Who will lead and who will follow: Identifying Influential Users in Online Social Networks - A Critical Review and Future Research Directions

Along with the explosive growth of the phenomenon Online Social Networks (OSN), identifying influential users in OSN has received a great deal of attention in recent years. However, the development of practical approaches for identifying them is still in its infancy. By means of a structured literature review, the authors analyze and synthesize the publications particularly from two perspectives. From a research perspective, they find that existing approaches mostly build on users’ connectivity and activity but hardly consider further characteristics of influential users. Moreover, they outline two major research streams. It becomes apparent that most marketing-oriented articles draw on real-world data of OSN, while more technology-oriented papers rather have a theoretical approach and mostly evaluate their artifacts by means of formal proofs. The authors find that a stronger collaboration between the scientific Business and Information Systems Engineering (BISE) and Marketing communities could be mutually beneficial. With respect to a practitioner’s perspective, they compile advice on the practical application of approaches for the identification of influential users. It is hoped that the results can stimulate and guide future research

Social Media in South India

One of the first ethnographic studies to explore use of social media in the everyday lives of people in Tamil Nadu, Social Media in South India provides an understanding of this subject in a region experiencing rapid transformation. The influx of IT companies over the past decade into what was once a space dominated by agriculture has resulted in a complex juxtaposition between an evolving knowledge economy and the traditions of rural life. While certain class tensions have emerged in response to this juxtaposition, a study of social media in the region suggests that similarities have also transpired, observed most clearly in the blurring of boundaries between work and life for both the old residents and the new

Influence Analysis towards Big Social Data

Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation

A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres

Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned

Social media use, online political discussion and UK political events 2013-2018: a phenomenographic study

A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Ph.D.Social media has had observably significant effects on the way many ordinary people participate in politics and appears both symptomatic and causal of a changing landscape. Research, often data-led, has shown marked trends in online behaviour, such as political polarisation, the tendency to form echo chambers and other distinct patterns in the way people debate, share opinions, express their self-identities, consume media and think critically, or otherwise, about political issues. A review of the literature shows that current research in this area across disciplines explores an increasingly wide range of potential influencing factors behind these phenomena, from the social to the psychological to the physiological. However, there have been – far - fewer phenomenological or phenomenographical studies into people’s lived experience of being part of this cultural shift, how their own inclinations, practices and behaviour might be helping to shape the bigger picture, and to what extent they understand this. Starting from an interdisciplinary theoretical framework, and based on in-depth conversations with 84 mostly UK-based adults spoken to one-to-one or in focus groups and webinars over an 18-month period, this study asked people’s about their own perceptions and understanding of their online engagement, focusing on recent major UK political events between 2013 and 2018, (including the Scottish Independence Referendum, The EU Referendum and the Labour Party leadership contests) and considers some of the inferences that might be drawn from people’s own insights. It shows:  People’s experiences are varied, influenced by a range of factors but there is a focus on personal needs and concerns as much as wider political ones  Participants often struggle with behavioural self-awareness and understanding of the motives and actions of others  They can have profound emotional responses owing to the difficulties of using social media but still value it as a medium for political learning and self-expression  A lot of activity takes places in covert, limited or private spaces  Social media itself is an unprecedented learning environment where people begin to understand their own behaviour better and adap

Multidimensional political polarization in online social networks

Political polarization in online social platforms is a rapidly growing phenomenon worldwide. Despite their relevance to modern-day politics, the structure and dynamics of polarized states in digital spaces are still poorly understood. We analyze the community structure of a two-layer, interconnected network of French Twitter users, where one layer contains members of Parliament and the other one regular users. We obtain an optimal representation of the network in a four-dimensional political opinion space by combining network embedding methods and political survey data. We find structurally cohesive groups sharing common political attitudes and relate them to the political party landscape in France. The distribution of opinions of professional politicians is narrower than that of regular users, indicating the presence of more extreme attitudes in the general population. We find that politically extreme communities interact less with other groups as compared to more centrist groups. We apply an empirically tested social influence model to the two-layer network to pinpoint interaction mechanisms that can describe the political polarization seen in data, particularly for centrist groups. Our results shed light on the social behaviors that drive digital platforms towards polarization, and uncover an informative multidimensional space to assess political attitudes online

Improving community health networks for people with severe mental illness : a case study investigation

Introduction Policy drivers in mental health to address personal recovery, stigma and poor physical health indicate that new service solutions are required. This study aimed to understand how connections to people, places and activities were utilised by individuals with severe mental illness (SMI) to benefit health and wellbeing. Methods A five-module mixed-methods design was undertaken in two study sites. Data were collected from 150 network-mapping interviews and 41 in-depth follow-up interviews with people with SMI; in-depth interviews with 30 organisation stakeholders and 12 organisation leaders; and 44 telephone interviews with practitioners. We undertook a three-stage synthesis process including independent lived experience feedback, and a patient and public involvement team participated in tool design, data collection, analysis and write-up. Results Three personal network types were found in our study using the community health network approach: diverse and active; family and stable; formal and sparse. Controlled for other factors we found only four variables significantly associated with which network type a participant had: living alone or not; housing status; formal education; long-term sickness or disability. Diagnosis was not a factor. These variables are challenging to address but they do point to potential for network change. The qualitative interviews with people with SMI provided further understanding of connection-building and resource utilisation. We explored individual agency across each network type, and identified recognition of the importance and value of social support and active connection management alongside the risks of isolation, even for those most affected by mental illness. We identified tensions in personal networks, be that relationships with practitioners or families, dealing with the impact of stigma, or frustrations of not being in employment, which all impact on network resources and well-being. The value of connectedness within personal networks of people, place and activity for supporting recovery was evident in shaping identity, providing meaning to life and sense of belonging, gaining access to new resources, structuring routines and helping individuals ‘move on’ in their recovery journey. Health-care practitioners recognised that social factors were important in recovery but reported system-level barriers (workload, administrative bureaucracy, limited contact time with clients) in addressing these issues fully. Even practitioners working in third-sector services whose remit involved increasing clients’ social connection faced restrictions due to being evaluated by outcome criteria that limited holistic recovery-focused practices. Service providers were keen to promote recovery-focused approaches. We found contrasts between recovery ideology within mental health policy and recovery practice on the ground. In particular, the social aspects of supporting people with SMI are often underprioritised in the health-care system. In a demanding and changing context, strategic multiagency working was seen as crucial but we found few examples of embedded multisector organisation partnerships. Conclusion While our exploratory study has limitations, findings suggest potential for people with SMI to be supported to become more active managers of their personal networks to support well-being regardless of current network type. The health and social care system does not currently deliver multiagency integrated solutions to support SMI and social recovery

Social Media in South India

One of the first ethnographic studies to explore use of social media in the everyday lives of people in Tamil Nadu, Social Media in South India provides an understanding of this subject in a region experiencing rapid transformation. The influx of IT companies over the past decade into what was once a space dominated by agriculture has resulted in a complex juxtaposition between an evolving knowledge economy and the traditions of rural life. While certain class tensions have emerged in response to this juxtaposition, a study of social media in the region suggests that similarities have also transpired, observed most clearly in the blurring of boundaries between work and life for both the old residents and the new. Venkatraman explores the impact of social media at home, work and school, and analyses the influence of class, caste, age and gender on how, and which, social media platforms are used in different contexts. These factors, he argues, have a significant effect on social media use, suggesting that social media in South India, while seeming to induce societal change, actually remains bound by local traditions and practices