User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Rethinking privacy in social networks: A case study of beacon

Popular online social network sites (SNS) such as Facebook and Bebo are technological platforms that are posing questions about personal privacy. This paper contributes to our understanding of the nature and form of online privacy by critically analysing the issues surrounding the failed launch of Facebook’s advertising tool Beacon. Beacon is an interesting case study because it highlighted the complexity of information ownership in an online social network. Qualitative data was gathered from 29 weblogs (blogs) representing user opinions published between 6th November 2007(when Beacon was launched) and 28th February 2008 (when commentary had dwindled). A thematic analysis of the blogs suggest that concerns such as commercialism, terms of service (TOS), lack of user control, lack of user awareness and data protection are factors that influence user perceptions of information ownership as a subset of online privacy

Towards an ontology of networked learning

Networked learning, conceived of as networks of people, informational resources and technologies, constitutes what has been termed a ‘highly interwined’ technology. In this paper we develop our earlier argument that sociotechnical networks can form the basis for a non-determinist theory of learning technology. Firstly, we argue that Kling et al’s sociotechnical interaction network (STIN) is compatible with a realist ontology, drawing on Fleetwood’s ‘ontology of the real’ and Lawson’s proposition of the social nature of the artefact in networks of ‘positioned practices’. This, we suggest, gives a more secure basis for the STIN concept, and provides a clear alternative to actor network theory (ANT)-based views of sociotechnical networks which do not distinguish between the influence of human and material agents. This also, we argue, provides an alternative way of anchoring concepts from the social informatics literature, often influenced by Giddens’ structuration theory, in ways that can help networked learning research. Secondly, we explore some potential implications of such an approach for theories of networked learning and learning more widely. In particular, we suggest a possible ontology of elements of learning technology. The use of the word ‘learning’ here is somewhat problematic, as it is routinely used rather loosely to describe changes at multiple levels but which are likely to have rather different underlying mechanisms. A more thorough ontology of learning technology would allow us to distinguish between these uses and identify potentially distinct mechanisms at play in different forms and levels of learning. Thirdly, we use this approach to explore how viewing learning technologies as sociotechnical networks helps to clarify our thinking about identities in social networking for personal, learning and professional purposes

Cultural consequences of computing technology

Computing technology is clearly a technical revolution, but will most probably bring about a cultural revolution\ud as well. The effects of this technology on human culture will be dramatic and far-reaching. Yet, computers and\ud electronic networks are but the latest development in a long history of cognitive tools, such as writing and printing.\ud We will examine this history, which exhibits long-term trends toward an increasing democratization of culture,\ud before turning to today's technology. Within this framework, we will analyze the probable effects of computing on\ud culture: dynamical representations, generalized networking, constant modification and reproduction. To address the\ud problems posed by this new technical environment, we will suggest possible remedies. In particular, the role of\ud social institutions will be discussed, and we will outline the shape of new electronic institutions able to deal with the\ud information flow on the internet

Reliable online social network data collection

Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users’ behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users’ behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.Postprin

Issues in the study of virtual world social movements

Virtual worlds are online three-dimensional worlds that are often constructed to look much like the real world. As more people begin to use these virtual worlds, virtual communities are emerging enabling various social activities and social interactions to be conducted online. Based on a literature review of social movements, virtual communities and virtual worlds, this paper suggests a framework to guide IS research into this new and exciting area