By Jiliang Tang, Xufei Wang, Huan Liu (auth.), Martin Atzmueller, Alvin Chin, Denis Helic, Andreas Hotho (eds.)
This e-book constitutes the joint completely refereed post-proceedings of the second one foreign Workshop on Modeling Social Media, MSM 2011, held in Boston, MA, united states, in October 2011, and the second one foreign Workshop on Mining Ubiquitous and Social Environments, MUSE 2011, held in Athens, Greece, in September 2011. The nine complete papers integrated within the e-book are revised and considerably prolonged models of papers submitted to the workshops. They conceal a variety of issues geared up in 3 major topics: groups and networks in ubiquitous social media; mining methods; and problems with person modeling, privateness and security.
Read or Download Modeling and Mining Ubiquitous Social Media: International Workshops MSM 2011, Boston, MA, USA, October 9, 2011, and MUSE 2011, Athens, Greece, September 5, 2011, Revised Selected Papers PDF
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Additional info for Modeling and Mining Ubiquitous Social Media: International Workshops MSM 2011, Boston, MA, USA, October 9, 2011, and MUSE 2011, Athens, Greece, September 5, 2011, Revised Selected Papers
Sample text
Anagnostopoulos et al. [2] explored the influence of Flickr’s social network on the content based tagging behavior of users. They used two different statistical techniques (shuffle test and edge-reversal test) to reveal potential influences. Their results show that few social influences exist on Flickr, which indicates that social networks in Flickr are less important than in Twitter. The approach of finding such influence patterns in social media has also been studied in Twitter. Suh et al. [19] studied factors which influence the retweetability of tweets.
E(m ¯ C) 2mnC (n − nC ) Thus, the segregation index explicitly includes the size n of the graph. By averaging the segregation over all clusters one obtains the segregation of a cluster model. 2 Available Data For capturing social interactions, RFID proximity tags of the Sociopatterns project were handed out to the participants of the conference LWA 2010. 70 out of 100 participants volunteered to wear an RFID tag which (approximately) detected mutual face-to-face sightings among participants with a minimum proximity of about one meter.
The considerable difference between the weighted and unweighted measures indicates the relevance of the frequency and the length of the contacts: Professors, for example, have longer and more contacts to other participants than PostDocs. Another aspect is illustrated by the betweenness (bet) scores: Relatively to the other groups, a lot of shortest paths of LWA[≥ 5]∗ run through nodes of PostDocs. We attribute this to the structure of scientific institutes, where usually one professor supervises several PostDocs who again each supervise several PhD-students.