The outbreak of the COVID-19 pandemic was accompanied by a massive flood of “fake news”, conspiracy theories, and distorted information more generally. Alternative news media have been accused of contributing to this pollution the information sphere. In this article, we argue that audiences with a counter-hegemonic, ideologically biased worldview are particularly likely to use alternative counter-news, because these outlets validate their worldview ― and worldview validation being a relevant psychological need in times of crises. We suggest that by turning to alternative counter-news, audiences increase their exposure to fake news and end up being less well informed about important events. Results of a random-quota survey in Germany (N = 967) confirmed that those with higher levels of conspiracy mentality and lower media trust were more likely to use alternative counter-news. Alternative counter-news users recalled more fake news than non-users and alternative counter-news use partially mediated the relationship between counter-hegemonic attitudes and fake news recall. Thus, although not all content in alternative counter-news is fake news, these outlets do attract a specific counter-hegemonic audience and they do contribute to the “pollution” of the information ecosystem.

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.

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.