In the last 10 years, many canonical findings in the social sciences appear unreliable. This so-called “replication crisis” has spurred calls for open science practices, which aim to increase the reproducibility, replicability, and generalizability of findings. Communication research is subject to many of the same challenges that have caused low replicability in other fields. As a result, we propose an agenda for adopting open science practices in Communication, which includes the following seven suggestions: (1) publish materials, data, and code; (2) preregister studies and submit registered reports; (3) conduct replications; (4) collaborate; (5) foster open science skills; (6) implement Transparency and Openness Promotion Guidelines; and (7) incentivize open science practices. Although in our agenda we focus mostly on quantitative research, we also reflect on open science practices relevant to qualitative research. We conclude by discussing potential objections and concerns associated with open science practices.

Social media technologies offer several features that allow users to monitor other people’s opinions on public issues. Initial research showed that user-generated content can shape recipients’ perceptions of the majority opinion on societal problems. Still, it remains largely unexplored under which circumstances people gauge other users’ opinions through social media and whether perceived opinion climates affect people’s opinions and communication behavior in these environments. Results of a two-session experiment revealed that people’s fear of isolation sharpens their attention toward user-generated comments on Facebook which, in turn, affect recipients’ public opinion perceptions. The latter influenced subjects’ opinions and their willingness to participate in social media discussions. These findings are discussed in light of the spiral of silence theory and the social projection hypothesis.

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.

With the increase in algorithms on social media, scholarship is increasingly focused on “algorithmic literacy,” or users’ understanding of algorithms. Algorithmic literacy is multi-faceted (knowledge, attitudes, and behavior), and researchers are still uncovering how these facets are connected. This article presents a preregistered survey of social media users from two western countries: the United States (n = 990) and Germany (n = 1117). Results show key predictors of algorithmic awareness—age, education, frequency of social media use—are the same in both countries. Nevertheless, U.S. social media users show higher algorithmic awareness and more positive attitudes toward algorithms than German social media users, likely due to their higher overall social media usage. Results also indicate that algorithmic awareness predicts attitudes about filtering algorithms depending on users’ defense motivations or accuracy motivations and behaviors to counteract filtering. These patterns have implications for literacy interventions and for increasing algorithmic transparency. Algorithms are formulas that decide what people see on social media. Not all social media users know how these algorithms work. This means they might not see information that others see. We asked social media users from the United States and Germany to complete an online survey. They answered questions about their social media use, what they know about algorithms, and how they feel about them. We wanted to know why some people understand algorithms better than others. Researchers call this understanding “algorithmic literacy.” We found that younger users, those with more education, and those who use social media more are more aware of algorithms. Overall, U.S. social media users were more aware than German users. They also felt more positive about algorithms. This is probably because they use social media more. We also found that how people feel about algorithms depends on what they want to use them for. This information will help researchers teach people who use social media about what algorithms do.