Chen Chen
University of Toronto
44 Papers
198 Citations
Chen Chen is an academic researcher from University of Toronto. The author has contributed to research in topics: Computer science & Node (networking). The author has an hindex of 11, co-authored 32 publications. Previous affiliations of Chen Chen include Huawei & IBM.
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Papers
Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach.
TL;DR: This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic.
Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter
TL;DR: In this article, the authors used machine learning techniques to analyze about 1.9 million tweets related to COVID-19 collected from January 23 to March 7, 2020 and found that fear for the unknown nature of the coronavirus is dominant in all topics.
Examining the Impact of COVID-19 Lockdown in Wuhan and Lombardy: A Psycholinguistic Analysis on Weibo and Twitter.
Yue Su,Jia Xue,Xiaoqian Liu,Peijing Wu,Junxiang Chen,Chen Chen,Tianli Liu,Weigang Gong,Tingshao Zhu +8 more
TL;DR: Results showed that individuals focused more on “home”, and expressed a higher level of cognitive process after a lockdown in both Wuhan and Lombardy, while the level of stress decreased, and the attention to leisure increased in Lombardy after the lockdown.
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Abusers indoors and coronavirus outside: an examination of public discourse about COVID-19 and family violence on Twitter
TL;DR: In this paper, a large-scale analysis of public discourse mentioning family violence and the COVID-19 pandemic on Twitter is presented. But the authors focus on the types of family violence (e.g., child abuse, domestic violence, sexual abuse) and risk factors of such forms of violence.
Algorithms based on divide and conquer for topic-based publish/subscribe overlay design
TL;DR: Inspired by the divide-and-conquer character of this idea, a number of algorithms for the original MinAvg-TCO problem are derived that accommodate a variety of practical pub/sub workloads and seek a balance between time efficiency and the number of edges required.
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