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Social media today provide an impressive amount of data about users and their societal interactions, thereby offering computer and social scientists, economists, and statisticiansamong others-many new opportunities for research exploration. Arguably, one of the most interesting lines of work is that of predicting future events and developments based on social media data, as we have recently seen in the areas of politics, finance, entertainment, market demands, health, etc. In fact, an average of one in seven research papers presented at the WWW, But what can be successfully predicted and why? Since the first algorithms and techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains.Better understanding the predictive power and limitations of social media is therefore of utmost importance, in order to be successful and avoid false expectations, misinformation or unintended consequences. Today, current methods and techniques are far from being well understood, and it is mostly unclear to what extent or under what conditions the different methods for prediction can be applied to social media. While there exists a respectable and growing amount of literature in this area, current work is fragmented, characterized by a lack of commonly accepted evaluation approaches. Yet, this research seems to have reached a sufficient level of interest and relevance to justify a dedicated section.This special section aims to shape a frame of important questions to be addressed in this field, and fill the gaps in current research with presentations of early research on algorithms, techniques, methods and empirical studies aimed at the prediction of future or current events based on user-generated content in social media.2

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T his paper presents new evidence regarding a firm's probability for survival, based on the network structure of the firm's managers. We found that start-ups that have larger informal communication networks increased their chance to survive external shock. Original data have been collected from Israeli software start-ups during the dot-com economic growth. About eight years later, we added information about their ability to survive the burst of the dot-com bubble. From a theoretical point of view, this paper highlights the power of the classic social networks approach in explaining organizational performance. From a practical point of view, these findings offer some guidelines for managers of start-ups. Our results show that the size of informal interfirm networks really matters.

Purpose This 7-year longitudinal study identifies factors influencing the growth of healthcare Virtual Communities of Practices (VCoPs) using metrics from social-network and semantic analysis. Studying online communication along the three dimensions of social interactions (connectivity, interactivity and language use) we aim to provide VCoPs managers with valuable insights to improve the success of their communities. Design/methodology/approach Communications over a period of 7 years (April 2008 to April 2015), and between 14,000 members of 16 different healthcare VCoPs coexisting on the same web-platform, were analyzed. Multilevel regression models were used to reveal the main determinants of community growth over time. Independent variables were derived from social network and semantic analysis measures. Findings Results show that structural and content-based variables predict the growth of the community. Progressively more people will join a community if: its structure is more centralized, leaders are more dynamic (they rotate more), and the language used in the posts is less complex. Research limitations/implications The available dataset included one web platform and a limited number of control variables. In order to consolidate the findings of the present study, the experiment should be replicated on other healthcare VCoPs. Originality/value The study provides useful recommendations for setting up and nurturing the growth of professional communities, considering at the same time the structure of the interaction patterns among community members, the dynamic evolution of these interactions and the use of language. New analytical tools are presented, together with the use of innovative interaction metrics which can significantly influence community growth, such as rotating leadership

We investigate the impact of a novel method called "virtual mirroring" to promote self-reflection and impact customer satisfaction. The method is based on measuring communication patterns, through social network and semantic analysis, and mirroring them back to the individual. Our goal is to demonstrate that self-reflection can trigger a change in communication behaviors. We illustrate and test our approach analyzing e-mails of a large global services company by comparing changes in customer satisfaction associated with team leaders exposed to virtual mirroring (the experimental group). We find an increase in customer satisfaction in the experimental group and a decrease in the control group (team leaders not involved in the virtual mirroring process). With regard to the individual communication indicators, we find that customer satisfaction is higher when employees are more responsive, use a simpler language, are embedded in less centralized communication networks, and show more stable leadership patterns.

This paper studies the temporal communication patterns of online communities of developers and users of the open source Eclipse Java development environment. It measures the productivity of each community and seeks to identify correlations that exist between group communication characteristics and productivity attributes. The study uses the TeCFlow (Temporal Communication Flow) visualizer to create movie maps of the knowledge flow by analyzing the publicly accessible Eclipse developer mailing lists as an approximation of the social networks of developers and users. Thirty-three different Eclipse communities discussing development and use of components of Eclipse such as the Java Development Tools, the different platform components, the C/C++ Development Tools and the AspectJ extension have been analyzed over a period of six months. The temporal evolution of social network variables such as betweenness centrality, density, contribution index, and degree have been computed and plotted. Productivity of each development group is measured in terms of two indices, namely performance and creativity. Performance of a group is defined as the ratio of new bugs submitted compared with bugs fixed within the same period of time. Creativity is calculated as a function of new features proposed and implemented. Preliminary results indicate that there is a correlation between attributes of social networks such as density and betweenness centrality and group productivity measures in an open source development community. We also find a positive correlation between changes over time in betweenness centrality and creativity, and a negative correlation between changes in betweenness centrality and performance.