'Try hard' : Attitudes to advertising in online social networks

Advertising has recently entered many new spaces it does not fully understand. The rules that apply in traditional media do not always translate in new media environments. However, their low cost of entry and the availability of hard-to-reach target markets, such as Generation Y, make environments such as online social networking sites attractive to marketers. This paper accumulates teenage perspectives from two qualitative studies to identify attitudes towards advertising in online social network sites and develop implications for marketers seeking to advertising on social network sites.

User-Created Content and Online Social Networks

Traditional media are under assault from digital technologies. Online advertising is eroding the financial basis of newspapers and television, demarcations between different forms of media are fading, and audiences are fragmenting. We can podcast our favourite radio show, data accompanies television programs, and we catch up with newspaper stories on our laptops. Yet mainstream media remain enormously powerful. The Media and Communications in Australia offers a systematic introduction to this dynamic field. Fully updated and revised to take account of recent developments, this third edition outlines the key media industries and explains how communications technologies are impacting on them. It provides a thorough overview of the main approaches taken in studying the media, and includes new chapters on social media, gaming, telecommunications, sport and cultural diversity. With contributions from some of Australia's best researchers and teachers in the field, The Media and Communications in Australia is the most comprehensive and reliable introduction to media and communications available. It is an ideal student text, and a reference for teachers of media and anyone interested in this influential industry.

New Media and Public Communication: Mapping Australian User-Created Content in Online Social Networks

Presentation describling a project in data intensive research in the humanities. Measuring activity of publically available data in social networks such as Blogosphere, Twitter, Flickr, YouTube

A comparative study of marketing student engagement in online social networks and virtual worlds : Facebook versus second life

The study evaluated two student online contemporary learning environments; Second Life and Facebook, student learning experiences and student knowledge outcomes. A case study methodology was used to gain rich exploratory knowledge of student learning when integrating online social networks (OSN) and virtual worlds (VW) platforms. Findings indicated students must perceive relevance in the activities when using such platforms, even though online environments create an interesting learning space for students and educators, the novelty can diminish quickly and these online environments dilute traditional authority boundaries.

Online social networks as an emerging opportunity for entrepreneurship : a study of the music industry

The explosion in use of online social networks is an important phenomenon that provides a new set of entrepreneurial opportunities. Emerging musicians have been among the first to exploit this new market opportunity – and indeed, many have used it successfully. A recent study Carter (2009) reveals that artists who earned the most returns had an online presence on multiple social online sites and services such as MySpace and Facebook. These web pages are leveraged to build fan bases and develop different types of revenue streams. Yet, little is currently known about discovery or exploitation of such opportunities.

A survey on the relationship between trust and interest similarity in online social networks

A remarkable growth in quantity and popularity of online social networks has been observed in recent years. There is a good number of online social networks exists which have over 100 million registered users. Many of these popular social networks offer automated recommendations to their users. This automated recommendations are normally generated using collaborative filtering systems based on the past ratings or opinions of the similar users. Alternatively, trust among the users in the network also can be used to find the neighbors while making recommendations. To obtain the optimum result, there must be a positive correlation exists between trust and interest similarity. Though the positive relations between trust and interest similarity are assumed and adopted by many researchers; no survey work on real life people’s opinion to support this hypothesis is found. In this paper, we have reviewed the state-of-the-art research work on trust in online social networks and have presented the result of the survey on the relationship between trust and interest similarity. Our result supports the assumed hypothesis of positive relationship between the trust and interest similarity of the users.

Managing Trust in Online Social Networks

In recent years, there is a dramatic growth in number and popularity of online social networks. There are many networks available with more than 100 million registered users such as Facebook, MySpace, QZone, Windows Live Spaces etc. People may connect, discover and share by using these online social networks. The exponential growth of online communities in the area of social networks attracts the attention of the researchers about the importance of managing trust in online environment. Users of the online social networks may share their experiences and opinions within the networks about an item which may be a product or service. The user faces the problem of evaluating trust in a service or service provider before making a choice. Recommendations may be received through a chain of friends network, so the problem for the user is to be able to evaluate various types of trust opinions and recommendations. This opinion or recommendation has a great influence to choose to use or enjoy the item by the other user of the community. Collaborative filtering system is the most popular method in recommender system. The task in collaborative filtering is to predict the utility of items to a particular user based on a database of user rates from a sample or population of other users. Because of the different taste of different people, they rate differently according to their subjective taste. If two people rate a set of items similarly, they share similar tastes. In the recommender system, this information is used to recommend items that one participant likes, to other persons in the same cluster. But the collaborative filtering system performs poor when there is insufficient previous common rating available between users; commonly known as cost start problem. To overcome the cold start problem and with the dramatic growth of online social networks, trust based approach to recommendation has emerged. This approach assumes a trust network among users and makes recommendations based on the ratings of the users that are directly or indirectly trusted by the target user.

ACE research vignette 003 : different strokes for different folks - classification of entrepreneurial musicians and their use of online social networks

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international entrepreneurship researchers. In this vignette Dr Maria Kaya and Associate Professor Paul Steffens consider both the classification of musicians and their use of online social networks.

Analyzing the effectiveness of graph metrics for anomaly detection in online social networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users’ graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches.

A semi-supervised graph-based algorithm for detecting outliers in online-social-networks

In this paper, we propose a semi-supervised approach of anomaly detection in Online Social Networks. The social network is modeled as a graph and its features are extracted to detect anomaly. A clustering algorithm is then used to group users based on these features and fuzzy logic is applied to assign degree of anomalous behavior to the users of these clusters. Empirical analysis shows effectiveness of this method.

Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach

Online Social Networks (OSNs) facilitate to create and spread information easily and rapidly, influencing others to participate and propagandize. This work proposes a novel method of profiling Influential Blogger (IB) based on the activities performed on one's blog documents who influences various other bloggers in Social Blog Network (SBN). After constructing a social blogging site, a SBN is analyzed with appropriate parameters to get the Influential Blog Power (IBP) of each blogger in the network and demonstrate that profiling IB is adequate and accurate. The proposed Profiling Influential Blogger (PIB) Algorithm survival rate of IB is high and stable. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Inferring Big 5 Personality from Online Social Networks

Thesis (Master's)--University of Washington, 2014