International Journal of Social Network Mining (3 papers in press)
Profiling Online Social Networks Users: An Omniopticon Tool
by Miltiadis Kandias, Lilian Mitrou, Vasilios Stavrou, Dimitris Gritzalis
Abstract: Online Social Networks and Media indicate and incorporate the shift to interpersonal, horizontal and mutual communication and, thus information aggregation. Online content of interest (namely, YouTube videos and comments or Tweets), along with online relations (friendships, followings, mentions, comments, etc.), may be collected and utilized for a variety of purposes. In our previous research we have demonstrated that it is possible and potentially trivial (by utilizing a simple personal computer and a broadband internet connection) to extract personal sensitive information such as political beliefs and psychosocial characteristics (such as narcissism and predisposition towards law enforcement) about Online Social Networks (OSN) users in an automated manner. Web 2.0 technological features combined with voluntary exposure to an indefinite audience in OSNs give rise to traditional surveillance as Government is enabled to connect the dots, combine information about political beliefs and every-day activities and generate mass user profiles on the base of identifying patterns. Despite the lack of centralized control over the Internet, its platforms and applications allow multilevel and latent surveillance, thus posing new risks for individuals by forming new power relations and asymmetries. Our research highlights how Web 2.0 and OSNs (YouTube and Twitter) may become a topos of participatory panopticism, an omniopticon in which the many watch the many and can reconstruct sen
Keywords: Social Media; Profiling; YouTube; Twitter; Panopticon; Omniopticon; Ethics; Surveillance.
Method of Visualizing Relations between Tweets to Facilitate Discussions via Twitter
by Yasuhiro Yamada, Hiroshi Suzuki, Akira Hattori, Haruo Hayami
Abstract: It is common for Twitter users to adopt clients that display posts
(tweets) as textboxes lined up in an perpendicular fashion. In a discussion with
multiple participants, tweets are commonly used as replies. However, it is
difficult for users of these types of displays to follow the context and make further
contributions to the conversations. In this study, we will also be proposing a
system which visualizes relations of tweets by usage of digraphs, along with an
evaluation of the proposed system on how it fares with existing solutions.
Keywords: Twitter; Visualization; Real-time systems; User interfaces; Receivers; Servers; Browsers; Digraph; Microblogging.
Multiple Metric Aware YouTube Tutorial videos Virality Analysis
by Niyati Aggrawal, Anuja Arora, Ponnurangam Kumaraguru
Abstract: A limitless quantity of content posts on the Social Networking sites every microsecond and gets viral. The reasons behind virality of content can be numerous such as users reactions (like, dislike, comment, etc.) towards content interestingness and many times some influential entities also affect to make content viral. This research paper focuses on the virality of content corresponding to users reaction and proposes a Multiple Metric Aware Virality Model (M2VM) which is beneficial to identify virality of content according to content characteristics and their resultant user reactions. The proposed M2VM assigns virality coefficient to each user reaction based on content properties/ characteristics and predict a virality score of target content corresponding to measured user reactions virality coefficients. To validate our proposed model we have chosen five tutorial based theme videos of YouTube as case study. Each topic related videos are available in utmost quantity on YouTube and users choose video according to their preference and past users reactions. Thus in this research work, efforts have been made to analyze and understand users selection intention towards tutorial videos on the basis of various video metrics. For this purpose considered video characteristics (VC) are video length and video age; and video statistics (VS) are View count, like count, dislike count and comment count. The prime objective of this research work is to analyze video characteristics role in identifying users selection preference on the basis of video statistics and formulate various video metric corresponding to outcome to conclude the reaction of video characteristics over video virality. The research gives a practical exhibit of YouTube tutorial videos virality with respect to video characteristics mapping with video statistics. We determine the virality model of videos over video age and video length through datasets acquired by YouTube data APIs.
Keywords: YouTube; Virality model; Video statistics; video metric; Social Network Analysis; Virality.