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Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media, and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term social bot is not well defined and different scientific disciplines use divergent definitions. This work starts with a balanced definition attempt, before providing an overview of how social bots actually work (taking the example of Twitter) and what their current technical limitations are. Despite recent research progress in Deep Learning and Big Data, there are many activities bots cannot handle well. We then discuss how bot capabilities can be extended and controlled by integrating humans into the process and reason that this is currently the most promising way to realize meaningful interactions with other humans. This finally leads to the conclusion that hybridization is a challenge for current detection mechanisms and has to be handled with more sophisticated approaches to identify political propaganda distributed with social bots.

Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophisticated these bots are. This work provides a much needed new perspective on these questions. By providing insights into the markets of social bots in the clearnet and darknet as well as an exhaustive analysis of freely available software tools for automation during the last decade, we shed light on the availability and capabilities of automated profiles in social media platforms. Our results confirm the increasing importance of social bot technology but also uncover an as yet unknown discrepancy of theoretical and practically achieved artificial intelligence in social bots: while literature reports on a high degree of intelligence for chat bots and assumes the same for social bots, the observed degree of intelligence in social bot implementations is limited. In fact, the overwhelming majority of available services and software are of supportive nature and merely provide modules of automation instead of fully fledged “intelligent” social bots.

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract Does time-varying business volatility affect the price setting of firms and thus the transmission of monetary policy into the real economy? To address this question, we estimate from the firm-level micro data of the German IFO Business Climate Survey the impact of idiosyncratic volatility on the price setting behavior of firms. In a second step, we use a calibrated New Keynesian business cycle model to gauge the effects of time-varying volatility on the transmission of monetary policy to output. Our results are twofold. Heightened business volatility increases the probability of a price change, though the effect is small: the tripling of volatility during the recession of 08/09 caused the average quarterly likelihood of a price change to increase from 31.6% to 32.3%. Second, the effects of this increase in volatility on monetary policy are also small; the initial effect of a 25 basis point monetary policy shock to output declines from 0.347% to 0.341%. Terms of use: Documents in JEL-Classification: E30, E31, E32, E50