TY - JOUR
T1 - Detecting fake news over online social media via domain reputations and content understanding
AU - Xu, Kuai
AU - Wang, Feng
AU - Wang, Haiyan
AU - Yang, Bo
N1 - Funding Information:
This work was supported in part by National Science Foundation (NSF) Algorithms for Threat Detection (ATD) Program (No. DMS #1737861) and NSF Computer and Network Systems (CNS) Program (No. CNS #1816995).
Publisher Copyright:
© 1996-2012 Tsinghua University Press.
PY - 2020/2
Y1 - 2020/2
N2 - Fake news has recently leveraged the power and scale of online social media to effectively spread misinformation which not only erodes the trust of people on traditional presses and journalisms, but also manipulates the opinions and sentiments of the public. Detecting fake news is a daunting challenge due to subtle difference between real and fake news. As a first step of fighting with fake news, this paper characterizes hundreds of popular fake and real news measured by shares, reactions, and comments on Facebookfrom two perspectives: domain reputations and content understanding. Our domain reputation analysis reveals that the Web sites of the fake and real news publishers exhibit diverse registration behaviors, registration timing, domain rankings, and domain popularity. In addition, fake news tends to disappear from the Web after a certain amount of time. The content characterizations on the fake and real news corpus suggest that simply applying term frequency-inverse document frequency (tf-idf) and Latent Dirichlet Allocation (LDA) topic modeling is inefficient in detecting fake news, while exploring document similarity with the term and word vectors is a very promising direction for predicting fake and real news. To the best of our knowledge, this is the first effort to systematically study domain reputations and content characteristics of fake and real news, which will provide key insights for effectively detecting fake news on social media.
AB - Fake news has recently leveraged the power and scale of online social media to effectively spread misinformation which not only erodes the trust of people on traditional presses and journalisms, but also manipulates the opinions and sentiments of the public. Detecting fake news is a daunting challenge due to subtle difference between real and fake news. As a first step of fighting with fake news, this paper characterizes hundreds of popular fake and real news measured by shares, reactions, and comments on Facebookfrom two perspectives: domain reputations and content understanding. Our domain reputation analysis reveals that the Web sites of the fake and real news publishers exhibit diverse registration behaviors, registration timing, domain rankings, and domain popularity. In addition, fake news tends to disappear from the Web after a certain amount of time. The content characterizations on the fake and real news corpus suggest that simply applying term frequency-inverse document frequency (tf-idf) and Latent Dirichlet Allocation (LDA) topic modeling is inefficient in detecting fake news, while exploring document similarity with the term and word vectors is a very promising direction for predicting fake and real news. To the best of our knowledge, this is the first effort to systematically study domain reputations and content characteristics of fake and real news, which will provide key insights for effectively detecting fake news on social media.
KW - content modeling
KW - domain reputations
KW - fake news detection
KW - social media
UR - http://www.scopus.com/inward/record.url?scp=85069524460&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069524460&partnerID=8YFLogxK
U2 - 10.26599/TST.2018.9010139
DO - 10.26599/TST.2018.9010139
M3 - Article
AN - SCOPUS:85069524460
SN - 1007-0214
VL - 25
SP - 20
EP - 27
JO - Tsinghua Science and Technology
JF - Tsinghua Science and Technology
IS - 1
M1 - 8768083
ER -