Semantic fake news detection: a machine learning perspective

Adrian Brasoveanu; Razvan Andonie

doi:10.1007/978-3-030-20521-8_54

Semantic fake news detection: a machine learning perspective

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

Abstract

Fake news detection is a difficult problem due to the nuances of language. Understanding the reasoning behind certain fake items implies inferring a lot of details about the various actors involved. We believe that the solution to this problem should be a hybrid one, combining machine learning, semantics and natural language processing. We introduce a new semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text. Our experiments show that by adding semantic features the accuracy of fake news classification improves significantly.

Original language	English
Title of host publication	Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science
Publisher	Springer
Pages	656-667
Volume	11506
ISBN (Electronic)	978-3-030-20521-8
ISBN (Print)	978-3-030-20520-1
DOIs	https://doi.org/10.1007/978-3-030-20521-8_54
Publication status	Published - Jun 2019
Event	International Work-Conference on Artificial Neural Networks - IWANN 2019 - , Spain Duration: 12 Jun 2019 → 14 Jun 2019

Conference

Conference	International Work-Conference on Artificial Neural Networks - IWANN 2019
Country/Territory	Spain
Period	12/06/2019 → 14/06/2019

Access to Document

10.1007/978-3-030-20521-8_54

Cite this

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title = "Semantic fake news detection: a machine learning perspective",

abstract = "Fake news detection is a difficult problem due to the nuances of language. Understanding the reasoning behind certain fake items implies inferring a lot of details about the various actors involved. We believe that the solution to this problem should be a hybrid one, combining machine learning, semantics and natural language processing. We introduce a new semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text. Our experiments show that by adding semantic features the accuracy of fake news classification improves significantly.",

author = "Adrian Brasoveanu and Razvan Andonie",

year = "2019",

month = jun,

doi = "10.1007/978-3-030-20521-8_54",

language = "English",

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booktitle = "Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science",

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address = "Germany",

note = "International Work-Conference on Artificial Neural Networks - IWANN 2019 ; Conference date: 12-06-2019 Through 14-06-2019",

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AU - Brasoveanu, Adrian

AU - Andonie, Razvan

PY - 2019/6

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N2 - Fake news detection is a difficult problem due to the nuances of language. Understanding the reasoning behind certain fake items implies inferring a lot of details about the various actors involved. We believe that the solution to this problem should be a hybrid one, combining machine learning, semantics and natural language processing. We introduce a new semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text. Our experiments show that by adding semantic features the accuracy of fake news classification improves significantly.

AB - Fake news detection is a difficult problem due to the nuances of language. Understanding the reasoning behind certain fake items implies inferring a lot of details about the various actors involved. We believe that the solution to this problem should be a hybrid one, combining machine learning, semantics and natural language processing. We introduce a new semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text. Our experiments show that by adding semantic features the accuracy of fake news classification improves significantly.

U2 - 10.1007/978-3-030-20521-8_54

DO - 10.1007/978-3-030-20521-8_54

M3 - Conference contribution

SN - 978-3-030-20520-1

VL - 11506

SP - 656

EP - 667

BT - Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science

PB - Springer

T2 - International Work-Conference on Artificial Neural Networks - IWANN 2019

Y2 - 12 June 2019 through 14 June 2019

ER -

Semantic fake news detection: a machine learning perspective

Abstract

Conference

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