A recommendation method for online dating networks based on social relations and demographic information

A new relationship type of social networks - online dating - are gaining popularity. With a large member base, users of a dating network are overloaded with choices about their ideal partners. Recommendation methods can be utilized to overcome this problem. However, traditional recommendation methods do not work effectively for online dating networks where the dataset is sparse and large, and a two-way matching is required. This paper applies social networking concepts to solve the problem of developing a recommendation method for online dating networks. We propose a method by using clustering, SimRank and adapted SimRank algorithms to recommend matching candidates. Empirical results show that the proposed method can achieve nearly double the performance of the traditional collaborative filtering and common neighbor methods of recommendation.

Leveraging the network information for evaluating answer quality in a collaborative question answering portal

Collaborative question answering (cQA) portals such as Yahoo! Answers allow users as askers or answer authors to communicate, and exchange information through the asking and answering of questions in the network. In their current set-up, answers to a question are arranged in chronological order. For effective information retrieval, it will be advantageous to have the users’ answers ranked according to their quality. This paper proposes a novel approach of evaluating and ranking the users’answers and recommending the top-n quality answers to information seekers. The proposed approach is based on a user-reputation method which assigns a score to an answer reflecting its answer author’s reputation level in the network. The proposed approach is evaluated on a dataset collected from a live cQA, namely, Yahoo! Answers. To compare the results obtained by the non-content-based user-reputation method, experiments were also conducted with several content-based methods that assign a score to an answer reflecting its content quality. Various combinations of non-content and content-based scores were also used in comparing results. Empirical analysis shows that the proposed method is able to rank the users’ answers and recommend the top-n answers with good accuracy. Results of the proposed method outperform the content-based methods, various combinations, and the results obtained by the popular link analysis method, HITS.

A social matching system : using implicit and explicit information for personalized recommendation in online dating service

Online dating websites enable a specific form of social networking and their efficiency can be increased by supporting proactive recommendations based on participants' preferences with the use of data mining. This research develops two-way recommendation methods for people-to-people recommendation for large online social networks such as online dating networks. This research discovers the characteristics of the online dating networks and utilises these characteristics in developing efficient people-to-people recommendation methods. Methods developed support improved recommendation accuracy, can handle data sparsity that often comes with large data sets and are scalable for handling online networks with a large number of users.

Identifying the Events That Connect Social Media Users : Charting Follower Accession on Twitter

Twitter is the focus of much research attention, both in traditional academic circles and in commercial market and media research, as analytics give increasing insight into the performance of the platform in areas as diverse as political communication, crisis management, television audiencing and other industries. While methods for tracking Twitter keywords and hashtags have developed apace and are well documented, the make-up of the Twitter user base and its evolution over time have been less understood to date. Recent research efforts have taken advantage of functionality provided by Twitter's Application Programming Interface to develop methodologies to extract information that allows us to understand the growth of Twitter, its geographic spread and the processes by which particular Twitter users have attracted followers. From politicians to sporting teams, and from YouTube personalities to reality television stars, this technique enables us to gain an understanding of what prompts users to follow others on Twitter. This article outlines how we came upon this approach, describes the method we adopted to produce accession graphs and discusses their use in Twitter research. It also addresses the wider ethical implications of social network analytics, particularly in the context of a detailed study of the Twitter user base.

Everyone’s watching it : the role of hype in television engagement through social media

Social media is playing an ever-increasing role in both viewers engagement with television and in the television industries evaluation of programming, in Australia – which is the focus of our study - and beyond. Twitter hashtags and viewer comments are increasingly incorporated into broadcasts, while Facebook fan pages provide a means of marketing upcoming shows and television personalities directly into the social media feed of millions of users. Additionally, bespoke applications such as FanGo and ZeeBox, which interact with the mainstream social networks, are increasingly being utilized by broadcasters for interactive elements of programming (c.f. Harrington, Highfield and Bruns, 2012). However, both the academic and industry study of these platforms has focused on the measure of content during the specific broadcast of the show, or a period surrounding it (e.g. 3 hours before until 3 am the next day, in the case of 2013 Nielsen SocialGuide reports). In this paper, we argue that this focus ignores a significant period for both television producers and advertisers; the lead-up to the program. If, as we argue elsewhere (Bruns, Woodford, Highfield & Prowd, forthcoming), users are persuaded to engage with content both by advertising of the Twitter hash-tag or Facebook page and by observing their network connections engaging with such content, the period before and between shows may have a significant impact on a viewers likelihood to watch a show. The significance of this period for broadcasters is clearly highlighted by the efforts they afford to advertising forthcoming shows through several channels, including television and social media, but also more widely. Biltereyst (2004, p.123) has argued that reality television generates controversy to receive media attention, and our previous small-scale work on reality shows during 2013 and 2014 supports the theory that promoting controversial behavior is likely to lead to increased viewing (Woodford & Prowd, 2014a). It remains unclear, however, to what extent this applies to other television genres. Similarly, while networks use of social media has been increasing, best practices remain unclear. Thus, by applying our telemetrics, that is social media metrics for television based on sabermetric approaches (Woodford, Prowd & Bruns, forthcoming; c.f. Woodford & Prowd, 2014b), to the period between shows, we are able to better understand the period when key viewing decisions may be made, to establish the significance of observing discussions within your network during the period between shows, and identify best practice examples of promoting a show using social media.

Identifying influential users of micro-blogging services : a dynamic action-based network approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our actionbaseduser influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered.

CSFinder: A Cold-Start Friend Finder in Large-Scale Social Networks

Recommending users for a new social network user to follow is a topic of interest at present. The existing approaches rely on using various types of information about the new user to determine recommended users who have similar interests to the new user. However, this presents a problem when a new user joins a social network, who is yet to have any interaction on the social network. In this paper we present a particular type of conversational recommendation approach, critiquing-based recommendation, to solve the cold start problem. We present a critiquing-based recommendation system, called CSFinder, to recommend users for a new user to follow. A traditional critiquing-based recommendation system allows a user to critique a feature of a recommended item at a time and gradually leads the user to the target recommendation. However this may require a lengthy recommendation session. CSFinder aims to reduce the session length by taking a case-based reasoning approach. It selects relevant recommendation sessions of past users that match the recommendation session of the current user to shortcut the current recommendation session. It selects relevant recommendation sessions from a case base that contains the successful recommendation sessions of past users. A past recommendation session can be selected if it contains recommended items and critiques that sufficiently overlap with the ones in the current session. Our experimental results show that CSFinder has significantly shorter sessions than the ones of an Incremental Critiquing system, which is a baseline critiquing-based recommendation system.

Small World Folksonomies: Clustering in Tri-Partite Hypergraphs

Many recent Web 2.0 resource sharing applications can be subsumed under the "folksonomy" moniker. Regardless of the type of resource shared, all of these share a common structure describing the assignment of tags to resources by users. In this report, we generalize the notions of clustering and characteristic path length which play a major role in the current research on networks, where they are used to describe the small-world effects on many observable network datasets. To that end, we show that the notion of clustering has two facets which are not equivalent in the generalized setting. The new measures are evaluated on two large-scale folksonomy datasets from resource sharing systems on the web.

Rationale and knowledge for the universal implementation of sanitation in areas of social vulnerability

The adoption of principles of equality and universality stipulated in legislation for the sanitation sector requires discussions on innovation. The existing model was able to meet sanitary demands, but was unable to attend all areas causing disparities in vulnerable areas. The universal implementation of sanitation requires identification of the know-how that promotes it and analysis of the model adopted today to establish a new method. Analysis of how different viewpoints on the restructuring process is necessary for the definition of public policy, especially in health, and understanding its complexities and importance in confirming social practices and organizational designs. These are discussed to contribute to universal implementation of sanitation in urban areas by means of a review of the literature and practices in the industry. By way of conclusion, it is considered that accepting a particular concept or idea in sanitation means choosing some effective interventions in the network and on the lives of individual users, and implies a redefinition of the space in which it exercises control and management of sewerage networks, such that connected users are perceived as groups with different interests.

Policy Contexts of Social Work in Britain: the wider implications of 'New' Labour and the 'New Legal Regime'

Several commentators have expressed disappointment with New Labour's apparent adherence to the policy frameworks of the previous Conservative administrations. The employment orientation of its welfare programmes, the contradictory nature of the social exclusion initiatives, and the continuing obsession with public sector marketisation, inspections, audits, standards and so on, have all come under critical scrutiny (c.f., Blyth 2001; Jordan 2001; Orme 2001). This paper suggests that in order to understand the socio-economic and political contexts affecting social work we need to examine the relationship between New Labour's modernisation project and its insertion within an architecture of global governance. In particular, membership of the European Union (EU), International Monetary Fund (IMF) and World Trade Organisation (WTO) set the parameters for domestic policy in important ways. Whilst much has been written about the economic dimensions of 'globalisation' in relation to social work rather less has been noted about the ways in which domestic policy agenda are driven by multilateral governance objectives. This policy dimension is important in trying to respond to various changes affecting social work as a professional activity. What is possible, what is encouraged, how things might be done, is tightly bounded by the policy frameworks governing practice and affected by those governing the lives of service users. It is unhelpful to see policy formulation in purely national terms as the UK is inserted into a network governance structure, a regulatory framework where decisions are made by many countries and organisations and agencies. Together, they are producing a 'new legal regime', characterised by a marked neo-liberal policy agenda. This paper aims to demonstrate the relationship of New Labour's modernisation programme to these new forms of legality by examining two main policy areas and the welfare implications they are enmeshed in. The first is privatisation, and the second is social policy in the European Union. Examining these areas allows a demonstration of how much of the New Labour programme can be understood as a local implementation of a transnational strategy, how parts of that strategy produce much of the social exclusion it purports to address, and how social welfare, and particularly social work, are noticeable by their absence within policy discourses of the strategy. The paper details how the privatisation programme is considered to be a crucial vehicle for the further development of a transnational political-economy, where capital accumulation has been redefined as 'welfare'. In this development, frameworks, codes and standards are central, and the final section of the paper examines how the modernisation strategy of the European Union depends upon social policy marked by an employment orientation and risk rationality, aimed at reconfiguring citizen identities.The strategy is governed through an 'open mode of coordination', in which codes, standards, benchmarks and so on play an important role. The paper considers the modernisation strategy and new legality within which it is embedded as dependent upon social policy as a technology of liberal governance, one demonstrating a new rationality in comparison to that governing post-Second World War welfare, and which aims to reconfigure institutional infrastructure and citizen identity.

Investigation of the Network Construction Behaviour on Social Networking Sites

By enabling connections between individuals, Social Networking Sites, such as Facebook, promise to create significant individual as well as social value. Encouraging connections between users is also crucial for service providers who increasingly rely on social advertising and viral marketing campaigns as important sources of their revenue. Consequently, understanding user’s network construction behavior becomes critical. However, previous studies offer only few scattered insights into this research question. In order to fill this gap, we employ Grounded Theory methodology to derive a comprehensive model of network construction behavior on social networking sites. In the following step we assess two Structural Equation Models to gain refined insights into the motivation to send and accept friendship requests – two network expansion strategies. Based on our findings, we offer recommendations for social network providers.

Leveraging Trust and Privacy Concerns in Online Social Networks: an Empirical Study

Unprecedented success of Online Social Networks, such as Facebook, has been recently overshadowed by the privacy risks they imply. Weary of privacy concerns and unable to construct their identity in the desired way, users may restrict or even terminate their platform activities. Even though this means a considerable business risk for these platforms, so far there have been no studies on how to enable social network providers to address these problems. This study fills this gap by adopting a fairness perspective to analyze related measures at the disposal of the provider. In a Structural Equation Model with 237 subjects we find that ensuring interactional and procedural justice are two important strategies to support user participation on the platform.

From the initial interest in ‘Why do people often communicate in very "strange" ways on Social Media?’ to a research project on CMC

This presentation is about the inside story of the PhD project El malagueño real, mental y virtual. Configuración de los significados sociales de una variedad urbana in Hispanic Linguistics. That is, the production and perception of the Spanish spoken in the city of Malaga and used on the social network sites Facebook and Tuenti by users from Malaga is analysed. Actually, the southern Spanish variety in question is quite distinct from the national standard in terms of its phonetic features, its prestige, and the attitudes to it. Thus, the project started with the initial interest in «Why do people often communicate in very “strange” ways on social media» which then slightly changed to the final research interest in «What do the different non-standard variants mean in virtual (and real) malagueño?». This long – sometimes hazardous, yet mostly fun – process is exposed in more detail by looking at the research questions, the methods and results. Lastly, the presentation concludes with some lessons learnt and an outlook on possibilities and necessities for further investigation.

Digital anthropology and educational eGames: learning through behavioural patterns in digital, game-based contexts

The selected publications are focused on the relations between users, eGames and the educational context, and how they interact together, so that both learning and user performance are improved through feedback provision. A key part of this analysis is the identification of behavioural, anthropological patterns, so that users can be clustered based on their actions, and the steps taken in the system (e.g. social network, online community, or virtual campus). In doing so, we can analyse large data sets of information made by a broad user sample,which will provide more accurate statistical reports and readings. Furthermore, this research is focused on how users can be clustered based on individual and group behaviour, so that a personalized support through feedback is provided, and the personal learning process is improved as well as the group interaction. We take inputs from every person and from the group they belong to, cluster the contributions, find behavioural patterns and provide personalized feedback to the individual and the group, based on personal and group findings. And we do all this in the context of educational games integrated in learning communities and learning management systems. To carry out this research we design a set of research questions along the 10-year published work presented in this thesis. We ask if the users can be clustered together based on the inputs provided by them and their groups; if and how these data are useful to improve the learner performance and the group interaction; if and how feedback becomes a useful tool for such pedagogical goal; if and how eGames become a powerful context to deploy the pedagogical methodology and the various research methods and activities that make use of that feedback to encourage learning and interaction; if and how a game design and a learning design must be defined and implemented to achieve these objectives, and to facilitate the productive authoring and integration of eGames in pedagogical contexts and frameworks. We conclude that educational games are a resourceful tool to provide a user experience towards a better personalized learning performance and an enhance group interaction along the way. To do so, eGames, while integrated in an educational context, must follow a specific set of user and technical requirements, so that the playful context supports the pedagogical model underneath. We also conclude that, while playing, users can be clustered based on their personal behaviour and interaction with others, thanks to the pattern identification. Based on this information, a set of recommendations are provided Digital Anthropology and educational eGames 6 /216 to the user and the group in the form of personalized feedback, timely managed for an optimum impact on learning performance and group interaction level. In this research, Digital Anthropology is introduced as a concept at a late stage to provide a backbone across various academic fields including: Social Science, Cognitive Science, Behavioural Science, Educational games and, of course, Technology-enhance learning. Although just recently described as an evolution of traditional anthropology, this approach to digital behaviour and social structure facilitates the understanding amongst fields and a comprehensive view towards a combined approach. This research takes forward the already existing work and published research onusers and eGames for learning, and turns the focus onto the next step — the clustering of users based on their behaviour and offering proper, personalized feedback to the user based on that clustering, rather than just on isolated inputs from every user. Indeed, this pattern recognition in the described context of eGames in educational contexts, and towards the presented aim of personalized counselling to the user and the group through feedback, is something that has not been accomplished before.

Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.