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This article uses a social capital framework to examine whether and how the use of three types of publicly accessible social media (LinkedIn, Twitter, Facebook) is related to professional informational benefits among a representative sample of Dutch online users. Professional informational benefits were conceptualized as the (timely) access to relevant information and being referred to career opportunities. The effect of content and structure of the respective online network on professional informational benefits was examined on the general (users vs. non-users of a platform) and more fine-grained level (within users of a specific platform). Overall, users of LinkedIn and Twitter reported higher informational benefits than non-users, whereas the Facebook users reported lower informational benefits. Posting about work and strategically selecting ties consistently predicted informational benefits. The network composition mattered most on LinkedIn; strong and weak ties predicted informational benefits. The results demonstrate the usefulness of the social capital framework.

This article focuses on the role of reputation in consumer communities. Reputation systems can have a sanctioning function (incentive for good conduct) or a signalling function (e.g. signalling competence or trustworthiness). If the sanctioning function is dominant, striving for reputation should be the major motivation for contribution. However, contributions also can be motivated by altruism, the pleasure of interaction, earning money or generalized reciprocity. An online study of members of a German consumer community assessed the motivations for contribution and the evaluation of various features of the site. Overall, reputation turned out to be the least important motivation. Five types of users could be distinguished, but only the multiple motive consumers scored high on reputation. However, all community members perceived the quality ratings of the reviews as very important. The findings suggest that reputation has mainly a signalling function, but not so much of a sanctioning function in consumer communities.

Social media, especially social network sites (SNS) such as Facebook have grown rapidly in popularity in the last ten years. [...]

In times of the COVID-19 pandemic, difficult decisions such as the distribution of ventilators must be made. For many of these decisions, humans could team up with algorithms; however, people often prefer human decision-makers. We examined the role of situational (morality of the scenario; perspective) and individual factors (need for leadership; conventionalism) for algorithm preference in a preregistered online experiment with German adults (n = 1,127). As expected, algorithm preference was lowest in the most moral-laden scenario. The effect of perspective (i.e., decision-makers vs. decision targets) was only significant in the most moral scenario. Need for leadership predicted a stronger algorithm preference, whereas conventionalism was related to weaker algorithm preference. Exploratory analyses revealed that attitudes and knowledge also mattered, stressing the importance of individual factors.

Do people who express negative feelings (loneliness, sadness) on Twitter gain or lose online contacts? To answer this question, the authors tracked the number of followers and followees of people who tweeted about loneliness or sadness twice; once when they expressed the negative feeling and a second time five months later. The authors compared the networks of those users with the networks of others who either simply retweeted tweets about loneliness/sadness or (re)tweeted about the corresponding positive feelings. People expressing loneliness in their tweets, as well as people expressing sadness in their tweets had smaller networks than people expressing feeling loved or happy. This effect held only for the original tweets, not retweets, and was – in case of sad/happy – stronger for the followees than the followers. Moreover, the authors found that people expressing loneliness also had smaller friends' networks five months later than the people expressing feeling loved, and that the networks of the people expressing sadness became even smaller during the following five months.

The paper explains antecedents and consequences of news during the BP oil spill crisis by analyzing newspaper and internet coverage as well as financial indicators. The study establishes the roles of routines in financial journalism and of BP's public relations efforts in building the U.S. media agenda. The U.S. media agenda in turn bears a classic agenda-setting effect on public awareness, an intermedia agenda-setting effect on foreign media, and a stakeholder agenda-setting effect on financial markets. A second-level attribute agenda-setting post-hoc study reveals that these first-order agenda setting effects depend on the resonance of specific problems and solutions with specific interests and a specific frame of mind. Financial stakeholders, for example, reacted negatively to news about judicial accountability, but positively to press releases about BP's skills in implementing solutions. The findings contradict research which states that the news in classic media merely mirrors share prices.

Networks of Practice (NoPs) facilitate knowledge sharing among geographically dispersed organization members. This research tests whether social influence in NoPs is reinforced by actors' embeddedness in practice (knowledge about informal content), organizational embeddedness (knowledge about formal organizational content), structural embeddedness (knowledge about who knows what), and relational embeddedness (knowledge about informal relationships). A full-fledged automated content analysis on all postings on four NoPs maintained by a multinational chemical company revealed four dimensions in communication content that largely coincide with the proposed embeddedness types. We measured social influence by assessing to what extent actors' use of uncommon language traits was adopted in the responses to the postings. Hypothesis testing revealed that network members who communicate about informal practice, and know who knows what, exert more social influence than others. The results suggest that network members' social influence is rooted in their utilitarian value for others, and not in their organizational or relational embeddedness.

AbstractCommunication scholars are increasingly concerned with interactions between humans and communicative agents. These agents, however, are considerably different from digital or social media: They are designed and perceived as life-like communication partners (i.e., as "communicative subjects"), which in turn poses distinct challenges for their empirical study. Hence, in this paper, we document, discuss, and evaluate potentials and pitfalls that typically arise for communication scholars when investigating simulated or non-simulated interactions between humans and chatbots, voice assistants, or social robots. In this paper, we focus on experiments (including pre-recorded stimuli, vignettes and the "Wizard of Oz"-technique) and field studies. Overall, this paper aims to provide guidance and support for communication scholars who want to empirically study human-machine communication. To this end, we not only compile potential challenges, but also recommend specific strategies and approaches. In addition, our reflections on current methodological challenges serve as a starting point for discussions in communication science on how meaning-making between humans and machines can be investigated in the best way possible, as illustrated in the concluding section.