Hanjia Lyu

I am a second-year Ph.D. student of the Computer Science Department at the University 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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Representative works are highlighted.

* indicates co-authorship.

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. In particular, we categorize these studies into three groups - using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

Computational Assessment of Hyperpartisanship in News Titles
Hanjia Lyu*, Jinsheng Pan*, Zichen Wang*, Jiebo Luo
arXiv, 2023
[PDF] [Bibtex]

We first adopt a human-guided machine learning framework to develop 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 the present by nine representative media organizations across three media bias groups - Left, Central, and Right in an active learning manner. The fine-tuned transformer-based language model achieves an overall accuracy of 0.84 and an F1 score of 0.78 on an external 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.

ML-SD Modeling: How Machine Learning Can Support Scientific Discovery Learning for K-12 STEM Education
Xiaofei Zhou, Hanjia Lyu, Jiebo Luo, Zhen Bai
Artificial Intelligence for Education Workshop (AI4Edu), AAAI Conference on Artificial Intelligence (AAAI), 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.

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. The proposed method consists of four parts: (1) a visual-textual branch to learn features directly from data for sentiment analysis, (2) a visual expert branch with a set of pre-trained "expert" encoders to extract effective visual features, (3) a CLIP branch to implicitly model visual-textual correspondence, and (4) a multimodal feature fusion network based on either BERT or MLP to fuse multimodal features and make sentiment prediction. Extensive experiments on three datasets show that our method produces better visual-textual sentiment analysis performance than existing methods.

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, which has been effectively and efficiently leveraged by researchers to address real-world issues during the COVID-19 pandemic.

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]

Causal inference and model interpretability research are gaining increasing attention, especially in the domains of healthcare and bioinformatics. Despite recent successes in this field, decorrelating features under nonlinear environments with human interpretable representations has not been adequately investigated. To address this issue, we introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding.

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.

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]

We conduct a large-scale social media-based study of oral health during the COVID-19 pandemic based on the 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.

Understanding Political Polarization via Jointly Modeling Users, Connections and Multimodal Contents on Heterogeneous Graphs
Hanjia Lyu, Jiebo Luo
ACM Multimedia Conference (ACM MM), 2022
[PDF] [Video] [Poster] [Bibtex]

Understanding political polarization on social platforms is important as public opinions may become increasingly extreme when they are circulated in homogeneous communities, thus potentially causing damages in the real world. Automatically detecting political ideology of social media users can help better understand political polarization. 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.

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.

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.

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.

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 (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. 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.

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 (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.

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.

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.

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 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.

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.

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.

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.

What Contributes to a Crowdfunding Campaign’s Success? Evidence and Analyses from GoFundMe Data
Xupin Zhang, Hanjia Lyu, Jiebo Luo
IEEE 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.

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.

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.

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.

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.

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.

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.

Meta AI, Menlo Park, CA
May - Sep 2022. Advisors: Fiona Tang, Ren Chen, Yinglong Xia.
Project: Recommender systems for short videos on Instagram Reels.

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

* indicates co-authorship.

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