Measuring research impact is important for ranking publications in academic search engines and for research evaluation. Social media metrics or altmetrics measure the impact of scientific work based on social media activity. Altmetrics are complementary to traditional, citation-based metrics, e.g. allowing the assessment of new publications for which citations are not yet available. Despite the increasing importance of altmetrics, their characteristics are not well understood: Until now it has not been researched what kind of researchers are actively using which social media services and why - important questions for scientific impact prediction. Based on a survey among 3,430 scientists, we uncover previously unknown and significant differences between social media services: We identify services which attract young and experienced researchers, respectively, and detect differences in usage motivations. Our findings have direct implications for the future design of altmetrics for scientific impact prediction.
Scholars use social media for various reasons. Van Noorden @cite_26 identified multiple categories of motivations for scholarly social media use: and . Jordan @cite_6 identified motivation categories by manually coding questions asked by researchers on Academia.edu. It is well known that a small minority of active social media users is responsible for a large share of activities @cite_2 @cite_18 . @cite_10 give an overview of multiple studies on various social media sites and Kunegis @cite_0 shows statistics for dozens of social networks, all confirming the effect. To the best of our knowledge, there is no study on the demographics and motivations of scholarly users on social media.
@cite_18 @cite_26 @cite_6 @cite_0 @cite_2 @cite_10
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Social networking sites (SNS) aimed at academics have the potential to enhance academic practice through developing an online academic identity and as a portal to further opportunities for collaboration and communication. This paper explores part of the communicative affordance offered by academic SNS through an analysis of the questions posed by academics via the Academia.edu website. We present the Koblenz Network Collection (KONECT), a project to collect network datasets in the areas of web science, network science and related areas, as well as provide tools for their analysis. In the cited areas, a surprisingly large number of very heterogeneous data can be modeled as networks and consequently, a unified representation of networks can be used to gain insight into many kinds of problems. Due to the emergence of the World Wide Web in the last decades many such datasets are now openly available. The KONECT project thus has the goal of collecting many diverse network datasets from the Web, and providing a way for their systematic study. The main parts of KONECT are (1) a collection of over 160 network datasets, consisting of directed, undirected, unipartite, bipartite, weighted, unweighted, signed and temporal networks collected from the Web, (2) a Matlab toolbox for network analysis and (3) a website giving a compact overview the various computed statistics and plots. In this paper, we describe KONECT's taxonomy of networks datasets, give an overview of the datasets included, review the supported statistics and plots, and briefly discuss KONECT's role in the area of web science and network science. Usenet may be regarded as the world’s largest and fastest growing conversational application. In 1988 there were fewer than 500 newsgroups. Current estimates vary, but at the time of our data collection in December 1996, there were over 17,000 newsgroups, with approximately 3 million users worldwide (Harrison, 1994). This growth has been achieved without any centralized organization or governing body (King, 1997). The ubiquity of Usenet, and the fact that it supports conversations between hundreds or even thousands of participants, provides the opportunity to study what we term mass interaction. However, we currently lack basic data about Usenet interactions. The current paper analyses over 2.15 million messages produced by 659,450 people in 500 representative newsgroups collected over 6 months. We provide descriptive data about newsgroup demographics, communication strategies, and interactivity. We then derive predictions from the common ground model of communication to test predictions about how these parameters interact. In recent years there has been a dramatic rise in the number of participatory media technologies which museums have employed to engage people in new ways under the rubric of Web 2.0. Blogs, wikis, podcasts, vodcasts, photo and video sharing, virtual environments, tagging, annotation and other authoring tools offer people new opportunities to engage with museum content processes through co-creation and interactive cultural experiences. Arguably, these platforms and tools are creating new relationships between institutions and the public.