Consuming news from social media is becoming increasingly popular nowadays. Social media brings benefits to users due to the inherent nature of fast dissemination, cheap cost, and easy access. However, the quality of news is considered lower than traditional news outlets, resulting in large amounts of fake news. Detecting fake news becomes very important and is attracting increasing attention due to the detrimental effects on individuals and the society. The performance of detecting fake news only from content is generally not satisfactory, and it is suggested to incorporate user social engagements as auxiliary information to improve fake news detection. Thus it necessitates an in-depth understanding of the correlation between user profiles on social media and fake news. In this paper, we construct real-world datasets measuring users trust level on fake news and select representative groups of both "experienced" users who are able to recognize fake news items as false and "naïve" users who are more likely to believe fake news. We perform a comparative analysis over explicit and implicit profile features between these user groups, which reveals their potential to differentiate fake news. The findings of this paper lay the foundation for future automatic fake news detection research.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538618578
StatePublished - Jun 26 2018
Event1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018 - Miami, United States
Duration: Apr 10 2018Apr 12 2018


Other1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018
CountryUnited States


  • Fake News
  • Trust Analysis
  • User Profile

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Media Technology

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