This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link: AbstractIn this paper we uncover a network of Twitterbots comprising 13,493 accounts that tweeted the U.K. E.U. membership referendum, only to disappear from Twitter shortly after the ballot. We compare active users to this set of political bots with respect to temporal tweeting behavior, the size and speed of retweet cascades, and the composition of their retweet cascades (user-to-bot vs.bot-to-bot) to evidence strategies for bot deployment. Our results move forward the analysis of political bots by showing that Twitterbots can be effective at rapidly generating small to medium-sized cascades; that the retweeted content comprises user-generated hyperpartisan news, which is not strictly fake news, but whose shelf life is remarkably short; and, finally, that a botnet may be organized in specialized tiers or clusters dedicated to replicating either active users or content generated by other bots.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link:

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link

This article presents a typological study of the Twitter accounts operated by the Internet Research Agency (IRA), a company specialized in online influence operations based in St. Petersburg, Russia. Drawing on concepts from 20th-century propaganda theory, we modeled the IRA operations along propaganda classes and campaign targets. The study relies on two historical databases and data from the Internet Archive’s Wayback Machine to retrieve 826 user profiles and 6,377 tweets posted by the agency between 2012 and 2017. We manually coded the source as identifiable, obfuscated, or impersonated and classified the campaign target of IRA operations using an inductive typology based on profile descriptions, images, location, language, and tweeted content. The qualitative variables were analyzed as relative frequencies to test the extent to which the IRA’s black, gray, and white propaganda are deployed with clearly defined targets for short-, medium-, and long-term propaganda strategies. The results show that source classification from propaganda theory remains a valid framework to understand IRA’s propaganda machine and that the agency operates a composite of different user accounts tailored to perform specific tasks, including openly pro-Russian profiles, local American and German news sources, pro-Trump conservatives, and Black Lives Matter activists.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link

In this article, we review our study of 13 493 bot-like Twitter accounts that tweeted during the UK European Union membership referendum debate and disappeared from the platform after the ballot. We discuss the methodological challenges and lessons learned from a study that emerged in a period of increasing weaponization of social media and mounting concerns about information warfare. We address the challenges and shortcomings involved in bot detection, the extent to which disinformation campaigns on social media are effective, valid metrics for user exposure, activation and engagement in the context of disinformation campaigns, unsupervised and supervised posting protocols, along with infrastructure and ethical issues associated with social sciences research based on large-scale social media data. We argue for improving researchers' access to data associated with contentious issues and suggest that social media platforms should offer public application programming interfaces to allow researchers access to content generated on their networks. We conclude with reflections on the relevance of this research agenda to public policy.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.

This paper compares the volume of news articles per section in newspapers and social media platforms. To this end, two weeks of news articles were retrieved by querying the public Application Programming Interfaces (APIs) of The New York Times and The Guardian and the diffusion of each article on social media platforms Twitter, Facebook, Google+, Delicious, Pinterest, and StumbleUpon, was tracked. The results show significant differences in the topics emphasized by newspaper editors and social media users. While users of social media platforms favor opinion pieces, along with national, local, and world news, in sharp contrast the decision of news editors emphasized sports and the economy, but also entertainment and celebrity news. Common to social networking sites is the prevalence of items about arts, technology, and opinion pieces. Niche social networks like StumbleUpon and Delicious presented a greater volume of articles about science and technology, while Pinterest is mostly dedicated to fashion, arts, lifestyle, and entertainment. Twitter is the only social network to have presented a statistically significant correlation with the distribution of news items per section by The Guardian and The New York Times. The results of this study provide a bridge between journalism and audience research and present evidence of the differences between readership in social and legacy media.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link