The spread of low-credibility content by social bots

By Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, Filippo Menczer. 2017

1,210 citations recorded by the source corpus.

The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation. Online misinformation is a threat to a well-informed electorate and undermines democracy. Here, the authors analyse the spread of articles on Twitter, find that bots play a major role in the spread of low-credibility content and suggest control measures for limiting the spread of misinformation.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics, Safety, Robustness & Interpretability

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Citations in the corpus

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “This can lead to problematic usages of LMs, whether malicious (disinformation) or unintentional (misinformation), with models producing misleading content, conspiracy theories, and falsehoods (Xu, 2020; Shao et al., 2018; McGuffie and Newhouse, 2020; Lin et al., 2021a).”
Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward (2021, background): “A study published in [28] concluded that during the 2016 US presidential election, bots generated one-fifth of the tweets during the last month of the campaign.”
“Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content (2019, background): “…served to amplify the dissemination of content coming from low-credibility sources, content such as conspiracy theories, false news, and junk science, suggesting that “curbing social bots may be an effective strategy for mitigating the spread of low credibility content” (Shao et al., 2018, p. 5-6).”
Arming the public with artificial intelligence to counter social bots (2019, background): “A study based on 14 million tweets posted during and after the 2016 U.S. presidential election revealed that bots played a key role in the spread of low-credibility content (Shao et al., 2018a,b).”
Fake news, disinformation and misinformation in social media: a review (2023, background): “Several authors (Shu et al. 2018b, 2017; Shi et al. 2019; Bessi and Ferrara 2016; Shao et al. 2018a) have also shown that fake news is likely to be created and spread by non-human accounts with similar attributes and structure in the network, such as social bots (Ferrara et al. 2016).”
Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation (2020, background): “…on society is still under scrutiny, there is increasingly convincing evidence that deliberate misinformation operations and social manipulation [13] have taken place during major events in the past—including in several high profile political instances [14]—and that they might play an important…”
Fake news on Twitter during the 2016 U.S. presidential election (2019, background): “1 Linking Voting Records to Twitter Accounts While difficulties with measurement on social media are well-established (4), these are particularly pernicious for studying fake news, where bots and governments alike engage in manipulative campaigns that include posing as ordinary people (7, 28, 29).”
Deep Learning for Text Style Transfer: A Survey (2020, background): “, even playing a significant role in the 2016 United States presidential election (Bessi and Ferrara 2016; Shao et al. 2018).”
Breaking Harmony Square: A game that “inoculates” against political misinformation (2020, background): “Artificially amplifying the reach and popularity of certain messages, for example through social media bots or by buying fake followers (McKew, 2018; Shao et al., 2017).”
A Survey of Fake News (2020, background): “From 14 million messages spreading 400,000 articles on Twitter during a 10-month period in 2016 and 2017, Shao et al. [2018] find evidence that bots spread unreliable news at an earlier time compared to humans to increase the chances that an article goes “viral” (Figure 18).”
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media (2018, neutral): “Since fake news pieces are likely to be created and spread by non-human accounts, such as social bots or cyborgs (Shao et al. 2017).”
Hidden Incentives for Auto-Induced Distributional Shift (2020, background): “Shao et al. (2018) examine the role of social bots in spreading fake news by analyzing 14 million Twitter messages.”
An exploratory study of COVID-19 misinformation on Twitter (2020, neutral): “Shao et al. [65] discussed the role of social bots in spreading the misininformation.”