Abstract

When a message, such as a piece of news, spreads in social networks, howcanwe classify it into categories of interests, such as genuine or fake news? Classification of social media content is a fundamental task for social media mining, and most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags. However, for many emerging applications like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to make it look like real news. To address this problem, this paper concentrates on modeling the propagation of messages in a social network. Specifically, we propose a novel approach, TraceMiner, to (1) infer embeddings of social media users with social network structures; and (2) utilize an LSTM-RNN to represent and classify propagation pathways of a message. Since content information is sparse and noisy on social media, adopting TraceMiner allows to provide a high degree of classification accuracy even in the absence of content information. Experimental results on real-world datasets show the superiority over state-of-the-art approaches on the task of fake news detection and news categorization.

Original languageEnglish (US)
Title of host publicationWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages637-645
Number of pages9
Volume2018-Febuary
ISBN (Electronic)9781450355810
DOIs
StatePublished - Feb 2 2018
Event11th ACM International Conference on Web Search and Data Mining, WSDM 2018 - Marina Del Rey, United States
Duration: Feb 5 2018Feb 9 2018

Other

Other11th ACM International Conference on Web Search and Data Mining, WSDM 2018
CountryUnited States
CityMarina Del Rey
Period2/5/182/9/18

Fingerprint

Spreaders

Keywords

  • Classification
  • Fake news detection
  • Graph mining
  • Misinformation
  • Social media mining
  • Social network analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computer Networks and Communications
  • Information Systems

Cite this

Wu, L., & Liu, H. (2018). Tracing fake-news footprints: Characterizing social media messages by how they propagate. In WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining (Vol. 2018-Febuary, pp. 637-645). Association for Computing Machinery, Inc. https://doi.org/10.1145/3159652.3159677

Tracing fake-news footprints : Characterizing social media messages by how they propagate. / Wu, Liang; Liu, Huan.

WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining. Vol. 2018-Febuary Association for Computing Machinery, Inc, 2018. p. 637-645.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wu, L & Liu, H 2018, Tracing fake-news footprints: Characterizing social media messages by how they propagate. in WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining. vol. 2018-Febuary, Association for Computing Machinery, Inc, pp. 637-645, 11th ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, United States, 2/5/18. https://doi.org/10.1145/3159652.3159677
Wu L, Liu H. Tracing fake-news footprints: Characterizing social media messages by how they propagate. In WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining. Vol. 2018-Febuary. Association for Computing Machinery, Inc. 2018. p. 637-645 https://doi.org/10.1145/3159652.3159677
Wu, Liang ; Liu, Huan. / Tracing fake-news footprints : Characterizing social media messages by how they propagate. WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining. Vol. 2018-Febuary Association for Computing Machinery, Inc, 2018. pp. 637-645
@inproceedings{a13239c4b39d480ab02439457be2d5f5,
title = "Tracing fake-news footprints: Characterizing social media messages by how they propagate",
abstract = "When a message, such as a piece of news, spreads in social networks, howcanwe classify it into categories of interests, such as genuine or fake news? Classification of social media content is a fundamental task for social media mining, and most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags. However, for many emerging applications like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to make it look like real news. To address this problem, this paper concentrates on modeling the propagation of messages in a social network. Specifically, we propose a novel approach, TraceMiner, to (1) infer embeddings of social media users with social network structures; and (2) utilize an LSTM-RNN to represent and classify propagation pathways of a message. Since content information is sparse and noisy on social media, adopting TraceMiner allows to provide a high degree of classification accuracy even in the absence of content information. Experimental results on real-world datasets show the superiority over state-of-the-art approaches on the task of fake news detection and news categorization.",
keywords = "Classification, Fake news detection, Graph mining, Misinformation, Social media mining, Social network analysis",
author = "Liang Wu and Huan Liu",
year = "2018",
month = "2",
day = "2",
doi = "10.1145/3159652.3159677",
language = "English (US)",
volume = "2018-Febuary",
pages = "637--645",
booktitle = "WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining",
publisher = "Association for Computing Machinery, Inc",

}

TY - GEN

T1 - Tracing fake-news footprints

T2 - Characterizing social media messages by how they propagate

AU - Wu, Liang

AU - Liu, Huan

PY - 2018/2/2

Y1 - 2018/2/2

N2 - When a message, such as a piece of news, spreads in social networks, howcanwe classify it into categories of interests, such as genuine or fake news? Classification of social media content is a fundamental task for social media mining, and most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags. However, for many emerging applications like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to make it look like real news. To address this problem, this paper concentrates on modeling the propagation of messages in a social network. Specifically, we propose a novel approach, TraceMiner, to (1) infer embeddings of social media users with social network structures; and (2) utilize an LSTM-RNN to represent and classify propagation pathways of a message. Since content information is sparse and noisy on social media, adopting TraceMiner allows to provide a high degree of classification accuracy even in the absence of content information. Experimental results on real-world datasets show the superiority over state-of-the-art approaches on the task of fake news detection and news categorization.

AB - When a message, such as a piece of news, spreads in social networks, howcanwe classify it into categories of interests, such as genuine or fake news? Classification of social media content is a fundamental task for social media mining, and most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags. However, for many emerging applications like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to make it look like real news. To address this problem, this paper concentrates on modeling the propagation of messages in a social network. Specifically, we propose a novel approach, TraceMiner, to (1) infer embeddings of social media users with social network structures; and (2) utilize an LSTM-RNN to represent and classify propagation pathways of a message. Since content information is sparse and noisy on social media, adopting TraceMiner allows to provide a high degree of classification accuracy even in the absence of content information. Experimental results on real-world datasets show the superiority over state-of-the-art approaches on the task of fake news detection and news categorization.

KW - Classification

KW - Fake news detection

KW - Graph mining

KW - Misinformation

KW - Social media mining

KW - Social network analysis

UR - http://www.scopus.com/inward/record.url?scp=85046902878&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046902878&partnerID=8YFLogxK

U2 - 10.1145/3159652.3159677

DO - 10.1145/3159652.3159677

M3 - Conference contribution

AN - SCOPUS:85046902878

VL - 2018-Febuary

SP - 637

EP - 645

BT - WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining

PB - Association for Computing Machinery, Inc

ER -