Role of Emotion in Excessive Use of Twitter During COVID-19 Imposed Lockdown in India

被引：44

作者：

Arora A. ^{[1
]}

Chakraborty P. ^{[1
]}

Bhatia M.P.S. ^{[1
]}

Mittal P. ^{[2
]}

机构：

[1] Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi

[2] Department of Commerce, Satyawati College (Evening), University of Delhi, Delhi

来源：

Journal of Technology in Behavioral Science | 2021年 / 6卷 / 2期

关键词：

COVID-19; Emotion analysis; Lockdown; Social media addiction; Twitter;

D O I：

10.1007/s41347-020-00174-3

中图分类号：

学科分类号：

摘要：

The COVID-19 pandemic and the lockdowns to contain it are affecting the daily life of people around the world. People are now using digital technologies, including social media, more than ever before. The objectives of this study were to analyze the social media usage pattern of people during the COVID-19 imposed lockdown and to understand the effects of emotion on the same. We scraped messages posted on Twitter by users from India expressing their emotion or view on the pandemic during the first 40 days of the lockdown. We identified the users who posted frequently and analyzed their usage pattern and their overall emotion during the study period based on their tweets. It was observed that 222 users tweeted frequently during the study period. Out of them, 13.5% were found to be addicted to Twitter and posted 13.67 tweets daily on an average (SD: 4.89), while 3.2% were found to be highly addicted and posted 40.71 tweets daily on an average (SD: 9.90) during the study period. The overall emotion of 40.1% of the users was happiness throughout the study period. However, it was also observed that users who tweeted more frequently were typically angry, disgusted, or sad about the prevailing situation. We concluded that people with a negative sentiment are more susceptible to addictive use of social media. © 2020, Springer Nature Switzerland AG.

引用

页码：370 / 377

页数：7

共 41 条

[1]

Ahmed M.Z., Ahmed O., Aibao Z., Hanbin S., Siyu L., Ahmad A., Epidemic of COVID-19 in China and associated psychological problems, Asian Journal of Psychiatry, 51, (2020)

[2]

Almeida H., Briand A., Meurs M.J., Detecting early risk of depression from social media user-generated content, In: Working Notes of the Conference and Labs of the Evaluation Forum, Article, (2017)

[3]

Althubaiti A., Information bias in health research: definition, pitfalls, and adjustment methods, Journal of Multidisciplinary Healthcare, 9, pp. 211-217, (2016)

[4]

Andreassen C.S., Torsheim T., Brunborg G.S., Pallesen S., Development of a Facebook addiction scale, Psychological Reports, 110, pp. 501-517, (2012)

[5]

Andreassen C.S., Pallesen S., Griffiths M.D., The relationship between addictive use of social media, narcissism, and self-esteem: findings from a large national survey, Addictive Behaviors, 64, pp. 287-293, (2017)

[6]

Arora A., Chakraborty P., Bhatia M.P.S., Problematic use of digital technologies and its impact on mental health during COVID-19 pandemic: Assessment using machine learning, Emerging Technologies during the Era of COVID-19 Pandemic, Accepted., (2020)

[7]

Barkur G., Vibha, Kamath G.B., Sentiment analysis of nationwide lockdown due to COVID-19 outbreak: evidence from India, Asian Journal of Psychiatry, 51, (2020)

[8]

Chung K.L., Morshidi I., Yoong L.C., Thian K.N., The role of the dark tetrad and impulsivity in social media addiction: findings from Malaysia, Personality and Individual Differences, 143, pp. 62-67, (2019)

[9]

Dalvi-Esfahani M., Niknafs A., Kuss D.J., Nilashi M., Afrough S., Social media addiction: applying the DEMATEL approach, Telematics and Informatics, 43, (2019)

[10]

de Choudhury M., Gamon M., Counts S., Horvitz E., Predicting depression via social media, In: Proceedings of the International AAAI Conference on Web and Social Media, pp. 12-137, (2013)

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