Title

A Semantic Model for Context-Based Fake News Detection on Social Media

Document Type

Conference Proceeding

Source of Publication

Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA

Publication Date

11-1-2020

Abstract

Context-based fake news detection provides means to define and describe a social context for news objects on social media, thereby facilitating detection of fake news through data analysis and patterns recognition. However, while content-based fake news detection has gained popularity with machine learning and NLP techniques, the context-based approach has seen very little exploitation. Therefore, it has become pertinent to significantly explore and integrate other technologies for context-based detection of fake news on social media. With semantic technologies capabilities to provide context-awareness for data, this paper analyses social media context and develops a taxonomy for entities classification. Furthermore, a semantic model is developed to describe classes extracted from the taxonomy towards fully semantically describing concepts, relations, instances, and axioms. The model would enhance fake news detection through semantic annotation for contextual features of news objects and datasets, providing a basis for patterns recognition, analysis, and identification of news articles on social media as either fake or not. © 2020 IEEE.

ISBN

9780000000000

ISSN

2161-5322

Publisher

IEEE Computer Society

Volume

2020-

Disciplines

Social and Behavioral Sciences

Keywords

Semantics; Social networking (online); Taxonomies; Context- awareness; Contextual feature; Nlp techniques; Paper analysis; Semantic annotations; Semantic Model; Semantic technologies; Social context; Pattern recognition

Scopus ID

85099791575

Indexed in Scopus

yes

Open Access

no

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