Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media

Min, Erxue; Rong, Yu; Bian, Yatao; Xu, Tingyang; Zhao, Peilin; Huang, Junzhou; Ananiadou, Sophia

doi:10.1145/3485447.3512163

Cited by 44 publications

(39 citation statements)

References 30 publications

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“…Other works focused on modelling users and their interactions on a more in-depth level to support fake news detection. A popular approach is to construct heterogeneous graphs of article and user nodes where articles share edges with users who interacted with them, and where users are linked to users with whom they share explicit social relations like followership ( Chandra, et al, 2020 , Min, et al, 2022 , Nguyen et al, 2020 ). It could be argued that meaningful social groups can be formed from the users’ social networks.…”

Section: Related Workmentioning

confidence: 99%

Preventing profiling for ethical fake news detection

Allein

Moens

Perrotta

2023

Information Processing & Management

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Section: Related Workmentioning

confidence: 99%

Preventing profiling for ethical fake news detection

Allein

Moens

Perrotta

2023

Information Processing & Management

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“…Propagation-based methods can utilize rich auxiliary social media information, including news spreaders' intent [2] or profiles [10], relationships between news spreaders and their posts [11], social feedback [12]- [14], social networks [15], and propagation paths [16], [17]. Nevertheless, they can only be deployed after news articles published on news outlets have been disseminated on social media.…”

Section: Related Workmentioning

confidence: 99%

Linguistic-style-aware Neural Networks for Fake News Detection

Zhou¹,

Li²,

Li³

et al. 2023

Preprint

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We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal. We first generate the hierarchical linguistic tree of news documents; by doing so, we translate each news document's linguistic style into its writer's usage of words and how these words are recursively structured as phrases, sentences, paragraphs, and, ultimately, the document. By integrating the hierarchical linguistic tree with the neural network, the proposed method learns and classifies the representation of news documents by capturing their locally sequential and globally recursive structures that are linguistically meaningful. It is the first work offering the hierarchical linguistic tree and the neural network preserving the tree information to our best knowledge. Experimental results based on public real-world datasets demonstrate the proposed method's effectiveness, which can outperform state-of-the-art techniques in classifying short and long news documents. We also examine the differential linguistic style of fake news and the truth and observe some patterns of fake news. 1

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“…Karimi et al 187 proposed a fake news detection approach (MMFD) using an automated feature extraction method that can accept the news that comes from multiple sources and detect whether news is fake or real and classify them depending on their degrees of fakeness. Hosseinimotlagh and Papalexakis 188 used the tensor factorization, Wang et al 189 195 used some evaluation models such as count vectorizer, N-gram, and TF-IDF vectorizer with the ML algorithms, Kanagavalli et al 196 used classification based on bi-directional long short term memory (BiLSTM), Jarrahi and Safari 197 introduced convolutional neural network in sentence-level, many other authors [198][199][200][201] also used various ML based approach for detecting fake news on OSNs.…”

Section: Solutions For Fake News Detectionmentioning

confidence: 99%

“…Using deep convolutional layers, Tembhurne et al 193 developed a multi‐channel model (Mc‐DNN) that extracts the most important features without considering hand‐crafted features. Choudhury and Acharjee 194 used SVM, random forest, naive Bayes, and logistic regression classifiers, Raja and Raj 195 used some evaluation models such as count vectorizer, N‐gram, and TF‐IDF vectorizer with the ML algorithms, Kanagavalli et al 196 used classification based on bi‐directional long short term memory (BiLSTM), Jarrahi and Safari 197 introduced convolutional neural network in sentence‐level, many other authors 198‐201 also used various ML based approach for detecting fake news on OSNs.…”

Section: Ml‐based Solutions For Osn Platformmentioning

confidence: 99%

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media

Cited by 44 publications

References 30 publications

Preventing profiling for ethical fake news detection

Preventing profiling for ethical fake news detection

Linguistic-style-aware Neural Networks for Fake News Detection

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

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