World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
5190
World Ranking
10846
National Ranking
1341

Tingshao Zhu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Tingshao Zhu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 206 publications — 48th percentile

48% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Tingshao Zhu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Tingshao Zhu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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
Gerald Häubl
Gerald Häubl 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
Antonio Chirumbolo
Antonio Chirumbolo Sapienza University of Rome
Koen Ponnet
Koen Ponnet Ghent University
Piotr Sorokowski
Piotr Sorokowski University of Wrocław

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