Fandet Semantic Model: An OWL Ontology for Context-Based Fake News Detection on Social Media

Bani-Hani, Anoud; Adedugbe, Oluwasegun; Benkhelifa, Elhadj; Majdalawieh, Munir

doi:10.1007/978-3-030-90087-8_5

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Authors in Bani-Hani et al (2022) develop native semantic technology solutions for the discourse space. The initial result is a taxonomy classifying socially contextual features for news articles and then Fandet: an OWL ontology for context-based fake news detection by semantically annotating contextual features of news articles and datasets using the ontology.…”

Section: Survey Of Literaturementioning

confidence: 99%

Fake news detection on Twitter

Sharma

Saraswat

Dubey

2022

IJWIS

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Purpose Owing to the increased accessibility of internet and related technologies, more and more individuals across the globe now turn to social media for their daily dose of news rather than traditional news outlets. With the global nature of social media and hardly any checks in place on posting of content, exponential increase in spread of fake news is easy. Businesses propagate fake news to improve their economic standing and influencing consumers and demand, and individuals spread fake news for personal gains like popularity and life goals. The content of fake news is diverse in terms of topics, styles and media platforms, and fake news attempts to distort truth with diverse linguistic styles while simultaneously mocking true news. All these factors together make fake news detection an arduous task. This work tried to check the spread of disinformation on Twitter. Design/methodology/approach This study carries out fake news detection using user characteristics and tweet textual content as features. For categorizing user characteristics, this study uses the XGBoost algorithm. To classify the tweet text, this study uses various natural language processing techniques to pre-process the tweets and then apply a hybrid convolutional neural network–recurrent neural network (CNN-RNN) and state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) transformer. Findings This study uses a combination of machine learning and deep learning approaches for fake news detection, namely, XGBoost, hybrid CNN-RNN and BERT. The models have also been evaluated and compared with various baseline models to show that this approach effectively tackles this problem. Originality/value This study proposes a novel framework that exploits news content and social contexts to learn useful representations for predicting fake news. This model is based on a transformer architecture, which facilitates representation learning from fake news data and helps detect fake news easily. This study also carries out an investigative study on the relative importance of content and social context features for the task of detecting false news and whether absence of one of these categories of features hampers the effectiveness of the resultant system. This investigation can go a long way in aiding further research on the subject and for fake news detection in the presence of extremely noisy or unusable data.

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Section: Survey Of Literaturementioning

confidence: 99%

Fake news detection on Twitter

Sharma

Saraswat

Dubey

2022

IJWIS

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“…A further advantage of relying upon a conceptual model is its ability to facilitate building well-justified and explainable models for Fake News detection and generation, which, to date, have rarely been available. Works as [4] explores and integrates the use of ontologies (OWL-based) trying to detect fake news on social media by identifying contextual features for news articles. However, as it was emphasized by [25,23], despite the surge of works around the concept of Fake News, how one can automatically assess news authenticity in an effective and explainable manner is still an open issue, especially due to the lack of the precise conceptual characterization of the Fake News concept that this paper advocates.…”

Section: Definitionsmentioning

confidence: 99%

Characterizing Fake News: A Conceptual Modeling-based Approach

Belloir

Ouerdane

Pastor

2022

Conceptual Modeling

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For some time, and even more so now, Fake News has increasingly occupied the media and social space. How identify Fake News and conspiracy theories have become an extremely attractive research area. However, the lack of a solid and well-founded conceptual characterization of what exactly Fake News is and what are its main characteristics, makes it difficult to manage their understanding, identification, and detection. This research work advocates that conceptual modeling must plays a crucial role in characterizing Fake News content accurately. Only by delimiting what Fake News is will it be possible to understand and manage their different perspectives and dimensions, with the ultimate goal of developing a reliable framework for online Fake News detection, as much automated as possible. To contribute in that direction from a pure and practical conceptual modeling perspective, this paper proposes a precise conceptual model of Fake Newss, an essential element for any explainable Artificial Intelligence (XAI)-based approach that must be based on the shared understanding of the domain that only such an accurate conceptualization dimension can facilitate.

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“…Due to the low cost of producing news content, social media is mostly used as a news distribution channel. Publishers use false information as a "tool" to make quick money [15]- [17]. Nowadays, bogus news thrives on social media platform.…”

Section: Introductionmentioning

confidence: 99%

Validation of the toolkit for fake news awareness in social media

Al-Shareeda

Saare

Manickam

et al. 2023

IJEECS

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Fake news has gained attention in recent years, particularly among social media users. The quick spread of fake news has been made possible by the increased usage of social media as a platform to get the most recent news and information. As a consequence, it is getting harder to distinguish between real news and fake news. This essay will outline a study that evaluated the usefulness of a toolset for identifying false news. The first hypothesis is that an increase in media literacy will result in a rise in awareness of fake news. The second theory is that fake news toolkits significantly increase students’ and working adults’ awareness of false news. On the staff of a manufacturing company and an institution, a survey was undertaken. The organization’s employees were given approximately 150 questionnaires, and 110 responses were received. The project includes the creation of a web application-a false news awareness toolkit-that will raise user awareness of fake news in terms of knowledge, behaviour, and attitude among students and workers.

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Fandet Semantic Model: An OWL Ontology for Context-Based Fake News Detection on Social Media

Cited by 6 publications

References 33 publications

Fake news detection on Twitter

Fake news detection on Twitter

Characterizing Fake News: A Conceptual Modeling-based Approach

Validation of the toolkit for fake news awareness in social media

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