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Understanding Social Interaction across Social Network Sites

机译:了解社交网站的社交互动

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摘要

People tend to utilize multiple social network sites (SNSs) simultaneously to maintain some social relationships, which results that there are many overlapping relationships and interactions among SNSs. Although many studies have focused on social interaction and cross-SNS user footprint analysis and understanding, little research investigates social interaction from a perspective of two or more SNSs, and the interplay of interaction among SNSs has been unknown. In this paper, we aim to explore whether interaction building in a new SNS hinders the interaction frequency in an existing site, and if so, what kinds of users and relationships' interactions are more or less likely to be affected. For these questions, we sampled 7,015 pairs of overlapping identities, 23,590 pairs of overlapping relationships and 6,771 pairs of overlapping interactions from Weibo and Douban and made analysis by combining multiple methods like Regression Discontinuity Design and Random-effects Negative Binomial Regression model. Our results suggest that no matter from the perspective of individuals or from the perspective of relationships, interaction construction in a new SNS is detrimental to interaction frequency in an existing site. Based on our findings, we also propose several valuable insights about how to enhance social interaction and promote its retention when users are involved into interacting practice in multiple platforms.
机译:人们倾向于同时利用多个社交网站(SNSS)来维持一些社会关系,这导致SNS之间存在许多重叠关系和相互作用。虽然许多研究侧重于社交互动和跨SNS用户足迹分析和理解,但很少的研究从两个或多个SNSS的角度调查了社会互动,并且SNS之间的相互作用是未知的。在本文中,我们的目标是探讨新SNS中的交互构建是否阻碍了现有网站中的互动频率,如果是的话,用户和关系的互动或多或少可能受到影响。对于这些问题,我们采样了7,015对重叠的身份,23,590对与微博和辅导的6,771对重叠的相互作用,并通过组合回归不连续性设计和随机效应负二进制回归模型进行分析。我们的研究结果表明,无论从个人的角度或从关系的角度来看,新SNS中的相互作用建设对现有网站中的相互作用频率有害。根据我们的调查结果,我们还提出了有关如何提高社会互动的有价值的见解,并在用户参与多个平台中的互动实践时促进其保留。

著录项

  • 来源
    《International Journal of Human-Computer Interaction》 |2020年第20期|1818-1833|共16页
  • 作者单位

    Fudan Univ Sch Comp Sci Shanghai Peoples R China;

    Fudan Univ Sch Comp Sci Shanghai Peoples R China;

    Fudan Univ Sch Comp Sci Shanghai Peoples R China;

    Amazon Seattle WA USA;

    Fudan Univ Sch Comp Sci Shanghai Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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