Social context-aware trust prediction: methods for identifying fake news

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

Abstract

Fake news, a type of yellow journalism or propaganda, consist of false or incorrect information and have the potential to spread very fast on online social networks. This false information is mainly distributed by social actors who has influence in a specific context (e.g., politics or industry) with the intent to mislead in order to damage an entity (e.g., a politician or a product). Identifying fake news is a challenging task and requires analyzing the reliability, truth, or ability (i.e., trust) of social actors in a certain context and to a certain extent. To address this challenge, in this paper, we present a context-aware trust prediction approach which considers the notion of a context (which conceptually refers to any knowledge to specify the condition of an entity) as well as the social actor’s behavior (supported by theories from social psychology) as first class citizens. We present novel algorithms that employ social context factors inspired by social phycology theories and mathematically model our approach based on Tensor Decomposition. We perform an extensive empirical study and present evaluation results on the effectiveness and the quality of the results using real-world datasets.

LanguageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2018
Subtitle of host publication19th International Conference, 2018, Proceedings, Part I
EditorsHakim Hacid, Wojciech Cellary, Hua Wang, Hye-Young Paik, Rui Zhou
Place of PublicationSwitzerland
PublisherSpringer, Springer Nature
Pages161-177
Number of pages17
ISBN (Electronic)9783030029227
ISBN (Print)9783030029210
DOIs
Publication statusPublished - 1 Jan 2018
Event19th International Conference on Web Information Systems Engineering, WISE 2018 - Dubai, United Arab Emirates
Duration: 12 Nov 201815 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11233 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Web Information Systems Engineering, WISE 2018
CountryUnited Arab Emirates
CityDubai
Period12/11/1815/11/18

Fingerprint

Context-aware
Tensors
Decomposition
Prediction
Industry
Tensor Decomposition
Empirical Study
Social Networks
Damage
Context
Evaluation
Actors
False
Model

Keywords

  • Fake news detection
  • Social networks analytics
  • Trust prediction

Cite this

Ghafari, S. M., Yakhchi, S., Beheshti, A., & Orgun, M. (2018). Social context-aware trust prediction: methods for identifying fake news. In H. Hacid, W. Cellary, H. Wang, H-Y. Paik, & R. Zhou (Eds.), Web Information Systems Engineering – WISE 2018: 19th International Conference, 2018, Proceedings, Part I (pp. 161-177). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11233 LNCS). Switzerland: Springer, Springer Nature. https://doi.org/10.1007/978-3-030-02922-7_11
Ghafari, Seyed Mohssen ; Yakhchi, Shahpar ; Beheshti, Amin ; Orgun, Mehmet. / Social context-aware trust prediction : methods for identifying fake news. Web Information Systems Engineering – WISE 2018: 19th International Conference, 2018, Proceedings, Part I. editor / Hakim Hacid ; Wojciech Cellary ; Hua Wang ; Hye-Young Paik ; Rui Zhou. Switzerland : Springer, Springer Nature, 2018. pp. 161-177 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Fake news, a type of yellow journalism or propaganda, consist of false or incorrect information and have the potential to spread very fast on online social networks. This false information is mainly distributed by social actors who has influence in a specific context (e.g., politics or industry) with the intent to mislead in order to damage an entity (e.g., a politician or a product). Identifying fake news is a challenging task and requires analyzing the reliability, truth, or ability (i.e., trust) of social actors in a certain context and to a certain extent. To address this challenge, in this paper, we present a context-aware trust prediction approach which considers the notion of a context (which conceptually refers to any knowledge to specify the condition of an entity) as well as the social actor’s behavior (supported by theories from social psychology) as first class citizens. We present novel algorithms that employ social context factors inspired by social phycology theories and mathematically model our approach based on Tensor Decomposition. We perform an extensive empirical study and present evaluation results on the effectiveness and the quality of the results using real-world datasets.",
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Ghafari, SM, Yakhchi, S, Beheshti, A & Orgun, M 2018, Social context-aware trust prediction: methods for identifying fake news. in H Hacid, W Cellary, H Wang, H-Y Paik & R Zhou (eds), Web Information Systems Engineering – WISE 2018: 19th International Conference, 2018, Proceedings, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11233 LNCS, Springer, Springer Nature, Switzerland, pp. 161-177, 19th International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, 12/11/18. https://doi.org/10.1007/978-3-030-02922-7_11

Social context-aware trust prediction : methods for identifying fake news. / Ghafari, Seyed Mohssen; Yakhchi, Shahpar; Beheshti, Amin; Orgun, Mehmet.

Web Information Systems Engineering – WISE 2018: 19th International Conference, 2018, Proceedings, Part I. ed. / Hakim Hacid; Wojciech Cellary; Hua Wang; Hye-Young Paik; Rui Zhou. Switzerland : Springer, Springer Nature, 2018. p. 161-177 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11233 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

TY - GEN

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AU - Beheshti, Amin

AU - Orgun, Mehmet

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N2 - Fake news, a type of yellow journalism or propaganda, consist of false or incorrect information and have the potential to spread very fast on online social networks. This false information is mainly distributed by social actors who has influence in a specific context (e.g., politics or industry) with the intent to mislead in order to damage an entity (e.g., a politician or a product). Identifying fake news is a challenging task and requires analyzing the reliability, truth, or ability (i.e., trust) of social actors in a certain context and to a certain extent. To address this challenge, in this paper, we present a context-aware trust prediction approach which considers the notion of a context (which conceptually refers to any knowledge to specify the condition of an entity) as well as the social actor’s behavior (supported by theories from social psychology) as first class citizens. We present novel algorithms that employ social context factors inspired by social phycology theories and mathematically model our approach based on Tensor Decomposition. We perform an extensive empirical study and present evaluation results on the effectiveness and the quality of the results using real-world datasets.

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A2 - Paik, Hye-Young

A2 - Zhou, Rui

PB - Springer, Springer Nature

CY - Switzerland

ER -

Ghafari SM, Yakhchi S, Beheshti A, Orgun M. Social context-aware trust prediction: methods for identifying fake news. In Hacid H, Cellary W, Wang H, Paik H-Y, Zhou R, editors, Web Information Systems Engineering – WISE 2018: 19th International Conference, 2018, Proceedings, Part I. Switzerland: Springer, Springer Nature. 2018. p. 161-177. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-02922-7_11