Band 56, Heft 5 - Special Issue: Social Media / Katrin Weller, Markus Strohmaier

A set of 1.4 million biomedical papers was analyzed with regards to how often articles are mentioned on Twitter or saved by users on Mendeley. While Twitter is a microblogging platform used by a general audience to distribute information, Mendeley is a reference manager targeted at an academic user group to organize scholarly literature. Both platforms are used as sources for so-called “altmetrics” to measure a new kind of research impact. This analysis shows in how far they differ and compare to traditional citation impact metrics based on a large set of PubMed papers.

Students and faculty members have always interacted informally. However, Facebook and other social networking sites (SNSs) present interaction spaces that blur the boundaries between professional, academic and personal lives. The impetus for this study is to understand the extent of SNS-mediated informal interactions between faculty members and students and the policies, preferences, and practices that inform these interactions. Adopting a multiple-case approach, interviews with faculty members and graduate students at three North American library and information science schools were completed to examine behaviors when interacting through Facebook. Additionally, syllabi at two of the case settings were analyzed to see if they contain any polices, guidelines, or other expectations for faculty and student interactions via SNSs. Interview findings show that a majority of faculty members and students prefer not to interact via Facebook, with several having developed ad hoc, informal personal guidelines informing their decision-making. No faculty or students reported explicit SNS policies or guidelines at their respective institutions addressing faculty-student interactions. The lack of explicit directives was apparent in the syllabi analysis as well. The implications for higher education and development of policies are discussed.

With the event of Social Media, well established principles of scholarly communication have changed dramatically. For centuries libraries served as single point of access to scientific literature. Social Media however brings up new publication forms, e. g. scientific wikis, new communication channels, e. g. micro-blogs, or community driven research and publication envirnoments, e. g. Mendeley or ResearchGate. Based upon our own experiences within ZBW – Leibniz Information Centre for Economics, we present how research and development in computer science will help scientific libraries to manage the necessary change within the next decade.

Computational social science has been described as a new field at the intersection of computer science and social sciences, aiming to study the ways that society evolves, interacts, and reacts. Like prospectors sifting the sand in a river bed for gold, computational social science researchers are looking into the streams of social media for insight on our social interactions. Enabled by the availability of and easy accessibility to vast amounts of data generated by social entities, as well as by powerful computing hardware and algorithms, its researchers conduct observations of social interaction and experiments testing social theories in scales not realizable before. In this paper, after a short review of the characteristics of this new area, we discuss issues related to the types of data sought and used, and some of the challenges in collecting and interpreting the data. Throughout the paper we also examine some of the pitfalls awaiting and the standards that need to be observed.

Scholarly research into the uses of social media has become a major area of growth in recent years, as the adoption of social media for public communication itself has continued apace. While social media platforms provide ready avenues for data access through their Application Programming interfaces, it is increasingly important to think through exactly what these data represent, and what conclusions about the role of social media in society the research which is based on such data therefore enables. This article explores these issues especially for one of the currently leading social media platforms: Twitter .

Today, our more-than-ever digital lives leave significant footprints in cyberspace. Large scale collections of these socially generated footprints, often known as big data, could help us to re-investigate different aspects of our social collective behaviour in a quantitative framework. In this contribution we discuss one such possibility: the monitoring and predicting of popularity dynamics of candidates and parties through the analysis of socially generated data on the web during electoral campaigns. Such data offer considerable possibility for improving our awareness of popularity dynamics. However they also suffer from significant drawbacks in terms of representativeness and generalisability. In this paper we discuss potential ways around such problems, suggesting the nature of different political systems and contexts might lend differing levels of predictive power to certain types of data source. We offer an initial exploratory test of these ideas, focussing on two data streams, Wikipedia page views and Google search queries. On the basis of this data, we present popularity dynamics from real case examples of recent elections in three different countries.

Complex network analysis is concerned with identifying statistically significant patterns in large and complex networks. A complex network is an abstract model of a complex system; it represents a well-chosen set of entities as nodes and one or more types of relationships between them as edges. Methods from complex network analysis have been used to identify small molecules called miRNAs that are able to stop breast cancer [13], to understand possible privacy breaches [9], or to analyze how humans solve complex problems [10]. As many areas in our globalized world tend to get more interconnected, the methods from complex network analysis became more important: for complex systems with an underlying network structure, the framework of complex network analysis provides the potential to identify central nodes, to describe deviating substructures, and to reveal the interaction between structure and function of complex networks. Based on this potential impact of network analysis on many fields of society, my work concentrates on understanding when to use which kind of network measure to analyze complex networks and where their limits are, a field I call network analysis literacy . This question can easily be generalized to data analysis literacy which can be even more generalized to the influence of our modern IT-systems on the individual, on organizations, and on society at large, which culminates in a new field of study called socioinformatics .

To date, it is not fully understood why people make errors in the design of business processes. Certainly, one reason is the boundedness of human cognitive abilities. Cognitive abilities are, for instance, restricted by the knowledge or memorability of process modelers. Due to this boundedness people make errors particularly in large and complex models while similar errors are not made in smaller and less complex models. My research focus lies in the investigation of tools and techniques that support users in the design of business process models in a way that improves productivity and efficiency (e. g., understanding and implementing the process model). This paper summarizes three research projects aiming at business process modeling support. The intention of my research is to contribute to the understanding of sociotechnical systems, which are an interplay between humans, information and communication technologies, and organizations. The first project facilitates support for process modeling with a recommendation system. Such a system has been developed in order to assist users twofold in purpose-oriented modeling of processes. The popularity of such a system increases when process models are recommended that can be rapidly understood. My second research project is the investigation of the empirical quality (i.e. structural and textual properties) of process models. To motivate users to reuse process model recommendations, process model reuse must be understood as a common practice and efficient tools and techniques must be provided, which I tackle in my third project.