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In light of the growing politicization of social media, the spiral of silence theory and its predictions on the conditions under which individuals express political opinions have gained increasing scholarly attention. This study contributes to this line of research by identifying the influence of a central characteristic of social media: message persistence. It was expected that high technical durability of political messages reduces users' propensity to voice their opinion, moderating the silence effect. A pre-registered experiment ( N = 772) revealed a small-to-medium persistence effect in three out of four topical contexts. While perceived congruence with the opinion climate was not associated with the likelihood of opinion expression, the latter could be explained by a mental cost-benefit calculus that was shaped by message persistence. Theoretical implications are discussed referring to (a) a situational approach regarding silencing processes on social media and (b) its connection to a behavioral calculus of human communication.

The growing significance of social networking sites (SNSs) as venues for political exchanges between citizens raises questions on the consequences of their use. This pre-registered experiment ( N = 704) aexamined to what extent a gradual variation of congruence between users' opinions and the opinion climate they encounter on SNSs affect their strength of opinion and selective exposure. No effects were found from the level of congruence on selective exposure, while exploratory analyses suggested that exposure to overly congruent opinion climates can lead to marginally stronger opinions. Building on research into political social identities which suggests polarizing effects of the latter, interaction effects of users' ideological identities and exposure to opinions on SNS were additionally investigated. However, the present work found no indication that effects of congruence are modulated by identity salience. Taken together, findings of this study suggest that socially divisive effects of like-minded or non-like-minded opinion climates conveyed by SNS may be limited.

The increasing role of algorithms shaping our use of communication technology—particularly on social media—comes with a growth of empirical research attempting to assess how literate users are regarding these algorithms. This rapidly emerging field is marked by great diversity in terms of how it theorizes and measures our understanding of algorithms, due, in part, to the opaque "black box" nature of the algorithms themselves. In this review article, we summarize the state of knowledge on algorithmic literacy, including its definitions, development, measurement, and current theorizing on human–algorithm interaction. Drawing on this existing work, we propose an agenda including four different directions that future research could focus on: (1) balancing users' expectations of algorithmic literacy with developers' responsibility for algorithmic transparency, (2) methods for engaging users in increasing their literacy, (3) further developing the affective and behavioral facets of literacy, and (4) addressing the new algorithmic divide.

This work proposes the expectation of sanctions as a promising construct to advance spiral of silence research in face-to-face and computer-mediated contexts. We argue that situational factors influence people's expectations about how their social environment would punish them should they express their viewpoint in a hostile opinion climate. These expected sanctions are suggested to explain the variance in people's willingness to express a minority opinion across different social situations. An experiment showed that the expectation of being personally attacked can explain why people are more willing to voice a deviant opinion in offline rather than online environments. Findings also revealed that in contemporary social networking websites, wherein users commonly face a personally relevant audience, people are prone to hold back their opinion as they expect losing control over the reactions of their audience. This research extends previous knowledge by presenting a more differentiated theoretical view of the fear of isolation and specifying its role in different situations of public deliberation.

Abstract
To date, the role of user behavior in the formation of politically homogeneous online environments (oftentimes called echo chambers) is not fully understood. Building on selective exposure research, we introduce the notion of selective political friending, that is, the preference for political like-mindedness in social affiliations on social networking sites. In a pre-registered laboratory experiment with users of social networking sites in Germany (N = 199), we find that users preferably build connections with those who share their opinions toward controversial political issues. Political like-mindedness outperforms other friending criteria such as popularity or career-related fit with another user. Political friending is pronounced when individuals' pre-existing opinions are strong. The present study points to the necessity to take the motivational complexity into account when studying phenomena linked to political homogeneity on SNS.

Research has documented that social media allow individuals to encounter political disagreement, potentially fostering deliberation in democratic systems. Another strand of studies has indicated that individuals tend to actively homogenize their network by removing (unfriending) political dissenters. To shed light on the psychological connection between encountering political disagreement online and acting in response, this study presents results from in-depth interviews with 20 German Facebook users. In line with cognitive dissonance theory, we found that users engage in different intrapsychic and behavioral strategies to reduce the dissonance provoked by political disagreements, such as mentally discrediting the source or legitimizing the existence of alternative viewpoints. Decisions about drastic measures, such as unfriending the source, are conditional on whether individuals perceive the disagreements as relatively severe or the relationship to the dissenter as relatively close. These findings inform literature on cross-cutting exposure and the political homogenization of online networks.

Since social media have increasingly become forums to exchange personal opinions, more and more approaches have been suggested to analyze those sentiments automatically. Neural networks and traditional machine learning methods allow individual adaption by training the data, tailoring the algorithm to the particular topic that is discussed. Still, a great number of methodological combinations involving algorithms (e.g., recurrent neural networks (RNN)), techniques (e.g., word2vec), and methods (e.g., Skip-Gram) are possible. This work offers a systematic comparison of sentiment analytical approaches using different word embeddings with RNN architectures and traditional machine learning techniques. Using German comments of controversial political discussions on YouTube, this study uses metrics such as F1-score, precision and recall to compare the quality of performance of different approaches. First results show that deep neural networks outperform multiclass prediction with small datasets in contrast to traditional machine learning models with word embeddings.

Research on non-hedonic entertainment suggests the experience of elevation as an important construct leading to beneficial outcomes such as prosocial motivation. This study builds on first findings in this realm by distinguishing between different meaningful media contents. In a 3 × 4 between-subjects online experiment, we varied type of video (beauty of the earth, unity of humankind, portrayals of human kindness, and funny control videos) and context of proliferation (presentation on an unknown video platform or on YouTube with low vs high number of views). Meaningful videos indeed led to greater elevation, more universal orientation, and prosocial motivation—with videos showing human kindness standing out against other forms of meaningful videos. Human kindness videos additionally fostered more positive attitudes toward stereotyped groups—mediated by the feeling of elevation and the subsequent feeling of universal orientation.

. ; Policy Paper (Forum Privatheit und selbstbestimmtes Leben in der digitalen Welt)