A computational approach for examining the roots and spreading patterns of fake news: Evolution tree analysis

Jang, S. Mo; Geng, Tieming; Li, Jo-Yun; Xia, Ruofan; Huang, Chin‐Tser; Kim, Hwalbin; Tang, Jijun

doi:10.1016/j.chb.2018.02.032

Cited by 114 publications

(82 citation statements)

References 22 publications

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“…This denotes that communities of users sharing disinformation tend to be more connected and clustered, with stronger interaction between users, whereas mainstream articles are shared in a more broadcast manner with less discussion between users. A similar result was reported in [16] where a sample of most shared news was inspected in the context of 2016 US presidential elections. Conversely, mainstream networks manifest a much larger number of weakly connected components (or cascades).…”

Section: Discussionsupporting

confidence: 79%

Topology comparison of Twitter diffusion networks effectively reveals misleading information

Pierri

Piccardi

Ceri

2020

Sci Rep

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In recent years, malicious information had an explosive growth in social media, with serious social and political backlashes. Recent important studies, featuring large-scale analyses, have produced deeper knowledge about this phenomenon, showing that disinformation spreads faster, deeper and more broadly than the truth on social media, where bots and echo chambers play an important role in diffusion networks. Following these directions, we explore the possibility of classifying news articles circulating on social media based exclusively on a topological analysis of their diffusion networks. To this aim we collected a large dataset of diffusion networks on Twitter pertaining to news articles published on two distinct classes of sources, namely outlets that convey mainstream, reliable and objective information and those that fabricate and disseminate various kinds of disinformation stories. We carried out an extensive comparison of these networks using several alignmentfree approaches including basic network properties, centrality measures distributions, and network distances. We accordingly evaluated to what extent these network features allow to discriminate between the networks associated to the aforementioned news domains. Our results highlight that the communities of users spreading mainstream rather than disinformation news tend to shape diffusion networks with subtle yet systematic differences. This opens the way to promptly and correctly identifying disinformation on social media by solely inspecting the resulting diffusion networks.

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Section: Discussionsupporting

confidence: 79%

Topology comparison of Twitter diffusion networks effectively reveals misleading information

Pierri

Piccardi

Ceri

2020

Sci Rep

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“…Fake news stories are being promoted on social media platforms to deceive the public for ideological gain [20]. In various articles it was stated that social media users are more likely to seek information from people who are more like-minded or congruent with their own opinions and attitudes [21,22].…”

Section: Social Factorsmentioning

confidence: 99%

“…It is the ability of an individual to understand thought and execute valid reasoning and understanding of concept [34]. With an increasing amount of information being shared across social media platforms it can be challenging for social media users to determine which of the information is closest to the original source [22]. The issue of individuals not having the ability to distinguish between real and fake news have been raised in many articles [10].…”

Section: Cognitive Factorsmentioning

confidence: 99%

“…The spreading of false political information have increased due to the emergence of streamline media environments [22]. There has been a considerable amount of research done on the influence of fake news on the political environment [14,18].…”

Section: Political Factorsmentioning

confidence: 99%

“…By creating false political statements, voters can be convinced or persuaded to change their opinions [3]. Critics reported that in the national election in the UK (regarding the nation's withdrawal out of the EU) and the 2016 presidential election in the US, a number of false information was shared on social media platforms that have influenced the outcome of the results [8,22]. Social media platforms, like Facebook, came under fire in the 2016 US presidential election, when fake news stories from unchecked sources were spread among many users [10].…”

Section: Political Factorsmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review on Fake News Themes Reported in Literature

Celliers

Hattingh

2020

Lecture Notes in Computer Science

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In this systematic literature review, a study of the factors involved in the spreading of fake news, have been provided. In this review, the root causes of the spreading of fake news are identified to reduce the encouraging of such false information. To combat the spreading of fake news on social media, the reasons behind the spreading of fake news must first be identified. Therefore, this literature review takes an early initiative to identify the possible reasons behind the spreading of fake news. The purpose of this literature review is to identify why individuals tend to share false information and to possibly help in detecting fake news before it spreads.

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Comprehending Covidiocy Communication

Spitzberg¹

2021

Communicating Science in Times of Crisis

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A computational approach for examining the roots and spreading patterns of fake news: Evolution tree analysis

Cited by 114 publications

References 22 publications

Topology comparison of Twitter diffusion networks effectively reveals misleading information

Topology comparison of Twitter diffusion networks effectively reveals misleading information

A Systematic Review on Fake News Themes Reported in Literature

Comprehending Covidiocy Communication

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