Hanjia Lyu's Home Page

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

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

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.

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

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.

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

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 Political Polarization on Social Platforms by Jointly Modeling Users, Connections and Multi-modal Post Contents in Heterogeneous Graphs

Hanjia Lyu, Jiebo Luo

arXiv, 2022

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.

“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

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.

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

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

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.

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.

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.

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.

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.

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.