Dataset Information

Anatomy of an online misinformation network.

ABSTRACT: Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.

SUBMITTER: Shao C

PROVIDER: S-EPMC5922526 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Anatomy of an online misinformation network.

Shao Chengcheng C Hui Pik-Mai PM Wang Lei L Jiang Xinwen X Flammini Alessandro A Menczer Filippo F Ciampaglia Giovanni Luca GL

PloS one 20180427 4

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enable ...[more]

PMID: 29702657

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Project description:IntroductionThe memory conformity effect occurs when people witness a given incident and then talk to each other about it, and the statement of one person affects the memory account of another person with respect to that incident. The main objectives of this experiment were (1) to examine the effectiveness of a modified version of the MORI-v technique in inducing the memory conformity effect and (2) to investigate how the manner in which participants discuss the observed event influences the magnitude of this effect. In general, the modified online MORI-v technique consists of the following main elements: (1) original material, that is, two versions of a short film which are identical except for certain critical details; for example, in one version, a thief puts on a red cap, but in the other version it is black; (2) the collaborative recognition test, that is, a discussion about the original material which leads to mutual misinformation; and (3) an individual recognition test that checks the effect of the discussion on the memory account of the original material.MethodsA total of 72 participants (36 pairs) aged 18-54 took part in the research. Participants were tested using the online MORI-v technique: They were familiarized with the original material on their computers at home, and then they talked about it via a video communication app and completed an individual recognition test on their computers. Importantly, the discussions were recorded and analyzed in detail after the experimental session.Results and discussionUsing the online MORI-v technique, the effect of memory conformity was demonstrated, that is, in the individual recognition test, the proportion of correct answers to questions about discussed details (related to misinformation) was lower than the proportion of correct answers to questions about non-discussed details. It was also demonstrated that if one participant introduced misinformation during the discussion about a particular item and the other did not question it, the latter's answer to that item during the individual recognition test was most often incorrect. However, if one participant introduced misinformation during the discussion about an item and the other questioned it, the latter's answer about that item during the individual recognition test was most often correct.

Project description:The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network. We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community.Supplementary informationThe online version contains supplementary material available at 10.1140/epjds/s13688-021-00289-4.

Dataset Information

Anatomy of an online misinformation network.

Publications

Anatomy of an online misinformation network.

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