Hanjia Lyu

I am a first year Ph.D. student of the Computer Science Department at Unversity of Rochester (UR), where I am advised by Prof. Jiebo Luo. Previously, I completed my master’s in Data Science at UR and bachelor’s at Fudan University. My general research area is data mining, network science, and computational social science. I am also interested in machine learning and health informatics.


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What's New


Research

Representative works are highlighted.

2022
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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, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine. After feature inference and opinion mining, 10,945 unique Twitter users are included in the study population. Multinomial logistic regression and counterfactual analysis are conducted.

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  • Taking sides: Public Opinion over the Israel-Palestine Conflict in 2021
    Arsal Imtiaz, Danish Khan, Hanjia Lyu, Jiebo Luo
    International Workshop on Social Sensing (SocialSens): Special Edition on Belief Dynamics, AAAI International Conference on Web and Social Media (ICWSM), 2022
    [PDF] [Bibtex]

    To understand the global sentiment on the conflict, we devise an observational study to understand the friendliness of countries, agglomerated by the sentiments of tweets. We collect Twitter data using popular hashtags around and specific to the conflict containing opinions neutral or partial to the two parties.

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  • Look behind the Censorship: Reposting-User Characterization and Muted-Topic Restoration
    Yichi Qian, Qiyi Shan, Hanjia Lyu, Jiebo Luo
    International Workshop on Social Sensing (SocialSens): Special Edition on Belief Dynamics, AAAI International Conference on Web and Social Media (ICWSM), 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
    International Conference on Pattern Recognition, 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. The memory is composed of the prototypes with corresponding labels, both of which can be updated online. We establish a benchmark for visual sentiment analysis with label noise using publicly available datasets. The experiment results of the proposed benchmark settings comprehensively show the effectiveness of our method.

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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
    International Conference of the Learning Sciences, 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]

    Using a sample of nearly four million geotagged English tweets and the data from the CDC COVID Data Tracker, 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 in the U.S. from April 19 when U.S. adults were vaccine eligible to June 30, 2021, after controlling state-level factors such as demographics, education, and the pandemic severity.

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  • Understanding Political Polarization on Social Platforms by Jointly Modeling Users, Connections and Multi-modal Post Contents in Heterogeneous Graphs
    Hanjia Lyu, Jiebo Luo
    arXiv, 2022
    [PDF] [Bibtex]

    Unlike previous studies, we characterize political polarization by jointly modeling user information, their connections, and the multi-modal post contents in a heterogeneous graph. By mapping the node embeddings into a two-dimensional space, we find there is clear segregation between left- and right-leaning users. Although using only one of the features or the concatenation does not improve the performance of modeling, we find notable differences in user descriptions, topics, images, and levels of retweet activities.

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  • “Ban the Chinese spyware!”: 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, a popular news-oriented social media platform. We employ the state-of-the-art transformer model to build a reliable sentiment classifier. We then use LDA to model the topics associated with positive and negative comments. We also conduct content analysis by studying the changes in the semantic meaning of the companies’ names over time.

    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 Transactions on Computational Social Systems, 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]

    We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California by utilizing a multitude of environmental data including population density, Normalized Difference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), tree mortality area, tree mortality number, and altitude.

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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 Disparities in Oral Health in America amid the COVID-19 Pandemic
    Yangxin Fan, Hanjia Lyu, Jin Xiao, Jiebo Luo
    arXiv, 2021
    [PDF] [Bibtex]

    We conduct a large-scale social media-based study of oral health during the COVID-19 pandemic based on tweets from 9,104 Twitter users across 26 states (with sufficient samples) in the United States for the period between November 12, 2020 and June 14, 2021. By conducting logistic regression, we find that discussions vary across user characteristics. More importantly, we find social disparities in oral health during the pandemic.

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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
    Journal of Medical Internet Research: 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
    Journal of Social Computing, 2021
    [PDF] [Bibtex]

    We focus on the performance of the crowdfunding campaigns on GoFundMe over a wide variety of funding categories. We analyze the attributes available at the launch of the campaign and identify attributes that are important for each category of the campaigns.

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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. However, the relationship between pandemic severity and the negative affect was moderated by personality (i.e., openness and conscientiousness) and family connectedness.

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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
    International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (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
    International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (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, by mining transportation patterns of US residents from March 2020 to July 2020.

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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
    Journal of Medical Internet Research: 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.

    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 Transactions on Big Data, 2020
    [PDF] [Bibtex]

    We characterize the Twitter users who use controversial terms and those who use non-controversial terms for COVID-19. We find significant differences between these two groups of Twitter users across their demographics, user-level features like the number of followers, political following status, as well as geo-locations.

    Internships

    Wyze Labs, Kirkland, WA
    Jan - May 2022. Advisor: Zhongwei Cheng, Mohammad Mahdi Kamani, Lin Chen.
    Project: Trigger-Action Rule Recommendation in Smart Home Devices.

    Awards
    Publications

    2022
    • Kevin C. Kapcio, Hanjia Lyu, Kyle C. Purrman, Christian G. Peyre, 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, California, 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, Georgia 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, Georgia 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, 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, 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.
    Service

    Journal Reviewer
    • Maternal and Child Health Journal
    • Telematics and Informatics
    • SAGE Open
    • International Journal of General Medicine
    • The Social Science Journal
    • IEEE Transactions on Computational Social Systems
    • Journal of Multidisciplinary Healthcare
    • BMC Public Health
    • IEEE Transactions on Knowledge and Data Engineering
    • IEEE Transactions on Multimedia
    • BMC Oral Health
    • BMC Medical Research Methodology
    • Journal of Computational Social Science
    • Frontiers in Psychology
    • Frontiers in Public Health
    • Scientific Reports
    • Discover Mental Health
    • BMC Health Services Research
    • Applied Artificial Intelligence
    • Frontiers in Psychiatry
    Conference Reviewer
    • ICWSM (21, 22)
    • ICDM (21)
    • TheWebConf (22)
    • ISLS (22)
    Teaching
    • TA CSC 440 - Fall 2022, Data Mining
    • TA CSC 446 - Spring 2022, Machine Learning
    • TA CSC 440 - Fall 2021, Data Mining
    • TA CSC 240/440 - Fall 2020, Data Mining

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