Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

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Social Media and Fake News in the 2016 Election

The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.

Fake news on Twitter during the 2016 U.S. presidential election.

The following report is intended to provide an overview of the current state of the literature on the relationship between social media; political polarization; and political “disinformation,” a term used to encompass a wide range of types of information about politics found online, including “fake news,” rumors, deliberately factually incorrect information, inadvertently factually incorrect information, politically slanted information, and “hyperpartisan” news. The review of the literature is provided in six separate sections, each of which can be read individually but that cumulatively are intended to provide an overview of what is known — and unknown — about the relationship between social media, political polarization, and disinformation. The report concludes by identifying key gaps in our understanding of these phenomena and the data that are needed to address them.

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Social media, political polarization, and political disinformation: a review of the scientific literature

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Media Manipulation and Disinformation Online

Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies. Designed as a comprehensive evaluation, this paper analyzes and compares these guidelines highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems - and how the effectiveness in the demands of AI ethics can be improved.

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The Ethics of AI Ethics -- An Evaluation of Guidelines

Bots are social media accounts that automate interaction with other users, and they are active on the StrongerIn-Brexit conversation happening over Twitter. These automated scripts generate content through these platforms and then interact with people. Political bots are automated accounts that are particularly active on public policy issues, elections, and political crises. In this preliminary study on the use of political bots during the UK referendum on EU membership, we analyze the tweeting patterns for both human users and bots. We find that political bots have a small but strategic role in the referendum conversations: (1) the family of hashtags associated with the argument for leaving the EU dominates, (2) different perspectives on the issue utilize different levels of automation, and (3) less than 1 percent of sampled accounts generate almost a third of all the messages.

Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum

Bots, #Strongerin, and #Brexit: Computational Propaganda During the UK-EU Referendum

US voters shared large volumes of polarizing political news and information in the form of links to content from Russian, WikiLeaks and junk news sources. Was this low quality political information distributed evenly around the country, or concentrated in swing states and particular parts of the country? In this data memo we apply a tested dictionary of sources about political news and information being shared over Twitter over a ten day period around the 2016 Presidential Election. Using self-reported location information, we place a third of users by state and create a simple index for the distribution of polarizing content around the country. We find that (1) nationally, Twitter users got more misinformation, polarizing and conspiratorial content than professionally produced news. (2) Users in some states, however, shared more polarizing political news and information than users in other states. (3) Average levels of misinformation were higher in swing states than in uncontested states, even when weighted for the relative size of the user population in each state. We conclude with some observations about the impact of strategically disseminated polarizing information on public life.

Social Media, News and Political Information during the US Election: Was Polarizing Content Concentrated in Swing States?

Bots are social media accounts that automate interaction with other users, and political bots have been particularly active on public policy issues, political crises, and elections. We collected data on bot activity using the major hashtags related to the U.S. Presidential debate. In this brief analysis we find that (1) Twitter traffic on pro-Trump hashtags was roughly double that of the pro-Clinton hashtags, (2) about one third of the pro-Trump twitter traffic was driven by bots and highly automated accounts, compared to one quarter of the pro-Clinton twitter traffic, (3) the significant rise of Twitter traffic around debate time is mostly from real users who generate original tweets using the more neutral hashtags. In short, Twitter is much more actively pro-Trump than pro-Clinton and more of the pro-Trump twitter traffic is driven by bots, but a significant number of (human) users still use Twitter for relatively neutral political expression in critical moments.

Bots and Automation over Twitter during the Second U.S. Presidential Debate

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Bence Kollanyi

Papers

Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum

Bots, #Strongerin, and #Brexit: Computational Propaganda During the UK-EU Referendum

Social Media, News and Political Information during the US Election: Was Polarizing Content Concentrated in Swing States?

Bots and Automation over Twitter during the Second U.S. Presidential Debate

Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum