A social media analytics application of impression management and social presence theories to Twitter interaction analysis

• Published in: Decision analytics journal 2023-12, Vol.9, p.100321, Article 100321
• Main Authors: Mehra, Vishal, Singh, Prabhsimran, Bharany, Salil, Sawhney, Ravinder Singh
• Format: Article
• Language: English
• Subjects: Impression Management Theory; Self-censorship; Social media; Social Presence Theory; Sports; Twitter; Weibo
• ISSN: 2772-6622; 2772-6622
• DOI: 10.1016/j.dajour.2023.100321

This study uses social media analytics to analyze Twitter users’ views, opinions, and sentiments towards the Winter Olympics 2022. Impression Management Theory (IMT) and Social Presence Theory (SPT) have been utilized to analyze the imposition of the decision to practice self-censorship to sports fans, spectators, and athletes for sharing Winter Olympics content over social media channels other than Weibo. A total of 3270 tweets were collected using the combination of a keyword and a hashtag “Weibo” and “#Beijing2022”. The findings suggest that Twitter users chose to remain vocal against the ban and disregarded the idea of self-censorship, as advised by Weibo. The results of sentiment analysis show overall positive polarity. After the warning from Weibo, the polarity peaked towards the negative value, and emotions such as anger, anticipation, fear, and surprise were observed. Despite a warning from Weibo, sports fans shared Winter Olympics content on Twitter, and a few links were blocked. This paper contributes to using social media in sports as a freer space to let users decide about sharing content over their preferred social media platforms. •Examine the impression management practices by Social Media organization, their effects, and their impact on sports fans.•Apply impression management theory and social presence theory to correlate the simultaneous interactions among Twitter users.•Use statistical and sentiment analysis of the Tweets to understand the psychological impact of imposed warnings.•Geo-location analysis of Tweets to find the highly engaged countries.•Social media analytics of the tweets to identify the hot topics of discussion.