How gullible are you? predicting susceptibility to fake news

Tracy Jia Shen; Robert Cowell; Aditi Gupta; Thai Le; Amulya Yadav; Dongwon Lee

doi:10.1145/3292522.3326055

How gullible are you? predicting susceptibility to fake news

Tracy Jia Shen, Robert Cowell, Aditi Gupta, Thai Le, Amulya Yadav, Dongwon Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

In this research, we hypothesize that some social users are more gullible to fake news than others, and accordingly investigate on the susceptibility of users to fake news-i.e., how to identify susceptible users, what are their characteristics, and if one can build a prediction model.Building on the crowdsourced annotations of 5 types of susceptible users in Twitter, we found out that: (1) susceptible users are correlated with a combination of user, network, and content features; (2) one can build a reasonably accurate prediction model with 0.82 in AUC-ROC for the multinomial classification task; and (3) there exists a correlation between the dominant susceptibility level of center nodes and that of the entire network.

Original language	English (US)
Title of host publication	WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science
Publisher	Association for Computing Machinery, Inc
Pages	287-288
Number of pages	2
ISBN (Electronic)	9781450362023
DOIs	https://doi.org/10.1145/3292522.3326055
State	Published - Jun 26 2019
Event	11th ACM Conference on Web Science, WebSci 2019 - Boston, United States Duration: Jun 30 2019 → Jul 3 2019

Publication series

Name	WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science

Conference

Conference	11th ACM Conference on Web Science, WebSci 2019
Country	United States
City	Boston
Period	6/30/19 → 7/3/19

All Science Journal Classification (ASJC) codes

Computer Networks and Communications

Cite this

Shen, T. J., Cowell, R., Gupta, A., Le, T., Yadav, A., & Lee, D. (2019). How gullible are you? predicting susceptibility to fake news. In WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science (pp. 287-288). (WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science). Association for Computing Machinery, Inc. https://doi.org/10.1145/3292522.3326055

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title = "How gullible are you? predicting susceptibility to fake news",

abstract = "In this research, we hypothesize that some social users are more gullible to fake news than others, and accordingly investigate on the susceptibility of users to fake news-i.e., how to identify susceptible users, what are their characteristics, and if one can build a prediction model.Building on the crowdsourced annotations of 5 types of susceptible users in Twitter, we found out that: (1) susceptible users are correlated with a combination of user, network, and content features; (2) one can build a reasonably accurate prediction model with 0.82 in AUC-ROC for the multinomial classification task; and (3) there exists a correlation between the dominant susceptibility level of center nodes and that of the entire network.",

author = "Shen, {Tracy Jia} and Robert Cowell and Aditi Gupta and Thai Le and Amulya Yadav and Dongwon Lee",

year = "2019",

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note = "11th ACM Conference on Web Science, WebSci 2019 ; Conference date: 30-06-2019 Through 03-07-2019",

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Shen, TJ, Cowell, R, Gupta, A, Le, T, Yadav, A & Lee, D 2019, How gullible are you? predicting susceptibility to fake news. in WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science. WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science, Association for Computing Machinery, Inc, pp. 287-288, 11th ACM Conference on Web Science, WebSci 2019, Boston, United States, 6/30/19. https://doi.org/10.1145/3292522.3326055

How gullible are you? predicting susceptibility to fake news. / Shen, Tracy Jia; Cowell, Robert; Gupta, Aditi; Le, Thai; Yadav, Amulya ; Lee, Dongwon.

WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science. Association for Computing Machinery, Inc, 2019. p. 287-288 (WebSci 2019 - Proceedings of the 11th ACM Conference on Web Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Access to Document

10.1145/3292522.3326055