Fake news detection: An interdisciplinary research

Xinyi Zhou; Reza Zafarani

doi:10.1145/3308560.3316476

Fake news detection: An interdisciplinary research

Xinyi Zhou, Reza Zafarani

Syracuse University

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

26 Scopus citations

Abstract

The explosive growth of fake news and its erosion to democracy, journalism and economy has increased the demand for fake news detection. To achieve efficient and explainable fake news detection, an interdisciplinary approach is required, relying on scientific contributions from various disciplines, e.g., social sciences, engineering, among others. Here, we illustrate how such multidisciplinary contributions can help detect fake news by improving feature engineering, or by providing well-justified machine learning models. We demonstrate how news content, news propagation patterns, and users' engagements with news can help detect fake news.

Original language	English
Title of host publication	The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019
Pages	1292
Number of pages	1
ISBN (Electronic)	9781450366755
DOIs	https://doi.org/10.1145/3308560.3316476
State	Published - 2019
Externally published	Yes
Event	2019 World Wide Web Conference, WWW 2019 - San Francisco, United States Duration: 13 May 2019 → 17 May 2019

Publication series

Name	The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019

Conference

Conference	2019 World Wide Web Conference, WWW 2019
Country/Territory	United States
City	San Francisco
Period	13/05/19 → 17/05/19

Keywords

Disinformation
Fake news detection
Fake news research
False news
Misinformation

Access to Document

10.1145/3308560.3316476

Cite this

@inproceedings{7cf36d6377d641c68c34bb12e01aaa25,

title = "Fake news detection: An interdisciplinary research",

abstract = "The explosive growth of fake news and its erosion to democracy, journalism and economy has increased the demand for fake news detection. To achieve efficient and explainable fake news detection, an interdisciplinary approach is required, relying on scientific contributions from various disciplines, e.g., social sciences, engineering, among others. Here, we illustrate how such multidisciplinary contributions can help detect fake news by improving feature engineering, or by providing well-justified machine learning models. We demonstrate how news content, news propagation patterns, and users' engagements with news can help detect fake news.",

keywords = "Disinformation, Fake news detection, Fake news research, False news, Misinformation",

author = "Xinyi Zhou and Reza Zafarani",

note = "Publisher Copyright: {\textcopyright} 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.; 2019 World Wide Web Conference, WWW 2019 ; Conference date: 13-05-2019 Through 17-05-2019",

year = "2019",

doi = "10.1145/3308560.3316476",

language = "English",

series = "The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019",

pages = "1292",

booktitle = "The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019",

}

Zhou, X & Zafarani, R 2019, Fake news detection: An interdisciplinary research. in The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019. The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, pp. 1292, 2019 World Wide Web Conference, WWW 2019, San Francisco, United States, 13/05/19. https://doi.org/10.1145/3308560.3316476

TY - GEN

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AU - Zhou, Xinyi

AU - Zafarani, Reza

PY - 2019

Y1 - 2019

N2 - The explosive growth of fake news and its erosion to democracy, journalism and economy has increased the demand for fake news detection. To achieve efficient and explainable fake news detection, an interdisciplinary approach is required, relying on scientific contributions from various disciplines, e.g., social sciences, engineering, among others. Here, we illustrate how such multidisciplinary contributions can help detect fake news by improving feature engineering, or by providing well-justified machine learning models. We demonstrate how news content, news propagation patterns, and users' engagements with news can help detect fake news.

AB - The explosive growth of fake news and its erosion to democracy, journalism and economy has increased the demand for fake news detection. To achieve efficient and explainable fake news detection, an interdisciplinary approach is required, relying on scientific contributions from various disciplines, e.g., social sciences, engineering, among others. Here, we illustrate how such multidisciplinary contributions can help detect fake news by improving feature engineering, or by providing well-justified machine learning models. We demonstrate how news content, news propagation patterns, and users' engagements with news can help detect fake news.

KW - Disinformation

KW - Fake news detection

KW - Fake news research

KW - False news

KW - Misinformation

UR - https://www.scopus.com/pages/publications/85066888553

U2 - 10.1145/3308560.3316476

DO - 10.1145/3308560.3316476

M3 - Conference contribution

AN - SCOPUS:85066888553

T3 - The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019

SP - 1292

BT - The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019

Y2 - 13 May 2019 through 17 May 2019

ER -

Fake news detection: An interdisciplinary research

Abstract

Publication series

Conference

Keywords

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