Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments

Hamed, Suhaib Kh.; Aziz, Mohd Juzaiddin Ab; Yaakub, Mohd Ridzwan

doi:10.3390/s23041748

Cited by 27 publications

(5 citation statements)

References 94 publications

(149 reference statements)

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“…These studies represent the state-of-the-art efforts in combating misinformation with AI. However, a key challenge in building an AI-based fake news classifier is related to datasets, feature representation, and data fusion [40].…”

Section: Fake News Detection Methodsmentioning

confidence: 99%

PL-NCC: A Novel Approach for Fake News Detection through Data Augmentation

Arunthavachelvan,

Hasan,

Ding

et al. 2024

Preprint

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Fake news has become rampant with the prominence of social media, jeopardizing information integrity and the reliability of news. It becomes imperative for media platforms to develop robust and efficient strategies to mitigate the spread of fake news. We make two contributions to fake news research in this work. Firstly, we propose the Psycho-Linguistic News Content and Comments (PL-NCC) dataset, a consolidated dataset of two leading fake news datasets, NELA-GT and Faked-dit, which leverages linguistic and psychological characteristics from the news articles and user comments to improve the classification accuracy of benchmark models. Secondly, we propose the News Content and Comments (NCC) classification model to leverage the psychological features extracted from our PL-NCC dataset, which introduces a feed-forward layer to a deep learning model, enhancing the efficacy of the extracted psychological features to better identify fake news. Our approach achieves a classification accuracy of over ninety percent, exceeding baseline results.

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Section: Fake News Detection Methodsmentioning

confidence: 99%

PL-NCC: A Novel Approach for Fake News Detection through Data Augmentation

Arunthavachelvan,

Hasan,

Ding

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…All of this adds up to the visible results of [19] that the spread of fake news is more likely to depend on emotions that can be measured. The main reason for the analysis of emotions can be reflected in a better understanding of the state and situation within which certain reactions can be expected.…”

Section: Finding Studies Interviewing Witnesses or Suspectsmentioning

confidence: 95%

The Impact of Fake News on Traveling and Antisocial Behavior in Online Communities: Overview

Stupavský,

Dakić,

Vranić

2023

Applied Sciences

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The concept of “fake news” has become widespread in recent years, especially with the rise of the Internet. Fake news has become a worldwide phenomenon in the consumption of online information, as it is often designed to look like real news and is widely shared on social networks. Concerns regarding the possible detrimental effects of fake news on the public’s knowledge of events and topics, as well as on democracy and public discourse in general, have arisen as a result of the rise of social networks. This article aims to provide a summary of a scientific investigation of antisocial behavior from historical research, conceptual analysis, and qualitative research in the form of a case study method. With the aim of analyzing online forums and the concept of disinformation using fake news, its implications have consequences that provoke reflection on this phenomenon. In the results, we propose a framework for investigating and evaluating the concepts of fake news and its interaction with other forms of antisocial behavior, including whether we can achieve satisfactory results with a reduced amount of searched text. The desire is to observe whether we can use our proposed procedure with the application of artificial intelligence with the VADER BERT model in combination with the intensity of individual types of sentiment.

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“…The proposed method improves the accuracy of fake news detection and aids in the reduction of misinformation spread on social media. The study [47] proposed a fake news detection model for social media that leverages sentiment analysis of news content and emotion analysis of users' comments. The model uses ML techniques to classify news articles as either fake or genuine based on the sentiment expressed in the article and the emotions expressed in the comments.…”

Section: Emotions-based Rumor Detection Approachesmentioning

confidence: 99%

Exploring the Role of Emotions in Arabic Rumor Detection in Social Media

Al-Saif,

Al-Dossari

2023

Applied Sciences

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With the increasing reliance on social media as a primary source of news, the proliferation of rumors has become a pressing global concern that negatively impacts various domains, including politics, economics, and societal well-being. While significant efforts have been made to identify and debunk rumors in social media, progress in detecting and addressing such issues in the Arabic language has been limited compared to other languages, particularly English. This study introduces a context-aware approach to rumor detection in Arabic social media, leveraging recent advancements in Natural Language Processing (NLP). Our proposed method evaluates Arabic news posts by analyzing the emotions evoked by news content and recipients towards the news. Moreover, this research explores the impact of incorporating user and content features into emotion-based rumor detection models. To facilitate this investigation, we present a novel Arabic rumor dataset, comprising both news posts and associated comments, which represents a first-of-its-kind resource in the Arabic language. The findings from this study offer promising insights into the role of emotions in rumor detection and may serve as a catalyst for further research in this area, ultimately contributing to improved detection and the mitigation of misinformation in the digital landscape.

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Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments

Cited by 27 publications

References 94 publications

PL-NCC: A Novel Approach for Fake News Detection through Data Augmentation

PL-NCC: A Novel Approach for Fake News Detection through Data Augmentation

The Impact of Fake News on Traveling and Antisocial Behavior in Online Communities: Overview

Exploring the Role of Emotions in Arabic Rumor Detection in Social Media

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