Hanjia Lyu

I am a first year Ph.D. student of the Computer Science Department at University of Rochester (UR), where I am advised by Prof. Jiebo Luo. Previously, I did 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.

Email: hlyu5 -at- ur.rochester.edu

Google Scholar

What’s new

Publications

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, Jiebo Luo, “Characterizing Discourse about COVID-19 Vaccines: A Reddit Version of the Pandemic Story,” Health Data Science, 2021.
  • Xupin Zhang, Hanjia Lyu, 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, 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.

Research

(in chronological order)

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

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

To investigate the hoarding behaviors in response to the pandemic, we propose a novel computational framework using large scale social media data.

Look behind the Censorship: Reposting-User Characterization and Muted-Topic Restoration

Yichi Qian, Qiyi Shan, Hanjia Lyu, Jiebo Luo

arXiv, 2021

In this paper, we focus on a study of censorship on Weibo, the counterpart of Twitter in China. Specifically, we 1) create a web-scraping pipeline and collect a large dataset solely focus on the reposts from Weibo; 2) discover the characteristics of users whose reposts contain censored information, in terms of gender, location, device, and account type; and 3) conduct a thematic analysis by extracting and analyzing topic information.

Social Disparities in Oral Health in America amid the COVID-19 Pandemic

Yangxin Fan, Hanjia Lyu, Jin Xiao, Jiebo Luo

arXiv, 2021

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.

Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis

Wei Zhu, Zihe Zheng, Haitian Zheng, Hanjia Lyu, Jiebo Luo

arXiv, 2021

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.

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, 2021

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.

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

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

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

Xuping Zhang, Hanjia Lyu, Jiebo Luo

Journal of Social Computing, 2021

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.

Both Rates of Fake News and Fact-based News on Twitter Negatively Correlate with the State-level COVID-19 Vaccine Uptake

Hanjia Lyu, Zihe Zheng, Jiebo Luo

arXiv, 2021

Using a sample of 1.6 million geotagged English tweets and the data from the CDC COVID Data Tracker, we conduct a quantitative study 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 May 7, 2021, after controlling state-level factors such as demographics, education, and the pandemic severity.

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

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

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

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.

State-level Racially Motivated Hate Crimes Contrast Public Opinion on the #StopAsianHate and #StopAAPIHate Movement

Hanjia Lyu, Yangxin Fan, Ziyu Xiong, Mayya Komisarchik, Jiebo Luo

arXiv, 2021

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.

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

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.

Understanding Patterns of Users Who Repost Censored Posts on Weibo

Yichi Qian, Feng Yuan, Hanjia Lyu, Jiebo Luo

arXiv, 2021

We focus on understanding patterns of users whose repost contents would later be censored on Weibo, a counterpart of Twitter in China as a social media platform. 

2020

InsurTech development: Evidence from Chinese media reports

Siqing Cao, Hanjia Lyu, Xian Xu

Technological Forecasting and Social Change, 2020

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

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.

Reviewing and 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
  • Conference Reviewer: ICWSM (21, 22), ICDM 2021, TheWebConf 2022

Teaching

  • TA CSC 440 – Fall 2021, Data Mining
  • TA CSC 240/440 – Fall 2020, Data Mining