2020
DOI: 10.1089/cyber.2020.0201
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Effects of Social Grooming on Incivility in COVID-19

Abstract: This study implements a computer-assisted content analysis to identify which social grooming factors reduce social media users' incivility when commenting or posting about the COVID-19 situation in South Korea. In addition, this study conducts semantic network analysis to interpret qualitatively how people express their thoughts. The findings suggest that social network size is a negative predictor of incivility. Moreover, Twitter users who have built larger networks and gained positive responses from others a… Show more

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Cited by 58 publications
(58 citation statements)
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“…Due to panic buying, the demand for essential items further increased, which also disrupted the overall supply chain process of crucial goods (CNN, 2020). Therefore, in this situation, the anxiety of consumers aggravated, and they tilted more towards the impulse buying behavior (Kim 2020). Hence, the following hypothesis is framed based on literature:…”
Section: The Limited Supply Of Essential Goodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Due to panic buying, the demand for essential items further increased, which also disrupted the overall supply chain process of crucial goods (CNN, 2020). Therefore, in this situation, the anxiety of consumers aggravated, and they tilted more towards the impulse buying behavior (Kim 2020). Hence, the following hypothesis is framed based on literature:…”
Section: The Limited Supply Of Essential Goodsmentioning
confidence: 99%
“…Human purchase behavior is the outcome of acquiring information, attitudes and behaviors from other individuals in peerto-peer social interaction in the form of trends and fashions (De Veirman et al, 2017). Since companies attempt to rise to these challenges, they ascertain these changes consumer attitudes and behavior (Kim, 2020). These challenges are conditioned by 1) the companies response to the problem, and 2) changing customer habits and attitudes that will drive others (Bergel & Brock, 2019).…”
Section: Peers Buyingmentioning
confidence: 99%
“…By employing more interpretable features, it becomes more straightforward to describe the social and communicative dynamics which underpin hate speech as it is used in context. This enables researchers to better understand not just which texts may be linked to hate speech, but also how they communicate hate, evolve in communities, and reinforce conflicts [3,33,47,50].…”
Section: Hate Speech On Social Media: From Classification To Charactementioning
confidence: 99%
“…In this work, we show how these methods can be reoriented toward the more situated task of characterizing hate speech in the concrete setting of understanding COVID-19 discourse [53]. Distinct studies have previously focused on the targeted nature of hate speech [2,32,33], its spread in communities [44,47], and the potential role of social bots [69,74]. Our work demonstrates a unified framework for viewing these phenomena as interlinked processes, thereby generating rich insights by examining their interplay.…”
Section: Integrative Approaches To Online Hate and Disinformationmentioning
confidence: 99%
“…As of April 20, 2020, already seven (7) papers on the topic of tracking and forecasting COVID-19 using Google Trends data have been published, according to PubMed (advanced search: covid AND google trends) [22], monitoring, analyzing, or forecasting COVID-19 in several regions like Taiwan [23], China [24][25], Europe [26][27], USA [27][28], Iran [27,29]. Note that for Twitter publications related to the COVID-19 pandemic, eight papers (8) are online up to this point (PubMed advanced search: covid AND twitter [22]), published from March 13 to April 20, 2020 [30][31][32][33][34][35][36][37]. Table 1 consists of the systematic reporting of COVID-19 Google Trends studies, in the order of the reported publication date.…”
Section: Cases Deathsmentioning
confidence: 99%