Can We Spot the "Fake News" Before It Was Even Written?

Nakov, Preslav

doi:10.48550/arxiv.2008.04374

Cited by 7 publications

(6 citation statements)

References 47 publications

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“…Hence, there is still no agreed definition of the term "fake news." Moreover, we can find many terms and concepts in the literature that refer to fake news Molina et al 2021) (Abu Arqoub et al 2022Allen et al 2020;Allcott and Gentzkow 2017;Shu et al 2017;Sharma et al 2019;Zhou and Zafarani 2020;Zhang and Ghorbani 2020;Conroy et al 2015;Celliers and Hattingh 2020;Nakov 2020;Shu et al 2020c;Jin et al 2016;Rubin et al 2016;Balmas 2014;Brewer et al 2013;Egelhofer and Lecheler 2019;Mustafaraj and Metaxas 2017;Klein and Wueller 2017;Potthast et al 2017;Lazer et al 2018;Weiss et al 2020;Tandoc Jr et al 2021;Guadagno and Guttieri 2021), disinformation (Kapantai et al 2021;Shu et al 2020a, c;Kumar et al 2016;Bhattacharjee et al 2020;Marsden et al 2020;Jungherr and Schroeder 2021;Starbird et al 2019;Ireton and Posetti 2018), misinformation (Wu et al 2019;Shu et al 2020c;Shao et al 2016Shao et al , 2018bMicallef et al 2020), malinformation (Dame Adjin-Tettey 2022) (Carmi et al 2020;…”

Section: Definitions Of Fake Newsmentioning

confidence: 99%

“…There is also a remarkable amount of disagreement over the classification of the term fake news in the research literature, as well as in policy (de Cock Buning 2018;ERGA 2018ERGA , 2021. Some consider fake news as a type of misinformation (Allen et al 2020;Singh et al 2021;Ha et al 2021;Shao et al 2018b;Di Domenico et al 2021;Sharma et al 2019;Celliers and Hattingh 2020;Klein and Wueller 2017;Potthast et al 2017;Islam et al 2020), others consider it as a type of disinformation (de Cock Buning 2018) (Bringula et al 2022;Baptista and Gradim 2022;Tsang 2020;Tandoc Jr et al 2021;Bastick 2021;Khan et al 2019;Shu et al 2017;Nakov 2020;Shu et al 2020c;Egelhofer and Lecheler 2019), while others associate the term with both disinformation and misinformation (Wu et al 2022;Dame Adjin-Tettey 2022;Hameleers et al 2022;Carmi et al 2020;Allcott and Gentzkow 2017;Zhang and Ghorbani 2020;Potthast et al 2017;Weiss et al 2020;Tandoc Jr et al 2021;Guadagno and Guttieri 2021). On the other hand, some prefer to differentiate fake news from both terms (ERGA 2018;Molina et al 2021;ERGA 2021) (Zhou and Zafarani 2020;…”

Section: Definitions Of Fake Newsmentioning

confidence: 99%

See 1 more Smart Citation

Fake news, disinformation and misinformation in social media: a review

Aïmeur

Amri

Brassard

2023

Soc. Netw. Anal. Min.

181

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Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

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Section: Definitions Of Fake Newsmentioning

confidence: 99%

Section: Definitions Of Fake Newsmentioning

confidence: 99%

Fake news, disinformation and misinformation in social media: a review

Aïmeur

Amri

Brassard

2023

Soc. Netw. Anal. Min.

181

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“…Several recent works [19], [20] have shown that the credibility or trustworthiness of media outlets can be used as a weak signal to fact-check the news articles that are published by the corresponding media outlets. These works attempt to assign a credibility score for each news outlet based on its previous publications and propagate the same label for future publications coming from the same outlet.…”

Section: Source Credibilitymentioning

confidence: 99%

“…Text content-based approaches [31], [53], [54] explore word usage and linguistic styles in the headline and body of news records to identify fake news. Some works explore the credibility of the source of the articles [17], [19]. Also, some works [6], [9], [37], [38], [55], [56] exploit the features from the social context (e.g., the features of the propagation pattern and the characteristics of the users engaging with the news records) of news records to identify their veracity with the help of machine learning techniques such as Propagation Tree Kernels [56], Recurrent Neural Networks [37], [55], and Graph Neural Networks [9].…”

Section: Multi-modal Fake News Detectionmentioning

confidence: 99%

Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data

Silva

Luo

Karunasekera

et al. 2021

AAAI

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With the rapid evolution of social media, fake news has become a significant social problem, which cannot be addressed in a timely manner using manual investigation. This has motivated numerous studies on automating fake news detection. Most studies explore supervised training models with different modalities (e.g., text, images, and propagation networks) of news records to identify fake news. However, the performance of such techniques generally drops if news records are coming from different domains (e.g., politics, entertainment), especially for domains that are unseen or rarely-seen during training. As motivation, we empirically show that news records from different domains have significantly different word usage and propagation patterns. Furthermore, due to the sheer volume of unlabelled news records, it is challenging to select news records for manual labelling so that the domain-coverage of the labelled dataset is maximised. Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost. Our experiments show that the integration of the proposed fake news model and the selective annotation approach achieves state-of-the-art performance for cross-domain news datasets, while yielding notable improvements for rarely-appearing domains in news datasets.

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“…NLP technology could also be leveraged in proactive strategies. Prior work has employed network analysis and similar techniques to identify key actors for intervention in social networks (Farajtabar et al, 2017); using NLP, such techniques could be extended to take into account the information shared by these actors, in addition to graph-based features (Nakov, 2020;Mu and Aletras, 2020). Another direction is to disseminate countermessaging before misinformation can spread widely; this is also known as pre-bunking, and has been shown to be more effective than post-hoc debunking (van der Linden et al, 2017;Roozenbeek et al, 2020;Lewandowsky and van der Linden, 2021).…”

Section: Research Challengesmentioning

confidence: 99%

A Survey on Automated Fact-Checking

Guo¹,

Schlichtkrull²,

Vlachos³

2021

Preprint

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Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how fact-checking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research.

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Can We Spot the "Fake News" Before It Was Even Written?

Cited by 7 publications

References 47 publications

Fake news, disinformation and misinformation in social media: a review

Fake news, disinformation and misinformation in social media: a review

Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data

A Survey on Automated Fact-Checking

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