Using Social Network Information into ICN

It was presented at NOMEN 2013 as a poster. NOMEN 2013 was a workshop held in IEEE INFOCOM 2013Online Social Networks carry extremely valuable information about their users and their relationships. We argue that this knowledge can help to drastically improve the efficiency of Information Centric Networks. In this paper, we propose a first step to include social infor- mation into ICN architectures. We conjecture a small number of users dominate the activity and receive most attention of others users in the social networks and we argue they produce content that will be more likely to be consumed, and in consequence their content must be replicated. We then propose a caching strategy based on prioritizing their content. We simulate a social network model where the proposed caching strategy is evaluated against common ICN caching strategies. Finally, we show that inclusion of social information into ICN networks may help to improve cache performances

SOCIAL MEDIA ANALYTICS − A UNIFYING DEFINITION, COMPREHENSIVE FRAMEWORK, AND ASSESSMENT OF ALGORITHMS FOR IDENTIFYING INFLUENCERS IN SOCIAL MEDIA

Given its relative infancy, there is a dearth of research on a comprehensive view of business social media analytics (SMA). This dissertation first examines current literature related to SMA and develops an integrated, unifying definition of business SMA, providing a nuanced starting point for future business SMA research. This dissertation identifies several benefits of business SMA, and elaborates on some of them, while presenting recent empirical evidence in support of foregoing observations. The dissertation also describes several challenges facing business SMA today, along with supporting evidence from the literature, some of which also offer mitigating solutions in particular contexts. The second part of this dissertation studies one SMA implication focusing on identifying social influencer. Growing social media usage, accompanied by explosive growth in SMA, has resulted in increasing interest in finding automated ways of discovering influencers in online social interactions. Beginning 2008, many variants of multiple basic approaches have been proposed. Yet, there is no comprehensive study investigating the relative efficacy of these methods in specific settings. This dissertation investigates and reports on the relative performance of multiple methods on Twitter datasets containing between them tens of thousands to hundreds of thousands of tweets. Accordingly, the second part of the dissertation helps further an understanding of business SMA and its many aspects, grounded in recent empirical work, and is a basis for further research and development. This dissertation provides a relatively comprehensive understanding of SMA and the implementation SMA in influencer identification