• Title/Summary/Keyword: uncivil comments

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Compliance to Feedback on Uncivil Comments in a Virtual Online News Portal: The Role of Avatar Presence (가상 온라인 기사 포털에서 아바타의 존재와 반시민적 댓글 피드백에 대한 행동 순응)

  • YounJung Park;HeeJo Keum;SeYoung Lee
    • The Journal of the Convergence on Culture Technology
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    • v.10 no.1
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    • pp.419-425
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    • 2024
  • As digital communication gains prominence, there is an increasing trend in uncivil behaviors like rude or hateful comments and the empathetic actions towards them, highlighting the need for social efforts to address these issues. As part of these endeavors, we investigated how avatar feedback in a virtual news portal affects users' empathy towards uncivil comments. We defined both posting and empathizing with uncivil comments as antisocial actions. To this end, we posted socially controversial news in a virtual space and provided feedback in two forms when participants selected uncivil comments: text-only feedback and feedback accompanied by an avatar. We then assessed the impact of this feedback on behavioral conformity, guilt, and self-image concern through surveys. Our results showed that avatar-provided feedback significantly influenced participants' social responses more than text-based feedback. Interaction with avatars notably increased participants' behavioral conformity, guilt, and self-image concern. We concluded that avatar-based interactions can positively influence users' social behaviors and attitudes, suggesting their potential in fostering a more civil and responsible digital communication culture.

The Effects of Online Uncivil Comments on Vicarious shame and Coping Strategies: Focusing on the Power of Social Identity and Social Recommendation

  • Kim, Jiwon
    • Journal of Internet Computing and Services
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    • v.21 no.1
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    • pp.119-125
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    • 2020
  • Based on an online experiment, this research examined how uncivil expressions made by participants from the same political partisan group (in-group) influenced the emotional and behavioral intentions of other in-group members, especially when the incivility was supported by social recommendations such as "recommendations." As predicted, results showed that a higher level of vicarious shame was felt when participants perceived higher levels of incivility. However, no significant effects of social recommendations were found regarding levels of vicarious shame. That is, the level of shame was not significantly different between participants who were exposed to an in-group uncivil comment that received recommendations and participants who were exposed to in-group uncivil comment without recommendations. Findings further found two types of coping strategies -situation-reparation and situation-avoidance - among participants exposed to in-group uncivil comments. Yet no significant effects were found regarding coping strategies in response to the presence of social recommendations. Participants' feelings of shame were positively correlated with both types of coping strategies, supporting findings of previous studies. Implications of this study are further discussed.

Detection of Incivility based on Attention-embedding and multi-channel CNN (어텐션임베딩과 다채널 CNN 기반 반시민성 검출 알고리즘)

  • Park, Youn-Jung;Lee, Se-Young;Keum, Hee-Jo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.12
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    • pp.1880-1889
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    • 2022
  • The online portal platform provides online news with online comments, but the anonymity of comments causes incivility, and online comments are considered social problems. While there are many foreign language-based incivility detection studies, in-depth research is not being conducted in Korea since there has not been implemented Korean language dataset which is labeled detailed criteria of incivility. In this study, the incivility notation of comments was conducted in a total of 13 items, uncivil words were summarized. Furthermore, Attention algorithm was applied to each comment and summary to extract embedding vectors. 2-d CNN followed at the end to detect incivility in given data. As a result, we showed that the proposed algorithm is useful for anti-citizen detection such as name-calling and offensive tones. This study is expected to contribute to the formation of a healthy online comment culture by detecting uncivil comments which hinder democratic discourse.