activity
20162021
most citedMonitoring Depression Trend on Twitter during the COVID-19 Pandemic

29 citations · 43 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CL2021

Salience-Aware Event Chain Modeling for Narrative Understanding

Xiyang Zhang, Muhao Chen, Jonathan May

Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a con…

cs.SI2020★ 5 cited

Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Dissemination

Hanjia Lyu, Junda Wang, Wei Wu +4

The current development of vaccines for SARS-CoV-2 is unprecedented. Little is known, however, about the nuanced public opinions on the vaccines on social media. We adopt a human-g…

cs.SI2020★ 29 cited

Monitoring Depression Trend on Twitter during the COVID-19 Pandemic

Yipeng Zhang, Hanjia Lyu, Yubao Liu +3

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…

cs.SI2017★ 1 cited

Tactics and Tallies: A Study of the 2016 U.S. Presidential Campaign Using Twitter 'Likes'

Yu Wang, Xiyang Zhang, Jiebo Luo

We propose a framework to measure, evaluate, and rank campaign effectiveness in the ongoing 2016 U.S. presidential election. Using Twitter data collected from Sept. 2015 to Jan. 20…

cs.SI2017★ 1 cited

When Follow is Just One Click Away: Understanding Twitter Follow Behavior in the 2016 U.S. Presidential Election

Yu Wang, Xiyang Zhang, Jiebo Luo

Motivated by the two paradoxical facts that the marginal cost of following one extra candidate is close to zero and that the majority of Twitter users choose to follow only one or…

cs.SI2016★ 1 cited

Tactics and Tallies: Inferring Voter Preferences in the 2016 U.S. Presidential Primaries Using Sparse Learning

Yu Wang, Yang Feng, Xiyang Zhang +1

In this paper, we propose a web-centered framework to infer voter preferences for the 2016 U.S. presidential primaries. Using Twitter data collected from Sept. 2015 to March 2016,…

cs.SI2016★ 1 cited

Gender Politics in the 2016 U.S. Presidential Election: A Computer Vision Approach

Yu Wang, Yang Feng, Xiyang Zhang +1

Gender is playing an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being fr…

cs.SI2016★ 1 cited

Will Sanders Supporters Jump Ship for Trump? Fine-grained Analysis of Twitter Followers

Yu Wang, Yang Feng, Xiyang Zhang +2

In this paper, we study the likelihood of Bernie Sanders supporters voting for Donald Trump instead of Hillary Clinton. Building from a unique time-series dataset of the three cand…

cs.SI2016★ 4 cited

Voting with Feet: Who are Leaving Hillary Clinton and Donald Trump?

Yu Wang, Yuncheng Li, Quanzeng You +3

From a crowded field with 17 candidates, Hillary Clinton and Donald Trump have emerged as the two front-runners in the 2016 U.S. presidential campaign. The two candidates each boas…