Detecting rumors from microblogs with recurrent neural networks

Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam Fai Wong, Meeyoung Cha

Research output: Contribution to journalConference article

98 Citations (Scopus)

Abstract

Microblogging platforms are an ideal place for spreading rumors and automatically debunking rumors is a crucial problem. To detect rumors, existing approaches have relied on hand-crafted features for employing machine learning algorithms that require daunting manual effort. Upon facing a dubious claim, people dispute its truthfulness by posting various cues over time, which generates long-distance dependencies of evidence. This paper presents a novel method that learns continuous representations of microblog events for identifying rumors. The proposed model is based on recurrent neural networks (RNN) for learning the hidden representations that capture the variation of contextual information of relevant posts over time. Experimental results on datasets from two real-world microblog platforms demonstrate that (1) the RNN method outperforms state-of-the-art rumor detection models that use hand-crafted features; (2) performance of the RNN-based algorithm is further improved via sophisticated recurrent units and extra hidden layers; (3) RNN-based method detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.

Original languageEnglish (US)
Pages (from-to)3818-3824
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
Volume2016-January
StatePublished - Jan 1 2016
Event25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States
Duration: Jul 9 2016Jul 15 2016

Fingerprint

Recurrent neural networks
Learning algorithms
Learning systems

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Ma, Jing ; Gao, Wei ; Mitra, Prasenjit ; Kwon, Sejeong ; Jansen, Bernard J. ; Wong, Kam Fai ; Cha, Meeyoung. / Detecting rumors from microblogs with recurrent neural networks. In: IJCAI International Joint Conference on Artificial Intelligence. 2016 ; Vol. 2016-January. pp. 3818-3824.
@article{30c0de09011b4b0c91704bfe29142828,
title = "Detecting rumors from microblogs with recurrent neural networks",
abstract = "Microblogging platforms are an ideal place for spreading rumors and automatically debunking rumors is a crucial problem. To detect rumors, existing approaches have relied on hand-crafted features for employing machine learning algorithms that require daunting manual effort. Upon facing a dubious claim, people dispute its truthfulness by posting various cues over time, which generates long-distance dependencies of evidence. This paper presents a novel method that learns continuous representations of microblog events for identifying rumors. The proposed model is based on recurrent neural networks (RNN) for learning the hidden representations that capture the variation of contextual information of relevant posts over time. Experimental results on datasets from two real-world microblog platforms demonstrate that (1) the RNN method outperforms state-of-the-art rumor detection models that use hand-crafted features; (2) performance of the RNN-based algorithm is further improved via sophisticated recurrent units and extra hidden layers; (3) RNN-based method detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.",
author = "Jing Ma and Wei Gao and Prasenjit Mitra and Sejeong Kwon and Jansen, {Bernard J.} and Wong, {Kam Fai} and Meeyoung Cha",
year = "2016",
month = "1",
day = "1",
language = "English (US)",
volume = "2016-January",
pages = "3818--3824",
journal = "IJCAI International Joint Conference on Artificial Intelligence",
issn = "1045-0823",

}

Detecting rumors from microblogs with recurrent neural networks. / Ma, Jing; Gao, Wei; Mitra, Prasenjit; Kwon, Sejeong; Jansen, Bernard J.; Wong, Kam Fai; Cha, Meeyoung.

In: IJCAI International Joint Conference on Artificial Intelligence, Vol. 2016-January, 01.01.2016, p. 3818-3824.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Detecting rumors from microblogs with recurrent neural networks

AU - Ma, Jing

AU - Gao, Wei

AU - Mitra, Prasenjit

AU - Kwon, Sejeong

AU - Jansen, Bernard J.

AU - Wong, Kam Fai

AU - Cha, Meeyoung

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Microblogging platforms are an ideal place for spreading rumors and automatically debunking rumors is a crucial problem. To detect rumors, existing approaches have relied on hand-crafted features for employing machine learning algorithms that require daunting manual effort. Upon facing a dubious claim, people dispute its truthfulness by posting various cues over time, which generates long-distance dependencies of evidence. This paper presents a novel method that learns continuous representations of microblog events for identifying rumors. The proposed model is based on recurrent neural networks (RNN) for learning the hidden representations that capture the variation of contextual information of relevant posts over time. Experimental results on datasets from two real-world microblog platforms demonstrate that (1) the RNN method outperforms state-of-the-art rumor detection models that use hand-crafted features; (2) performance of the RNN-based algorithm is further improved via sophisticated recurrent units and extra hidden layers; (3) RNN-based method detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.

AB - Microblogging platforms are an ideal place for spreading rumors and automatically debunking rumors is a crucial problem. To detect rumors, existing approaches have relied on hand-crafted features for employing machine learning algorithms that require daunting manual effort. Upon facing a dubious claim, people dispute its truthfulness by posting various cues over time, which generates long-distance dependencies of evidence. This paper presents a novel method that learns continuous representations of microblog events for identifying rumors. The proposed model is based on recurrent neural networks (RNN) for learning the hidden representations that capture the variation of contextual information of relevant posts over time. Experimental results on datasets from two real-world microblog platforms demonstrate that (1) the RNN method outperforms state-of-the-art rumor detection models that use hand-crafted features; (2) performance of the RNN-based algorithm is further improved via sophisticated recurrent units and extra hidden layers; (3) RNN-based method detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.

UR - http://www.scopus.com/inward/record.url?scp=85006173435&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006173435&partnerID=8YFLogxK

M3 - Conference article

VL - 2016-January

SP - 3818

EP - 3824

JO - IJCAI International Joint Conference on Artificial Intelligence

JF - IJCAI International Joint Conference on Artificial Intelligence

SN - 1045-0823

ER -