False News On Social Media

Pierri, Francesco; Ceri, Stefano

doi:10.1145/3377330.3377334

Cited by 108 publications

(74 citation statements)

References 64 publications

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“…As the behavior of spammers is different from non-spammers, some attributes or features are recognized in which both categories are different from one another. Feature identification is based on the number of features at user and tweet level such as followers or following, spam keywords, replies, hashtags, and URLs [30], [32].…”

Section: Fake User Identificationmentioning

confidence: 99%

Spammer Detection and Fake User Identification on Social Networks

et al. 2019

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Social networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform.

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Section: Fake User Identificationmentioning

confidence: 99%

Spammer Detection and Fake User Identification on Social Networks

et al. 2019

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“…We may distinguish three approaches: those built on content-based features, those based on features extracted from the social context, and those which combine both aspects. A few main challenges hinder the task, namely the impossibility to manually verify all news items, the lack of gold-standard datasets and the adversarial setting in which malicious content is created [4,7,9].…”

Section: Introductionmentioning

confidence: 99%

A multi-layer approach to disinformation detection in US and Italian news spreading on Twitter

2020

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We tackle the problem of classifying news articles pertaining to disinformation vs mainstream news by solely inspecting their diffusion mechanisms on Twitter. This approach is inherently simple compared to existing text-based approaches, as it allows to by-pass the multiple levels of complexity which are found in news content (e.g. grammar, syntax, style). As we employ a multi-layer representation of Twitter diffusion networks where each layer describes one single type of interaction (tweet, retweet, mention, etc.), we quantify the advantage of separating the layers with respect to an aggregated approach and assess the impact of each layer on the classification. Experimental results with two large-scale datasets, corresponding to diffusion cascades of news shared respectively in the United States and Italy, show that a simple Logistic Regression model is able to classify disinformation vs mainstream networks with high accuracy (AUROC up to 94%). We also highlight differences in the sharing patterns of the two news domains which appear to be common in the two countries. We believe that our network-based approach provides useful insights which pave the way to the future development of a system to detect misleading and harmful information spreading on social media.

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“…), or (b) they mention types of false information outside a general framework or classification model and therefore they are non-exhaustive or indicative (e.g. Campan et al, 2017; Guo et al, 2019; Pierri and Ceri, 2019; Rashkin et al, 2017; Zhou and Zafarani, 2018). Note here that although we exclude these sources as they do not meet our criteria in order to address RQ1, we do consider them for eligibility in terms of RQ2.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

A systematic literature review on disinformation: Toward a unified taxonomical framework

Kapantai

Christopoulou

Berberidis

et al. 2020

New Media & Society

142

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The scale, volume, and distribution speed of disinformation raise concerns in governments, businesses, and citizens. To respond effectively to this problem, we first need to disambiguate, understand, and clearly define the phenomenon. Our online information landscape is characterized by a variety of different types of false information. There is no commonly agreed typology framework, specific categorization criteria, and explicit definitions as a basis to assist the further investigation of the area. Our work is focused on filling this need. Our contribution is twofold. First, we collect the various implicit and explicit disinformation typologies proposed by scholars. We consolidate the findings following certain design principles to articulate an all-inclusive disinformation typology. Second, we propose three independent dimensions with controlled values per dimension as categorization criteria for all types of disinformation. The taxonomy can promote and support further multidisciplinary research to analyze the special characteristics of the identified disinformation types.

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False News On Social Media

Cited by 108 publications

References 64 publications

Spammer Detection and Fake User Identification on Social Networks

Spammer Detection and Fake User Identification on Social Networks

A multi-layer approach to disinformation detection in US and Italian news spreading on Twitter

A systematic literature review on disinformation: Toward a unified taxonomical framework

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