Current and Future Trends in Social Media - Baatarjav, Enkh-Amgalan; Ram Dantu (2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing, 2011)
Social networking has been the biggest cultural shift since the industrial revolution, attracting millions of people, creating an ever-expanding social web, and revolutionizing the way we communicate. Although many changes have occurred since the fledgling steps of social networking and what it has evolved into today, improvement is a never-ending cycle. Thus, in this paper, additional methods will be explored to advance social networking overall.
Keywords: Buildings; Electronic mail; Facebook; Media; Online Social Networks; Privacy; Social Media; Social Network Trends; Twitter

Identification of leaders, lurkers, associates and spammers in a social network: context-dependent and context-independent approaches - Fazeen, Mohamed; Ram Dantu; Guturu, Parthasarathy (Social Network Analysis and Mining, 2011)
In this paper, we present two methods for classification of different social network actors (individuals or organizations) such as leaders (e.g., news groups), lurkers, spammers and close associates. The first method is a two-stage process with a fuzzy-set theoretic (FST) approach to evaluation of the strengths of network links (or equivalently, actor-actor relationships) followed by a simple linear classifier to separate the actor classes. Since this method uses a lot of contextual information including actor profiles, actor-actor tweet and reply frequencies, it may be termed as a context-dependent approach. To handle the situation of limited availability of actor data for learning network link strengths, we also present a second method that performs actor classification by matching their short-term (say, roughly 25 days) tweet patterns with the generic tweet patterns of the prototype actors of different classes. Since little contextual information is used here, this can be called a context-independent approach. Our experimentation with over 500 randomly sampled records from a twitter database consists of 441,234 actors, 2,045,804 links, 6,481,900 tweets, and 2,312,927 total reply messages indicates that, in the context-independent analysis, a multilayer perceptron outperforms on both on classification accuracy and a new F-measure for classification performance, the Bayes classifier and Random Forest classifiers. However, as expected, the context-dependent analysis using link strengths evaluated using the FST approach in conjunction with some actor information reveals strong clustering of actor data based on their types, and hence can be considered as a superior approach when data available for training the system is abundant.
Keywords: Context dependent and context independent data analysis; Fuzzy logic; MLP; Naive Bayesian classifier; Random Forest; Social networks; Twitter

Unveiling Hidden Patterns to Find Social Relevance - Baatarjav, Enkh-Amgalan; Ram Dantu (Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on, 2011)
Twitter is both a useful social networking device and an incredible marketing tool. However, it is also a venue for dangerous stalkers and a sub-world of internet users that most people would intuitively avoid if seeing them in real life. It would improve Twitter's safety to have filters available which would allow users to select an audience for their status updates without being forced into changing their profiles to a private setting. The hypothetical filter, or model, studied in this paper was based on two particular attributes: activity correlations and vocabulary similarities between users and followers. If implemented, this model would restrict the availability of status updates to an automatically generated group of socially relevant followers. The result of this study shows that both of the attributes can be used to define social relevance, however, it was found that activity patterns have better predictive capabilities than correlating vocabulary usage between users and followers.
Keywords: activity correlations; Correlation; dangerous stalkers; data privacy; Facebook; hidden patterns; Internet; Internet users; marketing tool; Online Social Networks; Polynomials; Privacy; social networking (online); social networking device; social relevance; Twitter; Twitter safety; Vocabulary; vocabulary similarities

Are You My Friend? - Baatarjav, Enkh-Amgalan; Amin, Aliasgar; Ram Dantu; Gupta, Nikhil Kant (Proceedings of the 7th IEEE Conference on Consumer Communications and Networking Conference, 2010)
With Twitters growing popularity, privacy has become a major concern for users wary of sensitive information falling in the wrong hands. A typical Twitter user carries hundreds of followers - people who have subscribed to users twitter feeds. Our goal is to target followers that a Twitter user deems safe. Therefore, selecting followers with a closer relationship could help decrease the risk of sensitive information being sent to unknown people. We propose a privacy management system that helps a Twitter user restrict information to only certain followers based on the strength of their social tie. This system would incorporate two tools: the Exclusivity meter and the Twitter Response Estimator. The Exclusivity Meter employs user’s activity profile to guess similarities between users. Preliminary results have indicated that similarity in time and level of activity between user and follower does suggest a stronger social tie. The Twitter Response Estimator uses a measure of prestige to gauge the probability of response. When applied to a set of followers, the estimator separates real friends from recreational followers
Keywords: online social network; Privacy; Twitter