Identification and characterization of misinformation superspreaders on social media

@article{DeVerna2022IdentificationAC,
  title={Identification and characterization of misinformation superspreaders on social media},
  author={Matthew R. DeVerna and Rachith Aiyappa and Diogo Pacheco and John Bryden and Filippo Menczer},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.09524}
}
The world’s digital information ecosystem contin-ues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content — so-called superspreaders — are at the center of this problem. We quantitatively con-firm this hypothesis and introduce simple metrics to predict the top misinformation superspreaders several months into the future. We then conduct a qualitative review to characterize the most… 

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References

SHOWING 1-10 OF 63 REFERENCES

Scalable and Generalizable Social Bot Detection through Data Selection

This paper proposes a framework that uses minimal account metadata, enabling efficient analysis that scales up to handle the full stream of public tweets of Twitter in real time, and finds that strategically selecting a subset of training data yields better model accuracy and generalization than exhaustively training on all available data.

The spread of low-credibility content by social bots

It is found that bots play a major role in the spread of low-credibility content on Twitter, and control measures for limiting thespread of misinformation are suggested.

Searching for superspreaders of information in real-world social media

It is found that the widely-used degree and PageRank fail in ranking users' influence and the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society.

URL https://www.science.or g/doi/abs/10.1126/science.aau2706

  • Fake news on Twitter during the 2016 U.S. presidential election. Science,
  • 2019

The COVID-19 Infodemic: Twitter versus Facebook

This work analyzes the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook and highlights limits imposed by inconsistent data-access policies on the capability to study harmful manipulations of information ecosystems.

Anatomy of an online misinformation network

Hoaxy builds an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter, and performs 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.

Repeat Spreaders and Election Delegitimization: A Comprehensive Dataset of Misinformation Tweets from the 2020 U.S. Election

An analysis of uniquely dataset of misinformation, disinformation, and rumors spreading on Twitter the 2020 U.S. election, addressing the outsized role of repeat spreaders.

Combining interventions to reduce the spread of viral misinformation

Using a mathematical model of viral spread and Twitter data, Bak-Coleman and coauthors show how a combination of interventions, such as fact-checking, nudging and account suspension, can help combat the spread of misinformation.

Automated Classification of Fake News Spreaders to Break the Misinformation Chain

This paper proposes a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic, and turns a batch of Twitter posts authored by users of the CoAID dataset into a high-dimensional matrix of features which are exploited by a deep neural network architecture based on transformers to perform user classification.

Right and left, partisanship predicts (asymmetric) vulnerability to misinformation

We analyze the relationship between partisanship, echo chambers, and vulnerability to online misinformation by studying news sharing behavior on Twitter. While our results confirm prior findings that
...