Over the last several years political actors worldwide have begun harnessing the digital power of social bots — software programs designed to mimic human social media users on platforms like Facebook, Twitter, and Reddit. Increasingly, politicians, militaries, and government-contracted firms use these automated actors in online attempts to manipulate public opinion and disrupt organizational communication. Politicized social bots — here ‘political bots’ — are used to massively boost politicians’ follower levels on social media sites in attempts to generate false impressions of popularity. They are programmed to actively and automatically flood news streams with spam during political crises, elections, and conflicts in order to interrupt the efforts of activists and political dissidents who publicize and organize online. They are used by regimes to send out sophisticated computational propaganda. This paper conducts a content analysis of available media articles on political bots in order to build an event dataset of global political bot deployment that codes for usage, capability, and history. This information is then analyzed, generating a global outline of this phenomenon. This outline seeks to explain the variety of political bot-oriented strategies and presents details crucial to building understandings of these automated software actors in the humanities, social and computer sciences.

Political communication is the process of putting information, technology, and media in the service of power. Increasingly, political actors are automating such processes, through algorithms that obscure motives and authors yet reach immense networks of people through personal ties among friends and family. Not all political algorithms are used for manipulation and social control however. So what are the primary ways in which algorithmic political communication-organized by automated scripts on social media-may undermine elections in democracies? In the US context, what specific elements of communication policy or election law might regulate the behavior of such "bots," or the political actors who employ them? First, we describe computational propaganda and define political bots as automated scripts designed to manipulate public opinion. Second, we illustrate how political bots have been used to manipulate public opinion and explain how algorithms are an important new domain of analysis for scholars of political communication. Finally, we demonstrate how political bots are likely to interfere with political communication in the United States by allowing surreptitious campaign coordination, illegally soliciting either contributions or votes, or violating rules on disclosure.

Although socializing is a powerful driver of youth engagement online, platforms struggle to leverage social engagement to promote learning. We seek to understand this dynamic using a multi-stage analysis of over 14,000 comments on Scratch, an online platform designed to support learning about programming. First, we inductively develop the concept of “participatory debugging”—a practice in which users learn through the process of collaborative technical troubleshooting. Second, we use a content analysis to establish how common the practice is on Scratch. Third, we conduct a qualitative analysis of user activity over time and identify three factors that serve as social antecedents of participatory debugging: (1) sustained community, (2) identifiable problems, and (3) what we call “topic porousness” to describe conversations that are able to span multiple topics. We integrate these findings in a framework that highlights a productive tension between the desire to promote learning and the interest-driven sub-communities that drive user engagement in many new media environments.

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