Academics’ online connections: Characterising the structure of personal networks on academic social networking sites and Twitter

Academic social networking sites (SNS), such as Academia.edu and ResearchGate, seek to bring the benefits of online social networking to academics' professional lives. Online academic social networking offers the potential to revolutionise academic publishing, foster novel collaborations, and empower academics to develop their professional identities online. However, the role that such sites play in relation to academic practice and other social media is not well understood at present. Arguably, the defining characteristic of academic social networking sites is the connections formed between profiles (in contrast to the traditional static academic homepage, for example). The social network of connections fostered by SNSs occupies an interesting space in relation to online identity, being both an attribute of an individual and shaped by the social context they are embedded within. As such, personal network structures may reflect an expression of identity (as "public displays of connection" (Donath & boyd, 2004) or "relational self portraits[s]" (Hogan & Wellman, 2014)), while social capital has been linked to network structures (Crossley et al., 2015). Network structure may therefore have implications for the types of roles that a network can play in professional life. What types of network structures are being fostered by academic SNS and how do they relate to academics' development of an online identity? This presentation will discuss findings from a project which has used a mixed-methods social network analysis approach to analyse academics' personal networks online. The personal networks of 55 academics (sampled from survey participants, to reflect a range of disciplines and job positions) on both one academic SNS (either Academia.edu or ResearchGate) and Twitter were collected and analysed. Differences in network structure emerged according to platform, with Twitter networks being larger and less dense, while academic SNS networks were smaller and more highly clustered. There were differences between academic SNS and Twitter in the brokerage positions occupied by the participant. The results are discussed in relation to other salient studies relating network structure in online social networks to social capital, and implications for academic practice. Future work, including co-interpretive interviews to explore the significance of network structures with participants, is introduced

Spanish Communication Academia: Scientific Productivity vs. Social Activity

At a time when academic activity in the area of communication is principally assessed by the impact of scientific journals, the scientific media and the scientific productivity of researchers, the question arises as to whether social factors condition scientific activity as much as these objective elements. This investigation analyzes the influence of scientific productivity and social activity in the area of communication. We identify a social network of researchers from a compilation of doctoral theses in communication and calculate the scientific production of 180 of the most active researchers who sit on doctoral committees. Social network analysis is then used to study the relations that are formed on these doctoral thesis committees. The results suggest that social factors, rather than individual scientific productivity, positively influence such a key academic and scientific activity as the award of doctoral degrees. Our conclusions point to a disconnection between scientific productivity and the international scope of researchers and their role in the social network. Nevertheless, the consequences of this situation are tempered by the nonhierarchical structure of relations between communication scientists

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Student Network Centrality and Academic Performance: Evidence from United Nations University

In this paper we empirically studied the relationship between network centrality and academic performance among a group of 47 PhD students from UNU-MERIT institute. We conducted an independent email survey and relied on social networks theory as well as standard econometric procedures to analyse the data. We found a significant reversed U-shaped relation between network centrality and students' academic performance. We controlled our results by several node's characteristics such as age, academic background, and research area. Additional evidence shows that there is a negative impact of age on academic performance at PhD student level. Contributions of this paper can refer to the input into studies that aim to explore peereffect. Also it contributes to the methodological approach by combining elements of network analysis and econometric theories. This study demonstrates that when evaluating the impact of network centrality on performance, there is no significant difference between various network centrality measurements.Networks analysis, Network centrality, Peer-effect, Academic performance

When does centrality matter? Scientific productivity and the moderating role of research specialization and cross-community ties

The present study addresses the ongoing debate concerning academic scientific productivity. Specifically, given the increasing number of collaborations in academia and the crucial role networks play in knowledge creation, we investigate the extent to which building social capital within the academic community represents a valuable resource for a scientist's knowledge-creation process. We measure the social capital in terms of structural position within the academic collaborative network. Furthermore, we analyse the extent to which an academic scientist's research specialization and ties that cross-community boundaries act as moderators of the aforementioned relationship. Empirical results derived from an analysis of an Italian academic community from 2001 to 2008 suggest academic scientists that build social capital by occupying central positions in the community outperform their more isolated colleagues. However, scientific productivity declines beyond a certain threshold value of centrality, hence revealing the existence of an inverted U-shaped relationship. This relationship is negatively moderated by the extent to which an academic focuses research activities in few scientific knowledge domains, whereas it is positively moderated by the number of cross-community ties established

iSchools and Social Identity ??? A Social Network Analysis

We analyze the publication co-authorship network of an iSchool faculty community using ???Social Identity Theory??? as the theoretical lens. Initially, we discuss the need for a theoretical framework to analyze and interpret social network data. Then, we find out the patterns in the levels of interaction happening within the faculty community at an inter-group level. We grouped faculty members into different clusters according to several parameters such as their educational backgrounds, affiliations with research centers/labs, and h-indices. We based our analysis on this classification and we try to understand the relationship among social identity, group affiliation and academic collaborations. We conclude with the remarks that one could avoid idiosyncratic ways of interpreting social network data by using a proven theoretical lens like ???Social Identity Theory??

Vibrant and engaging online social learning: an innovative response to threatened part-time study in Higher Education

Austerity measures and increased tuition fees place heightened pressures on universities to provide sustainable, cost effective, high quality provision. This paper analyses how a team of staff in a School of Education at a UK University are leading collaborative work with partner colleges, to deliver a model that ameliorates the financial pressures, whilst developing high quality student-centred engagement for part-time students. When face-to-face teaching sessions were significantly reduced, an online academic social network for tutors and students was introduced to encourage collaboration, peer support and ‘coffee room’ discussion. Feedback from participants through focus groups and surveys confirmed a social support network as important for engagement and was perceived as supporting achievement, even by those who were reluctant to join the network. Recommendations include: more time face-to-face at the beginning of the course, more online tutor presence and scaffolded activities to build confidence in using an academic social network