Demystifying Social Bots: On the Intelligence of Automated Social Media Actors

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

MODELING AND OPTIMAL CONTROL OF ABLE-BODIED AND UNILATERAL AMPUTEE RUNNING

The remarkable performances of amputee athletes in sprint competitions aroused media and scientific interest and led to the question whether running-specific prostheses can be an advantage with respect to able-bodied running. The aim of this study was to bring together motion capture data and Scientific Computing methods to analyze the running motions of an able-bodied and a unilateral transtibial amputee athlete. For each of them a rigid multibody system model was created. By application of optimal control techniques, the dynamics of reference running movements from motion capture data was reconstructed for both models. The able-bodied and the transtibial amputee sprinters rely on dissimilar actuation strategies to perform similar running motions

Behind Blue Skies: A Multimodal Automated Content Analysis of Islamic Extremist Propaganda on Instagram

Social media platforms, such as Instagram, are regularly misused for spreading covert (Islamic) extremist propaganda. Affect and emotion are central tools used in extremist propaganda, but there is little research into the combined employment of different social media elements, such as hashtags, visuals, and texts, in the context of propaganda. This study contributes to closing this gap. Using the German group Generation Islam as a case study, we examined the group’s Instagram activity ( N  = 1,187 posts) over the course of 2 years. To reflect the platform users’ logic, we (a) examined affect in hashtag networks in which users can come across propagandistic content, (b) employed deep learning to examine the emotional valence transmitted in the visuals, and (c) used automated linguistic analysis to describe collective action cues contained within the texts. The results are novel, as they provide nuanced insights into extremist propaganda’s employment of affect and emotions across Instagram’s affordances

Process-Oriented Stream Classification Pipeline: A Literature Review

Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field

Process-Oriented Stream Classification Pipeline: A Literature Review

Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field

Social Influence Analysis (SIA) in Online Social Networks

Online social networks have become globally ubiquitous, and therefore are an arena where important social phenomena can be observed: e.g. diffusion of (dis)information, social and political polarization, as well as distribution of hate speech and radical content. To understand their spread and effects, it is important to analyze and model the notion of social influence in online networks. For empirical modeling, it is crucial to study the relational nature of interactions between users of the networks, together with analyzing the content of communications between them. This research focuses on investigating social influence in online social networks as the fundamental principle for information diffusion that needs to be modeled, parameterized, and measured. According to Google Scholar, in the first 5 months of 2022 alone, scholars from multiple disciplines roughly produced 30,000 papers dealing with the concept of influence in online social networks. This indicates that this research is indeed a multidisciplinary challenge