arXiv subjects

Hanjia Lyu

Publications and source records attributed to Hanjia Lyu.

60 records · Page 4Linked to original sources

arXiv2021

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

#StopAsianHate and #StopAAPIHate are two of the most commonly used hashtags that represent the current movement to end hate crimes against the Asian American and Pacific Islander community. 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. The movement attracts more participation from women, younger adults, Asian and Black communities. 51.56% of the Twitter users show direct support, 18.38% are news about anti-Asian hate crimes, while 5.43% show a negative attitude towards the movement. Public opinion varies across user characteristics. Furthermore, among the states with most racial bias motivated hate crimes, the negative attitude towards the #StopAsianHate and #StopAAPIHate movement is the weakest. To our best knowledge, this is the first large-scale social media-based study to understand public opinion on the #StopAsianHate and #StopAAPIHate movement. We hope our study can provide insights and promote research on anti-Asian hate crimes, and ultimately help address such a serious societal issue for the common benefits of all communities.

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arXiv2021

Understanding Patterns of Users Who Repost Censored Posts on Weibo

In this study, 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. Little is known about the way regulations and censorship work in this indigenous platform and what role it plays in affecting users' expression of ideas. We collect over a million reposts from over 18,000 users and investigate the patterns of users whose reposts contain content that is no longer visible to the public, from the perspective of user location, device, gender, social capital as well as certified status. We find that user characteristics play an important role in affecting behaviors on Weibo.

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arXiv2020

In the Eyes of the Beholder: Analyzing Social Media Use of Neutral and Controversial Terms for COVID-19

During the COVID-19 pandemic, "Chinese Virus" emerged as a controversial term for coronavirus. To some, it may seem like a neutral term referring to the physical origin of the virus. To many others, however, the term is in fact attaching ethnicity to the virus. While both arguments appear reasonable, quantitative analysis of the term's real-world usage is lacking to shed light on the issues behind the controversy. In this paper, we attempt to fill this gap. To model the substantive difference of tweets with controversial terms and those with non-controversial terms, we apply topic modeling and LIWC-based sentiment analysis. To test whether "Chinese Virus" and "COVID-19" are interchangeable, we formulate it as a classification task, mask out these terms, and classify them using the state-of-the-art transformer models. Our experiments consistently show that the term "Chinese Virus" is associated with different substantive topics and sentiment compared with "COVID-19" and that the two terms are easily distinguishable by looking at their context.

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arXiv2020

How Political is the Spread of COVID-19 in the United States? An Analysis using Transportation and Weather Data

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. To ensure a fair comparison, we first use a K-means clustering method to group the 50 states into five clusters according to their population, area and population density. We then characterize daily transportation patterns of the residents of different states using the mean percentage of residents traveling and the number of trips per person. For each state, we study the correlations between travel patterns and infection rate for a 2-month period before and after the official states reopening dates. We observe that during the lock-down, Red and Blue states both displayed strong positive correlations between their travel patterns and infection rates. However, after states reopened we find that Red states had higher travel-infection correlations than Blue states in all five state clusters. We find that the residents of both Red and Blue states displayed similar travel patterns during the period post the reopening of states, leading us to conclude that, on average, the residents in Red states might be mobilizing less safely than the residents in Blue states. Furthermore, we use temperature data to attempt to explain the difference in the way residents travel and practice safety measures.

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arXiv2020

Monitoring Depression Trend on Twitter during the COVID-19 Pandemic

The COVID-19 pandemic has severely affected people's daily lives and caused tremendous economic loss worldwide. However, its influence on people's mental health conditions has not received as much attention. To study this subject, we choose social media as our main data resource and create by far the largest English Twitter depression dataset containing 2,575 distinct identified depression users with their past tweets. To examine the effect of depression on people's Twitter language, we train three transformer-based depression classification models on the dataset, evaluate their performance with progressively increased training sizes, and compare the model's "tweet chunk"-level and user-level performances. Furthermore, inspired by psychological studies, 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. Finally, we demonstrate our model's capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. We hope this study can raise awareness among researchers and the general public of COVID-19's impact on people's mental health.

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arXiv2020

Sense and Sensibility: Characterizing Social Media Users Regarding the Use of Controversial Terms for COVID-19

With the world-wide development of 2019 novel coronavirus, although WHO has officially announced the disease as COVID-19, one controversial term - "Chinese Virus" is still being used by a great number of people. In the meantime, global online media coverage about COVID-19-related racial attacks increases steadily, most of which are anti-Chinese or anti-Asian. As this pandemic becomes increasingly severe, more people start to talk about it on social media platforms such as Twitter. When they refer to COVID-19, there are mainly two ways: using controversial terms like "Chinese Virus" or "Wuhan Virus", or using non-controversial terms like "Coronavirus". In this study, we attempt to characterize the Twitter users who use controversial terms and those who use non-controversial terms. We use the Tweepy API to retrieve 17 million related tweets and the information of their authors. 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 their geo-locations. Moreover, we apply classification models to predict Twitter users who are more likely to use controversial terms. To our best knowledge, this is the first large-scale social media-based study to characterize users with respect to their usage of controversial terms during a major crisis.

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