This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1274) Natural Language Processing (644) JMIR Theme Issue: COVID-19 Special Issue (2391) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1013) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1570)

Published on in Vol 24, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38776, first published .
Increased Online Aggression During COVID-19 Lockdowns: Two-Stage Study of Deep Text Mining and Difference-in-Differences Analysis

Increased Online Aggression During COVID-19 Lockdowns: Two-Stage Study of Deep Text Mining and Difference-in-Differences Analysis

Increased Online Aggression During COVID-19 Lockdowns: Two-Stage Study of Deep Text Mining and Difference-in-Differences Analysis

Authors of this article:

Jerome Tze-Hou Hsu1, 2 Author Orcid Image ;   Richard Tzong-Han Tsai1, 3 Author Orcid Image
Article Authors Cited by (4) Tweetations (5) Metrics

Journals

  1. Fujii S, Kunii Y, Nonaka S, Hamaie Y, Hino M, Egawa S, Kuriyama S, Tomita H. Real-Time Prediction of Medical Demand and Mental Health Status in Ukraine under Russian Invasion Using Tweet Analysis. The Tohoku Journal of Experimental Medicine 2023;259(3):177 View
  2. Xia X, Zhang Y, Jiang W, Wu C. Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders. Journal of Medical Internet Research 2023;25:e45757 View
  3. Brown J, Barringer A, Kouros C, Papp L. Examining enduring effects of COVID-19 on college students' internalizing and externalizing problems: A four-year longitudinal analysis. Journal of Affective Disorders 2024;351:551 View
  4. Yao A, Zhu M, Li L. Psychological experience of university students during prolonged quarantine in China: a qualitative study. BMJ Open 2024;14(3):e077483 View