TY - GEN
T1 - A first step towards combating fake news over online social media
AU - Xu, Kuai
AU - Wang, Feng
AU - Wang, Haiyan
AU - Yang, Bo
N1 - Funding Information:
Acknowledgements. This work was supported in part by National Science Foundation Algorithms for Threat Detection (ATD) Program under the grant DMS #1737861.
Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
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 Facebook from two perspectives: Web sites and content. Our site analysis reveals that the Web sites of the fake and real news publishers exhibit diverse registration behaviors and registration timing. 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 the Web sites 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 Facebook from two perspectives: Web sites and content. Our site analysis reveals that the Web sites of the fake and real news publishers exhibit diverse registration behaviors and registration timing. 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 the Web sites and content characteristics of fake and real news, which will provide key insights for effectively detecting fake news on social media.
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U2 - 10.1007/978-3-319-94268-1_43
DO - 10.1007/978-3-319-94268-1_43
M3 - Conference contribution
AN - SCOPUS:85049027465
SN - 9783319942674
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 521
EP - 531
BT - Wireless Algorithms, Systems, and Applications - 13th International Conference, WASA 2018, Proceedings
A2 - Cheng, Wei
A2 - Li, Wei
A2 - Chellappan, Sriram
PB - Springer Verlag
T2 - 13th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2018
Y2 - 20 June 2018 through 22 June 2018
ER -