Suspicious news detection using micro blog text

Tsubasa Tagami; Hiroki Ouchi; Hiroki Asano; Kazuaki Hanawa; Kaori Uchiyama; Kaito Suzuki; Kentaro Inui; Atsushi Komiya; Atsuo Fujimura; Hitofumi Yanai; Ryo Yamashita; Akinori MacHino

Suspicious news detection using micro blog text

Tsubasa Tagami, Hiroki Ouchi, Hiroki Asano, Kazuaki Hanawa, Kaori Uchiyama, Kaito Suzuki, Kentaro Inui, Atsushi Komiya, Atsuo Fujimura, Hitofumi Yanai, Ryo Yamashita, Akinori MacHino

INF - System Information Sciences

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

We present a new task, suspicious news detection using micro blog text. This task aimsto support human experts to detect suspiciousnews articles to be verified, which is costly buta crucial step before verifying the truthfulnessof the articles. Specifically, in this task, givena set of posts on SNS referring to a news article, the goal is to judge whether the articleis suspicious or not. For this task, we create apublicly available dataset in Japanese and provide benchmark results by using several basic machine learning techniques. Experimental results show that our models can reduce thecost of manual fact-checking process.

Original language	English
Pages	648-657
Number of pages	10
Publication status	Published - 2018
Event	32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018 - Hong Kong, Hong Kong Duration: 2018 Dec 1 → 2018 Dec 3

Conference

Conference	32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018
Country/Territory	Hong Kong
City	Hong Kong
Period	18/12/1 → 18/12/3

Cite this

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title = "Suspicious news detection using micro blog text",

abstract = "We present a new task, suspicious news detection using micro blog text. This task aimsto support human experts to detect suspiciousnews articles to be verified, which is costly buta crucial step before verifying the truthfulnessof the articles. Specifically, in this task, givena set of posts on SNS referring to a news article, the goal is to judge whether the articleis suspicious or not. For this task, we create apublicly available dataset in Japanese and provide benchmark results by using several basic machine learning techniques. Experimental results show that our models can reduce thecost of manual fact-checking process.",

author = "Tsubasa Tagami and Hiroki Ouchi and Hiroki Asano and Kazuaki Hanawa and Kaori Uchiyama and Kaito Suzuki and Kentaro Inui and Atsushi Komiya and Atsuo Fujimura and Hitofumi Yanai and Ryo Yamashita and Akinori MacHino",

note = "Funding Information: This work was supported by JSPS KAKENHI Grant Number JP15H01702.We thank the anonymous reviewers for many helpful advices and comments. Publisher Copyright: {\textcopyright} 2018 by the authors.; 32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018 ; Conference date: 01-12-2018 Through 03-12-2018",

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language = "English",

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TY - CONF

T1 - Suspicious news detection using micro blog text

AU - Tagami, Tsubasa

AU - Ouchi, Hiroki

AU - Asano, Hiroki

AU - Hanawa, Kazuaki

AU - Uchiyama, Kaori

AU - Suzuki, Kaito

AU - Inui, Kentaro

AU - Komiya, Atsushi

AU - Fujimura, Atsuo

AU - Yanai, Hitofumi

AU - Yamashita, Ryo

AU - MacHino, Akinori

N1 - Funding Information: This work was supported by JSPS KAKENHI Grant Number JP15H01702.We thank the anonymous reviewers for many helpful advices and comments. Publisher Copyright: © 2018 by the authors.

PY - 2018

Y1 - 2018

N2 - We present a new task, suspicious news detection using micro blog text. This task aimsto support human experts to detect suspiciousnews articles to be verified, which is costly buta crucial step before verifying the truthfulnessof the articles. Specifically, in this task, givena set of posts on SNS referring to a news article, the goal is to judge whether the articleis suspicious or not. For this task, we create apublicly available dataset in Japanese and provide benchmark results by using several basic machine learning techniques. Experimental results show that our models can reduce thecost of manual fact-checking process.

AB - We present a new task, suspicious news detection using micro blog text. This task aimsto support human experts to detect suspiciousnews articles to be verified, which is costly buta crucial step before verifying the truthfulnessof the articles. Specifically, in this task, givena set of posts on SNS referring to a news article, the goal is to judge whether the articleis suspicious or not. For this task, we create apublicly available dataset in Japanese and provide benchmark results by using several basic machine learning techniques. Experimental results show that our models can reduce thecost of manual fact-checking process.

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M3 - Paper

AN - SCOPUS:85090186614

SP - 648

EP - 657

T2 - 32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018

Y2 - 1 December 2018 through 3 December 2018

ER -

Suspicious news detection using micro blog text

Abstract

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