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  • 标题:Opinion Spam Detection based on Annotation Extension and Neural Networks
  • 本地全文:下载
  • 作者:Yuanchao Liu ; Bo Pang
  • 期刊名称:Computer and Information Science
  • 印刷版ISSN:1913-8989
  • 电子版ISSN:1913-8997
  • 出版年度:2019
  • 卷号:12
  • 期号:2
  • 页码:87-102
  • DOI:10.5539/cis.v12n2p87
  • 出版社:Canadian Center of Science and Education
  • 摘要:Facebook has become indispensable in social interactions. Unmarried users may find a date or life partner by uploading attractive photos of themselves or messaging their crushes. This study developed the Daily Facebook Addiction Scale (DFAS), which focuses on using mobile devices to access Facebook. The aims were explored how flow experience is created based on the self-traits of Facebook users and analyzed the relationship between flow experience and Facebook addiction. Data was obtained 401 participants through the Internet, in total, 231 were addicted to Facebook, that is, they accessed it for >2 hours a day. This study indicated: (1) users’ concentration and interactivity had a positive effect on creating flow experience but enjoyment did not. (2) Respondents’ flow experiences had a significant effect on Facebook addiction. (3) The subfactors of a respondent’s self-traits individually had positive effects on flow experience and Facebook addiction, and self-control generated the most significant effect. Three antecedents, namely self-traits, flow experience, and Facebook addiction, do indeed affect each other.
  • 关键词:opinion spam detection; annotation extension; neural networks
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