2022.
- Yangxin Fan, Hanjia Lyu, Jin Xiao, and Jiebo Luo, “American Twitter Users Revealed Social Determinants-related Oral Health Disparities amid the COVID-19 Pandemic,” Quintessence International, 2022.
- Hanjia Lyu, and Jiebo Luo, “Understanding Political Polarization via Jointly Modeling Users, Connections and Multimodal Contents on Heterogeneous Graphs,” ACM Multimedia Conference (ACM MM), Lisboa, Portugal, October 2022.
- Kevin C. Kapcio, Hanjia Lyu, Kyle C. Purrman, Christian G. Peyre, and Jiebo Luo, Carolyn E. Jones, and Michal J. Lada, “Applying Machine Learning to Predict Esophageal Cancer Recurrence After Esophagectomy,” Supplemental Issue of Journal of the American College of Surgeons, ACS Clinical Congress, San Diego, CA, October 2022.
- Arsal Imtiaz, Danish Khan, Hanjia Lyu, and Jiebo Luo, “Taking sides: Public Opinion over the Israel-Palestine Conflict in 2021,” International Workshop on Social Sensing (SocialSens): Special Edition on Belief Dynamics, AAAI International Conference on Web and Social Media (ICWSM), Atlanta, GA and online, June 2022.
- Yichi Qian, Qiyi Shan, Hanjia Lyu, and Jiebo Luo, “Look behind the Censorship: Reposting-User Characterization and Muted-Topic Restoration,” International Workshop on Social Sensing (SocialSens): Special Edition on Belief Dynamics, AAAI International Conference on Web and Social Media (ICWSM), Atlanta, GA and online, June 2022.
- Wei Zhu, Zihe Zheng, Haitian Zheng, Hanjia Lyu, and Jiebo Luo, “Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis,” International Conference on Pattern Recognition (ICPR), Montréal, August 2022.
- Xiaofei Zhou, Jingwan Tang, Beilei Guo, Hanjia Lyu, and Zhen Bai, “Challenges and Design Opportunities in Data Analysis for ML-Empowered Scientific Inquiry – Insights from a Teacher Professional Development Study,” International Conference of the Learning Sciences (ICLS), Virtual, June 2022.
- Hanjia Lyu, Zihe Zheng, and Jiebo Luo, “Misinformation versus Facts: Understanding the Influence of News Regarding COVID-19 Vaccines on Vaccine Uptake,” Health Data Science, 2022.
2021
- Hanjia Lyu, Yangxin Fan, Ziyu Xiong, Mayya Komisarchik, and Jiebo Luo, “Understanding Public Opinion toward the #StopAsianHate Movement and the Relation with Racially Motivated Hate Crimes in the US,” IEEE Transactions on Computational Social Systems, 2021.
- Tanqiu Jiang, Sidhant Bendre, Hanjia Lyu, and Jiebo Luo, “From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors,” Special Session on Intelligent Data Mining, IEEE Big Data Conference, Virtual, December 2021.
- Xupin Zhang, Hanjia Lyu, and Jiebo Luo, “Understanding the Hoarding Behaviors during the COVID-19 Pandemic using Large Scale Social Media Data,” Special Session on Intelligent Data Mining, IEEE Big Data Conference, Virtual, December 2021.
- Hanjia Lyu, Junda Wang, Wei Wu, Viet Duong, Xiyang Zhang, Timothy D. Dye, and Jiebo Luo, “Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Dissemination,” Intelligent Medicine, 2021.
- Ziyu Xiong, Pin Li, Hanjia Lyu, and Jiebo Luo, “Social Media Opinions on Working From Home in the United States During the COVID-19 Pandemic: Observational Study,” Journal of Medical Internet Research: Medical Informatics, 2021.
- Wei Wu, Hanjia Lyu, and Jiebo Luo, “Characterizing Discourse about COVID-19 Vaccines: A Reddit Version of the Pandemic Story,” Health Data Science, 2021.
- Xupin Zhang, Hanjia Lyu, and Jiebo Luo, “What Contributes to a Crowdfunding Campaign’s Success? Evidence and Analyses from GoFundMe Data,” IEEE Journal of Social Computing, 2021.
- Xiyang Zhang, Yu Wang, Hanjia Lyu, Yipeng Zhang, Yubao Liu, and Jiebo Luo, “The Influence of COVID-19 on people’s Well-Being: Big Data Methods for Capturing Working Adults’ Well-being and Protective Factors Nationwide,” Frontiers in Psychology, 2021.
- Long Chen, Hanjia Lyu, Tongyu Yang, Yu Wang, and Jiebo Luo, “Fine-Grained Analysis of the Use of Neutral and Controversial Terms for COVID-19 on Social Media,” International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), Virtual, July 2021.
- Karan Vombatkere, Hanjia Lyu, and Jiebo Luo, “How Political is the Spread of COVID-19 in the United States? An Analysis using Transportation and Weather Data,” International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), Virtual, July 2021.
- Yipeng Zhang, Hanjia Lyu*, Yubao Liu*, Xiyang Zhang, Yu Wang, and Jiebo Luo, “Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study,” Journal of Medical Internet Research: Infodemiology, 2021.
2020
- Siqing Cao, Hanjia Lyu, and Xian Xu, “InsurTech development: Evidence from Chinese media reports,” Technological Forecasting and Social Change, 2020.
- Hanjia Lyu, Long Chen, Yu Wang, and Jiebo Luo, “Sense and Sensibility: Characterizing social media users regarding the use of controversial terms for covid-19,” IEEE Transactions on Big Data, 2020.
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