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Political actors are now deploying software programs called social bots that use social networking services such as Facebook or Twitter to communicate with users and manipulate their behavior, creating profound issues for Internet security. Current approaches in bot control continue to fail because social media platforms supply communication resources that allow bots to escape detection and enact influence. Bots become agents by harnessing profile settings, popularity measures, and automated conversation tools, along with vast amounts of user data that social media platforms make available. This article develops an ecological approach to thinking about bots that focuses on how social media environments propel bots into agency. This habitat-based model uses bots to expose ripe targets of intervention and innovation at the level of interface design. It also situates bots in the context of platform providers with a vested interest in interface design, revealing a range of new political problems. Most important, it invites a hybrid ethics, wherein humans and bots act together to solve problems in bot security and Internet ethics more broadly.

Amid widespread reports of digital influence operations during major elections, policymakers, scholars, and journalists have become increasingly interested in the political impact of social media bots. Most recently, platform companies like Facebook and Twitter have been summoned to testify about bots as part of investigations into digitally enabled foreign manipulation during the 2016 U.S. Presidential election. Facing mounting pressure from both the public and from legislators, these companies have been instructed to crack down on apparently malicious bot accounts. But as this article demonstrates, since the earliest writings on bots in the 1990s, there has been substantial confusion as to exactly what a "bot" is and what it does. We argue that multiple forms of ambiguity are responsible for much of the complexity underlying contemporary bot‐related policy, and that before successful policy interventions can be formulated, a more comprehensive understanding of bots—especially how they are defined and measured—will be needed. In this article, we provide a typology of different types of bots, provide clear guidelines for better categorizing political automation, and unpack the impact that it can have on contemporary technology policy. We conclude by outlining the main challenges and ambiguities that will face both researchers and legislators as they tackle bots in the future.

Vital scientific communications are frequently misinterpreted by the lay public as a result of motivated reasoning, where people misconstrue data to fit their political and psychological biases. In the case of climate change, some people have been found to systematically misinterpret climate data in ways that conflict with the intended message of climate scientists. While prior studies have attempted to reduce motivated reasoning through bipartisan communication networks, these networks have also been found to exacerbate bias. Popular theories hold that bipartisan networks amplify bias by exposing people to opposing beliefs. These theories are in tension with collective intelligence research, which shows that exchanging beliefs in social networks can facilitate social learning, thereby improving individual and group judgments. However, prior experiments in collective intelligence have relied almost exclusively on neutral questions that do not engage motivated reasoning. Using Amazon's Mechanical Turk, we conducted an online experiment to test how bipartisan social networks can influence subjects' interpretation of climate communications from NASA. Here, we show that exposure to opposing beliefs in structured bipartisan social networks substantially improved the accuracy of judgments among both conservatives and liberals, eliminating belief polarization. However, we also find that social learning can be reduced, and belief polarization maintained, as a result of partisan priming. We find that increasing the salience of partisanship during communication, both through exposure to the logos of political parties and through exposure to the political identities of network peers, can significantly reduce social learning.