Fake news detection on social media using a natural language inference approach

Sadeghi, Fariba; Bidgoly, Amir Jalaly; Amirkhani, Hossein

doi:10.1007/s11042-022-12428-8

Cited by 34 publications

(7 citation statements)

References 46 publications

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“…The number of classes and their meanings in the NLI task are very similar to the labels "Support", "Refute", and "Not enough info", which are used for the stance detection task in the fake news detection pipeline and the manual markup. Moreover, in [57], the usage of NLI features for stance detection tasks was tested. The best model based on NLI features showed a 10% improvement in accuracy over baselines in the FNC-1 dataset.…”

Section: Natural Language Inference (Nli)mentioning

confidence: 99%

Multiverse: Multilingual Evidence for Fake News Detection

2023

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The rapid spread of deceptive information on the internet can have severe and irreparable consequences. As a result, it is important to develop technology that can detect fake news. Although significant progress has been made in this area, current methods are limited because they focus only on one language and do not incorporate multilingual information. In this work, we propose Multiverse—a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches. Our hypothesis that cross-lingual evidence can be used as a feature for fake news detection is supported by manual experiments based on a set of true (legit) and fake news. Furthermore, we compared our fake news classification system based on the proposed feature with several baselines on two multi-domain datasets of general-topic news and one fake COVID-19 news dataset, showing that (in combination with linguistic features) it yields significant improvements over the baseline models, bringing additional useful signals to the classifier.

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Section: Natural Language Inference (Nli)mentioning

confidence: 99%

Multiverse: Multilingual Evidence for Fake News Detection

2023

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“…Some research employs BERT as a word-embedding technique. Sadeghi et al (2022) compared BERT to the conventional word-embedding approach. The results indicate that BERT is the most accurate method compared to the others.…”

Section: Literature Reviewmentioning

confidence: 99%

CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method

Wang,

Maslim,

Liu

2023

DTA

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PurposeA clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.Design/methodology/approachThis study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.FindingsThis research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.Originality/valueThe originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

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“…Many techniques have been proposed to identify propaganda, disinformation, fake news and others information distortion techniques, which include data mining and text-mining using ensemble methods [1,2], linguistic-based detection and social network analysis methods [3], natural language inference approach [4], sentence-level analysis [5] and sentiment analysis techniques [6]. Due to generation of enormous amount of information pieces after full-scale Russian invasion binary classification methods using text-mining and dictionaries also regaining their popularity [7].…”

Section: Existing Feature Based Approaches For Suggestive Influence I...mentioning

confidence: 99%

“…Let's summarize most important markers, relying on investigations [3,6,8,9,12,13,4,11,14], and create logically full list of notintersecting markers:…”

Section: Suggestive Influence As An Information Manipulation Toolmentioning

confidence: 99%

Proposing of suggestive influence detection and classification method based on fuzzy logic and feature driven analysis

Nakonechna

2023

TACS

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This research proposes an approach to identify and classify tools utilized in information operations that aim to exert suggestive influence. The proposed method combines fuzzy sets theory and fuzzy inference methods with a feature-based analysis approach. By employing this approach, the study focuses on identifying and categorizing tools such propaganda, fake, disinformation, manipulation, and artificial narrative. This framework provides a comprehensive and nuanced understanding of the various tactics employed in information operations, allowing for improved analysis and mitigation strategies.

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Fake news detection on social media using a natural language inference approach

Cited by 34 publications

References 46 publications

Multiverse: Multilingual Evidence for Fake News Detection

Multiverse: Multilingual Evidence for Fake News Detection

CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method

Proposing of suggestive influence detection and classification method based on fuzzy logic and feature driven analysis

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