CoTrRank

trust ranking on Twitter

Peiyao Li, Weiliang Zhao*, Jian Yang, Jia Wu

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Trust evaluation of people and information on social media is critical for maintaining a healthy online social environment. How to evaluate the trustworthiness of users and tweets is challenging due to the complex and complicated relationships between/among users and their posts. As existing approaches use a single network to represent users, posts, and their relationships, they have the limitation to reflect the different statistical features of users and tweets, which has reduced the ability to determine the trustworthiness of users and tweets. To address this issue, we develop a trust evaluation method that models users and tweets separately in two networks that are coupled with each other via interactions. We provide mapping functions to map the statistical numbers of actions of users/tweets to trust values that indicate their relevant trust degrees. The proposed method provides a configurable solution that has the capability to consider the effects of users and tweets differently in different trust ranking situations. A set of experiments are conducted against real data collected from Twitter. The experimental results show that the proposed approach is more effective in trust evaluation compared with several baseline methods.

Original languageEnglish
JournalIEEE Intelligent Systems
DOIs
Publication statusE-pub ahead of print - 15 Dec 2020

Keywords

  • Complicated Relationships
  • Coupled Dual Networks
  • Coupling Effect
  • Interactions
  • Trust
  • Twitter

Fingerprint Dive into the research topics of 'CoTrRank: trust ranking on Twitter'. Together they form a unique fingerprint.

Cite this