The science of fake news

Lazer, David; Baum, Matthew; Benkler, Yochai; Berinsky, Adam J.; Greenhill, Kelly M.; Menczer, Filippo; Metzger, Miriam J.; Nyhan, Brendan; Pennycook, Gordon; Rothschild, David; Schudson, Michael; Sloman, Steven A.; Sunstein, Cass R.; Thorson, Emily; Watts, Duncan J.; Zittrain, Jonathan

doi:10.1126/science.aao2998

Cited by 2,598 publications

(1,779 citation statements)

References 8 publications

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“…While researchers interested in the study of misinformation or fake news have thus far focused primarily on social media, our results point out a diverse ecosystem of information presentation that, in combination with the trust placed in search engines [7,60], could increase exposure to and consumption of misinformation. Future work on misinformation may want to consider how search engines and highly ranked components featuring direct answers (knowledge and people-ask) and news or social media (twitter, news-card, and news-triplets) might contribute to the spread of untrue information [2,30,39,44,73]. Similarly, future behavioral experiments on the influence of search rankings on beliefs and behavior should explore how the diversity of components Track: Web and Society WWW 2018, April 23-27, 2018, Lyon, France we identified (e.g., knowledge, news-card, and people-ask) could be used to enhance effects like SEME [21,22,59].…”

Section: Discussionmentioning

confidence: 99%

Auditing the Personalization and Composition of Politically-Related Search Engine Results Pages

Robertson

Lazer

Wilson

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Search engines are a primary means through which people obtain information in today's connected world. Yet, apart from the search engine companies themselves, little is known about how their algorithms filter, rank, and present the web to users. This question is especially pertinent with respect to political queries, given growing concerns about filter bubbles, and the recent finding that bias or favoritism in search rankings can influence voting behavior. In this study, we conduct a targeted algorithm audit of Google Search using a dynamic set of political queries. We designed a Chrome extension to survey participants and collect the Search Engine Results Pages (SERPs) and autocomplete suggestions that they would have been exposed to while searching our set of political queries during the month after Donald Trump's Presidential inauguration. Using this data, we found significant differences in the composition and personalization of politically-related SERPs by query type, subjects' characteristics, and date. CCS CONCEPTS INTRODUCTIONRecent concerns surrounding political polarization, fake news, and the impact of media on public opinion have largely focused on social media platforms like Facebook and Twitter. Yet, recent surveys suggest that more news is sought through search engines than social media [2,57], and that search engines are the secondmost likely news gateway to inspire follow-up actions, such as This paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

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Section: Discussionmentioning

confidence: 99%

Auditing the Personalization and Composition of Politically-Related Search Engine Results Pages

Robertson

Lazer

Wilson

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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“…The spread of fake news online is another area in which the effect of bots is believed to be relevant (Bessi et al, ; Lazer et al, ). 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, ,b).…”

Section: Literature Reviewmentioning

confidence: 99%

Arming the public with artificial intelligence to counter social bots

Yang

Varol

Davis

et al. 2019

Human Behav and Emerg Tech

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305

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The increased relevance of social media in our daily life has been accompanied by efforts to manipulate online conversations and opinions. Deceptive social bots—automated or semi‐automated accounts designed to impersonate humans—have been successfully exploited for these kinds of abuse. Researchers have responded by developing artificial intelligence (AI) tools to arm the public in the fight against social bots. Here we review the literature on different types of bots, their impact, and detection methods. We use the case study of Botometer, a popular bot detection tool developed at Indiana University, to illustrate how people interact with AI countermeasures. A user experience survey suggests that bot detection has become an integral part of the social media experience for many users. However, barriers in interpreting the output of AI tools can lead to fundamental misunderstandings. The arms race between machine learning methods to develop sophisticated bots and effective countermeasures makes it necessary to update the training data and features of detection tools. We again use the Botometer case to illustrate both algorithmic and interpretability improvements of bot scores, designed to meet user expectations. We conclude by discussing how future AI developments may affect the fight between malicious bots and the public.

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“…So why do some humans believe and share fake news? One explanation is confirmation bias , or when individuals find information that supports their pre‐existing beliefs as more persuasive that information that contradicts what they believe in (Lazer et al, ). Another explanation is how social media allow users to stay within their filter bubbles , so that users only get exposed to content that is already consistent with their pre‐existing attitudes and interests instead of exposing them to diverse content (Spohr, ).…”

Section: Audiences and Messagesmentioning

confidence: 99%

The facts of fake news: A research review

Tandoc

2019

Sociology Compass

153

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This article offers a review of scholarly research on the phenomenon of fake news. Most studies have so far focused on three main themes: the definition and the scope of the problem; the potential causes; and the impact of proposed solutions. First, scholarly research has defined fake news as a form of falsehood intended to primarily deceive people by mimicking the look and feel of real news. While initial research has shown that only a small fraction of the online audience is exposed to fake news, for this small group of individuals, the impact of fake news can be quite substantial. Second, studies have identified cognitive processes that make individuals more prone to the influence of fake news, such as confirmation bias, selective exposure, and lack of analytical thinking. Fake news also derives its power from its appeal to partisanship, perceived novelty, and repeated exposure facilitated by both bots and human users that share them in the online sphere. Finally, while fact checking has also risen in response to fake news, studies have found that corrections to wrong information only work on some individuals.

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The science of fake news

Abstract: Addressing fake news requires a multidisciplinary effort

Cited by 2,598 publications

References 8 publications

Auditing the Personalization and Composition of Politically-Related Search Engine Results Pages

Auditing the Personalization and Composition of Politically-Related Search Engine Results Pages

Arming the public with artificial intelligence to counter social bots

The facts of fake news: A research review

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