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

Dynamics of private social networks

Social networks, have been a significant turning point in ways individuals and companies interact. Various research has also revolved around public social networks, such as Twitter and Facebook. In most cases trying to understand what's happening in the network such predicting trends, and identifying natural phenomenon. Seeing the growth of public social networks several corporations have sought to build their own private networks to enable their staff to share knowledge, and expertise. Little research has been done in regards to the value private networks give to their stake holders. This is primarily due to the fact as their name implies, these networks are private, thus access to internal data is limited to a trusted few. This paper looks at a particular online private social network, and seeks to investigate the research possibilities made available, and how this can bring value to the organisation which runs the network. Notwithstanding the limitations of the network, this paper seeks to explore the connections graph between members of the network, as well as understanding the topics discussed within the network. The findings show that by visualising a social network one can assess the success or failure of their online networks. The Analysis conducted can also identify skill shortages within areas of the network, thus allowing corporations to take action and rectify any potential problems.peer-reviewe

Multi-dimensional Conversation Analysis across Online Social Networks

With the advance of the Internet, ordinary users have created multiple personal accounts on online social networks, and interactions among these social network users have recently been tagged with location information. In this work, we observe user interactions across two popular online social networks, Facebook and Twitter, and analyze which factors lead to retweet/like interactions for tweets/posts. In addition to the named entities, lexical errors and expressed sentiments in these data items, we also consider the impact of shared user locations on user interactions. In particular, we show that geolocations of users can greatly affect which social network post/tweet will be liked/ retweeted. We believe that the results of our analysis can help researchers to understand which social network content will have better visibility.Comment: Datasets will be anonymized and published at: http://akcora.wordpress.com/2013/12/24/pointer-for-datasets

Gaming on and off the social graph: the social structure of Facebook games

Games built on Online Social Networks (OSNs) have become a phenomenon since 3rd party developer tools were released by OSNs such as Facebook. However, apart from their explosive popularity, little is known about the nature of the social networks that are built during play. In this paper, we present the findings of a network analysis study carried out on two Facebook applications, in comparison with a similar but stand-alone game. We found that games built both on and off a social graph exhibit similar social properties. Specifically, the distribution of player-to-player interactions decays as a power law with a similar exponent for the majority of players. For games built on the social network platform however, we find that the networks are characterised by a sharp cut-off, compared with the classically scale-free nature of the social network for the game not built on an existing social graph

Extraction and Analysis of Facebook Friendship Relations

Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms

Reading the Source Code of Social Ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web (WebSci'14