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Computer Science

D-Index
37
Citations
5190
World Ranking
10848
National Ranking
1341

Overview

Tingshao Zhu is affiliated with the University of Chinese Academy of Sciences in China. Their research spans various domains within psychology and social sciences, with a focus on social psychology, clinical psychology, sociology and political science, experimental and cognitive psychology, and artificial intelligence.

The main topical areas of their research include mental health via writing, COVID-19 and mental health, misinformation and its impacts, mental health research topics broadly, digital mental health interventions, vaccine coverage and hesitancy, as well as suicide and self-harm studies.

Among recent publications authored by or associated with Zhu are:

  • The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users (2020, International Journal of Environmental Research and Public Health)
  • Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach (2020, Journal of Medical Internet Research)
  • Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter (2020, PLoS ONE)
  • The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets (2020, Journal of Medical Internet Research)
  • How fear and collectivism influence public's preventive intention towards COVID-19 infection: a study based on big data from the social media (2020, BMC Public Health)

Zhu frequently collaborates with several co-authors, including:

  • Xiaoqian Liu
  • Sijia Li
  • Jia Xue
  • Feng Huang
  • Ang Li

The primary publication venues for Zhu's work include the International Journal of Environmental Research and Public Health and Frontiers in Psychology, each with nine publications, followed by the Journal of Medical Internet Research with eight publications, Frontiers in Psychiatry with six, and Human Behavior and Emerging Technologies with five.

Best Publications

  • Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach.

    Jia Xue;Junxiang Chen;Ran Hu;Chen Chen

  • Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

    Jia Xue;Junxiang Chen;Chen Chen;Chengda Zheng

  • Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study.

    Qijin Cheng;Tim M. H. Li;Chi-Leung Kwok;Tingshao Zhu

  • Developing Simplified Chinese Psychological Linguistic Analysis Dictionary for Microblog

    Rui Gao;Bibo Hao;He Li;Yusong Gao

  • Predicting Active Users' Personality Based on Micro-Blogging Behaviors

    Lin Li;Ang Li;Bibo Hao;Zengda Guan

  • The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets.

    Jia Xue;Junxiang Chen;Chen Chen;Ran Hu

  • How fear and collectivism influence public's preventive intention towards COVID-19 infection: a study based on big data from the social media.

    Feng Huang;Huimin Ding;Zeyu Liu;Peijing Wu

  • Learning a model of a web user's interests

    Tingshao Zhu;Russ Greiner;Gerald Häubl

  • 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

  • Evaluating the Validity of Simplified Chinese Version of LIWC in Detecting Psychological Expressions in Short Texts on Social Network Services

    Nan Zhao;Dongdong Jiao;Shuotian Bai;Tingshao Zhu

  • Detecting depression stigma on social media: A linguistic analysis.

    Ang Li;Ang Li;Dongdong Jiao;Tingshao Zhu

  • Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users

    Lei Zhang;Lei Zhang;Xiaolei Huang;Tianli Liu;Ang Li

  • Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons

    Xiaolei Huang;Lei Zhang;David Chiu;Tianli Liu

  • Emotion recognition based on customized smart bracelet with built-in accelerometer

    Zhan Zhang;Yufei Y Song;Liqing Cui;Xiaoqian Liu

  • Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model

    Li Guan;Bibo Hao;Qijin Cheng;Paul Sf Yip

  • Improving user profile with personality traits predicted from social media content

    Rui Gao;Bibo Hao;Shuotian Bai;Lin Li

  • Acoustic differences between healthy and depressed people: a cross-situation study

    Jingying Wang;Lei Zhang;Tianli Liu;Wei Pan

  • Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors

    Xingyun Liu;Xingyun Liu;Xiaoqian Liu;Jiumo Sun;Nancy Xiaonan Yu

  • Creating a Chinese suicide dictionary for identifying suicide risk on social media

    Meizhen Lv;Meizhen Lv;Ang Li;Ang Li;Tianli Liu;Tingshao Zhu

  • Emotion recognition using Kinect motion capture data of human gaits

    Shun Li;Liqing Cui;Changye Zhu;Baobin Li

  • Predicting Big Five Personality Traits of Microblog Users.

    Shuotian Bai;Bibo Hao;Ang Li;Sha Yuan

Frequent Co-Authors

Russell Greiner
Russell Greiner University of Alberta
Lei Zhang
Lei Zhang Hong Kong Polytechnic University
Bin Hu
Bin Hu Lanzhou University
Dong Nie
Dong Nie University of North Carolina at Chapel Hill
Paul S. F. Yip
Paul S. F. Yip University of Hong Kong
Jonathan Flint
Jonathan Flint University of California, Los Angeles
Michel Walrave
Michel Walrave University of Antwerp
Koen Ponnet
Koen Ponnet Ghent University
Antonio Chirumbolo
Antonio Chirumbolo Sapienza University of Rome
Piotr Sorokowski
Piotr Sorokowski University of Wrocław

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