Features of network interaction: the method of qualitative analysis and visualization online ego – networks

The method of qualitative analysis of Online Ego – networks visualization makes it possible to reveal the characteristic features of network interaction for Social Network Sites, to interpret the structural features of the Ego - networks, discover the main strategies used by participants to form network ties through online interactions. Analysis of 82 cases of Ego - networks has revealed different areas of community structure related to interests, ideologies and activities of the actors. At the same time, we have identified three main strategies of online ties formation: (1) formation of a single bond(2) formation of bonds forming a structurally homogeneous community(3) formation of bonds forming a heterogeneous structure with clearly defined communities. In addition, our results show that toward online networks from 4 to 1255 people: intimate network is about 5 people, a network of support and active contacts is about 8-10 and the average limit for the extended network - 150 -200 people.Метод качественного анализа визуализации Online Эго – сетей позволяет выявить характерные особенности сетевого взаимодействия на Сетевых Сайтах, интерпретировать особенности структуры Эго – сетей, обнаружить основные стратегии, используемые участниками online взаимодействий для формирования сетевых связей. Анализ 82 Эго – сетей позволил обнаружить структурные сообщества, связанные с интересами, мировоззрениями и деятельностью акторов. При этом мы выделили три основных стратегии образования связей: (1) формирование одиночных связи(2) формирование связей, образующих структурно гомогенные сообщества(3) формирование связей, образующих гетерогенную структуру с четко очерченными сообществами. Кроме того, наши результаты показывают, что для online сетей от 4 до 1255 человек: личная сеть составляет около 5 человек, сеть поддержки и активных контактов - около 8-10, а средний лимит для сети знакомых- 150 -200 человек

Online social networks and networked academic identity

Academic online social networking has received increasing focus in recent years with the development of a number of services aimed specifically at academics. There has, however, been little empirical work on the subject. This study  seeks  to  understand  the  structure  and  role  of  academics’  ego-networks on social networking sites in relation to developing an academic identity and becoming professional in their disciplines. To this end, a mixed-methods social network analysis approach will be used. Current outstanding issues relating to analysis and reporting, ethics, and the role of theory, will be outlined for discussion

Evaluation of Structural and Temporal Properties of Ego Networks for Data Availability in DOSNs

The large diffusion of Online Social Networks (OSNs) has influenced the way people interact with each other. OSNs present several drawbacks, one of the most important is the problem of privacy disclosures. Distributed Online Social Networks (DOSNs) have been proposed as a valid alternative solution to solve this problem. DOSNs are Online Social Networks implemented on a distributed platform, such as a P2P system or a mobile network. However, the decentralization of the control presents several challenges, one of the main ones is guaranteeing data availability without relying on a central server. To this aim, users’ data allocation strategies have to be defined and this requires the knowledge of both structural and temporal characteristics of ego networks which is a difficult task due to the lack of real datasets limiting the research in this field. The goal of this paper is the study of the behaviour of users in a real social network in order to define proper strategies to allocate the users’ data on the DOSN nodes. In particular, we present an analysis of the temporal affinity and the structure of communities and their evolution over the time by using a real Facebook dataset

On the discovery of social roles in large scale social systems

The social role of a participant in a social system is a label conceptualizing the circumstances under which she interacts within it. They may be used as a theoretical tool that explains why and how users participate in an online social system. Social role analysis also serves practical purposes, such as reducing the structure of complex systems to rela- tionships among roles rather than alters, and enabling a comparison of social systems that emerge in similar contexts. This article presents a data-driven approach for the discovery of social roles in large scale social systems. Motivated by an analysis of the present art, the method discovers roles by the conditional triad censuses of user ego-networks, which is a promising tool because they capture the degree to which basic social forces push upon a user to interact with others. Clusters of censuses, inferred from samples of large scale network carefully chosen to preserve local structural prop- erties, define the social roles. The promise of the method is demonstrated by discussing and discovering the roles that emerge in both Facebook and Wikipedia. The article con- cludes with a discussion of the challenges and future opportunities in the discovery of social roles in large social systems

Understanding the structure and role of academics' ego-networks on social networking sites

Academic social networking sites (SNS) seek to bring the benefits of online networking to an academic audience. Currently, the two largest sites are Academia.edu and ResearchGate. The ability to make connections to others is a defining affordance of SNS, but what are the characteristics of the network structures being facilitated by academic SNS, and how does this relate to their professional use by academics? This study addressed this question through mixed methods social network analysis. First, an online survey was conducted to gain contextual data and recruit participants (n = 528). Second, ego-networks were drawn up for a sub-sample of 55 academics (reflecting a range of job positions and disciplines). Ego-networks were sampled from an academic SNS and Twitter for each participant. Third, co-interpretive interviews were held with 18 participants, to understand the significance of the structures and how the networks were constructed. Academic SNS networks were smaller and more highly clustered; Twitter networks were larger and more diffuse. Communities within networks are more frequently defined by institutions and research interests on academic SNS, compared to research topics and personal interests on Twitter. Emerging themes link network structure to differences in how academics conceptualise and use the sites. Academic SNS are regarded as a more formal academic identity, akin to a business card, or used as a personal repository. Twitter is viewed as a space where personal and professional are mixed, similar to a conference coffee break. Academic SNS replicate existing professional connections, Twitter reinforces existing professional relationships and fosters novel connections. Several strategies underpinning academics’ use of the sites were identified, including: circumventing institutional constraints; extending academic space; finding a niche; promotion and impact; and academic freedom. These themes also provide a bridge between academic identity development online and formal academic identity and institutional roles

An Army of Me: Sockpuppets in Online Discussion Communities

In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as "I", and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.Comment: 26th International World Wide Web conference 2017 (WWW 2017

Supporting meaningful social networks

Recent years have seen exponential growth of social network sites (SNSs) such as Friendster, MySpace and Facebook. SNSs flatten the real-world social network by making personal information and social structure visible to users outside the ego-centric networks. They provide a new basis of trust and credibility upon the Internet and Web infrastructure for users to communicate and share information. For the vast majority of social networks, it takes only a few clicks to befriend other members. People’s dynamic ever-changing real-world connections are translated to static links which, once formed, are permanent – thus entailing zero maintenance. The existence of static links as public exhibition of private connections causes the problem of friendship inflation, which refers to the online practice that users will usually acquire much more “friends” on SNSs than they can actually maintain in the real world. There is mounting evidence both in social science and statistical analysis to support the idea that there has been an inflated number of digital friendship connections on most SNSs. The theory of friendship inflation is also evidenced by our nearly 3-year observation on Facebook users in the University of Southampton. Friendship inflation can devalue the social graph and eventually lead to the decline of a social network site. From Sixdegrees.com to Facebook.com, there have been rise and fall of many social networks. We argue that friendship inflation is one of the main forces driving this move. Despite the gravity of the issue, there is surprisingly little academic research carried out to address the problems. The thesis proposes a novel algorithm, called ActiveLink, to identify meaningful online social connections. The innovation of the algorithm lies in the combination of preferential attachment and assortativity. The algorithm can identify long-range connections which may not be captured by simple reciprocity algorithms. We have tested the key ideas of the algorithms on the data set of 22,553 Facebook users in the network of University of Southampton. To better support the development of SNSs, we discuss an SNS model called RealSpace, a social network architecture based on active links. The system introduces three other algorithms: social connectivity, proximity index and community structure detection. Finally, we look at the problems relating to improving the network model and social network systems

Evolution of Ego-networks in Social Media with Link Recommendations

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl

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