TY - GEN
T1 - Understanding User Profiles on Social Media for Fake News Detection
AU - Shu, Kai
AU - Wang, Suhang
AU - Liu, Huan
N1 - Funding Information:
We thank the reviewers for insightful feedbacks. This material is based upon work supported by, or in part by, the ONR grant N00014-16-1-2257.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - 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.
AB - 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.
KW - Fake News
KW - Trust Analysis
KW - User Profile
UR - http://www.scopus.com/inward/record.url?scp=85050097165&partnerID=8YFLogxK
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U2 - 10.1109/MIPR.2018.00092
DO - 10.1109/MIPR.2018.00092
M3 - Conference contribution
AN - SCOPUS:85050097165
T3 - Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
SP - 430
EP - 435
BT - Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018
Y2 - 10 April 2018 through 12 April 2018
ER -