• Lazer et al., “The science of fake news”

    The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.

    Covered partly in The Atlantic, “Why It’s Okay to Call It ‘Fake News’”: “We can’t shy away from phrases because they’ve been somehow weaponized. We have to stick to our guns and say there is a real phenomenon here”.

  • Zannettou et al., “The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans”.

    In this paper, we make a step in this direction by providing a taxonomy of the Web’s false information ecosystem, comprising various types of false information, actors, and their motives. We report a comprehensive overview of existing research on the false information ecosystem by identifying several lines of work: 1) how the public perceives false information; 2) understanding the propagation of false information; 3) detecting and containing false information on the Web; and 4) false information on the political stage. Finally, for each of these lines of work, we report several future research directions that can help us better understand and mitigate the emerging problem of false information dissemination on the Web.

    The taxonomy is based on the one first shared by First Draft’s Claire Wardle in “Fake news. It’s complicated.”, which has been referenced by others. The same with the classification of purposes.

  • Vosoughi et al., “The spread of true and false news online”.

    We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.

    Covered by The Atlantic, “The Grim Conclusions of the Largest-Ever Study of Fake News”.

  • Zhang et al., “A Structured Response to Misinformation: Defining and Annotating Credibility Indicators in News Articles”, via @amyxzh. Part of the Credibility Coalition’s work:

    The proliferation of misinformation in online news and its amplification by platforms are a growing concern, leading to numerous efforts to improve the detection of and response to misinformation. Given the variety of approaches, collective agreement on the indicators that signify credible content could allow for greater collaboration and data-sharing across initiatives. In this paper, we present an initial set of indicators for article credibility defined by a diverse coalition of experts. These indicators originate from both within an article’s text as well as from external sources or article metadata. As a proof-of-concept, we present a dataset of 40 articles of varying credibility annotated with our indicators by 6 trained annotators using specialized platforms. We discuss future steps including expanding annotation, broadening the set of indicators, and considering their use by platforms and the public, towards the development of interoperable standards for content credibility.

  • Karadzhov et al., “Fully Automated Fact Checking Using External Sources”, is mentioned in Graves’ factsheet on automated factchecking for the Reuters Institute:

    We have presented and evaluated a general-purpose method for fact checking that relies on retrieving supporting information from the Web and comparing it to the claim using machine learning. Our method is lightweight in terms of features and can be very efficient because it shows good performance by only using the snippets provided by the search engines. The combination of the representational power of neural networks with the classification of kernel-based methods has proven to be crucial for making balanced predictions and obtaining good results. Overall, the strong performance of our model across two different fact-checking tasks confirms its generality and potential applicability for different domains and for different fact-checking task formulations.