Strategic social media use positively influences organizational goals such as the long-term accrual of social capital, and thus social media information governance has become an increasingly important organizational objective. It is particularly important for humanitarian nongovernmental organizations (HNGOs), whose work relies on accurate and timely information regarding socially altruistic behavior (donations, volunteerism, etc.). Despite the potential of social media for increasing social capital, tensions in governing social media information across an organization's different operational levels (regional, intermediate, and national) pose a difficult challenge. Prominent governance frameworks offer little guidance, as their focus on control and incremental policymaking is largely incompatible with the processes, roles, standards, and metrics needed for managing self-governing social media. This study offers a notion of dynamic and co-evolutionary process management of multi-level organizations as a means of conceptualizing social media information governance for the accrual of organizational social capital. Based on interviews with members of HNGOs, this study reveals tensions that emerge within eight focus areas of accruing social capital in multi-level organizations, explains how dynamic process management can ease those tensions, and proposes corresponding strategy recommendations.

Self-disclosure on social network sites (SNSs) constitutes a feedback necessity as well as a potential privacy risk. We integrate both perspectives by studying privacy-related factors that influence self-disclosure: perceived control, trust in provider and perceived privacy risk. We further propose the application of digital nudging as a conceptual basis for interventions that is similar to persuasion but focuses on informed and consistent decision-making. In a qualitative assessment of persuasive elements used by the SNS Facebook, we collect and present currently-used intervention and behaviour change strategies. Two privacy-related nudges are selected for a quantitative study with N = 382 in Germany. Regression analyses show effects of control, trust and risk on self-disclosure. The identified nudges aiming at a higher privacy awareness do not yield clear results. We find indications that nudges may have a converse effect, meaning that reminders to change privacy settings trigger privacy concerns. The results are discussed in respect to short- and long-term changes of perceptions. Furthermore, we propose perceived control as the best influential factor. The study contributes by depicting the current usage of persuasive elements on Facebook and studying their impact on privacy factors.

This study focuses on Twitter affordances and sense-making outcomes during a single emergency situation. By using an interpretive affordance lens, this study aims to assess rumors as influencers of sense-making during the 2017 Manchester terrorist attack. The authors combined a quantitative network analysis with a qualitative content analysis to assess the role of rumors during the emergency management after the attack. This study provides argumentative grounds for the notion of sense-making as a consequence of affording social media and builds on prior research to place sense-making as a cognitive process within the affordance concept. The authors emphasize new potentials to prevent or control rumors on social media for practitioners and contribute insights to rumor research. Namely, the authors contribute a novel perspective of rumors and their role during emergency management on social media.

The growing importance of social media in conflicts and crises is accompanied by an ever-increasing research interest in the crisis informatics field in order to identify potential benefits and develop measures against the technology’s abuse. This special issue sets out to give an overview of current research on the use of social media in conflicts and crises. In doing so, it focuses on both good and malicious aspects of social media and includes a variety of papers of conceptual, theoretical and empirical nature. In six sections, the special issue presents an overview of the field, analytical methods, technical challenges, current advancements and the accepted papers before concluding. Specific topics range from cyber deception over information trustworthiness to mining and near-real-time processing of social media data.

Information systems such as social media strongly influence public opinion formation. Additionally, communication on the internet is shaped by individuals and organisations with various aims. This environment has given rise to phenomena such as manipulated content, fake news, and social bots. To examine the influence of manipulated opinions, we draw on the spiral of silence theory and complex adaptive systems. We translate empirical evidence of individual behaviour into an agent-based model and show that the model results in the emergence of a consensus on the collective level. In contrast to most previous approaches, this model explicitly represents interactions as a network. The most central actor in the network determines the final consensus 60–70% of the time. We then use the model to examine the influence of manipulative actors such as social bots on public opinion formation. The results indicate that, in a highly polarised setting, depending on their network position and the overall network density, bot participation by as little as 2–4% of a communication network can be sufficient to tip over the opinion climate in two out of three cases. These findings demonstrate a mechanism by which bots could shape the norms adopted by social media users.

This article addresses how uncertainties during crisis situations evolve over time and how social positions dynamically affect the collective sense-making process in social media crisis communication. We carried out two case studies on Twitter: (1) the Brussels attacks (2016) with 4,390,784 tweets and (2) the Munich rampage (2016) with 1,258,227 tweets. By applying computed regression-based time-series analyses, we revealed the underlying tweet behavior. As next steps, we trained a machine learning algorithm to identify tweets that express uncertainty and we conducted social network analyses to determine the most influential actors and their social positions. The results reveal that tweet behavior in early crisis stages is dominated by information distribution and guided by content that is characterized by a high percentage of tweets expressing uncertainty. Based on our results, we identified two forms of collective sense-making: (1) acute and guided collective sense-making and (2) evaluative and retrospective collective sense-making.