Social media and micro-blogging platforms have been successful for communication and information exchange enjoying vast number of user participation. Given their millions of users, it is natural that there is a lot of interest for marketing and advertising on these platforms as attested by the introduced advertising platforms on Twitter and Facebook. In this paper, inspired by micro-blogging advertising platforms, we introduce two problems to aid ad and marketing campaigns. The first problem identifies topics (called analogous topics) that have approximately the same audience in a micro-blogging platform as a given query topic. The main idea is that by bidding on an analogous topic instead of the original query topic, we reach approximately the same audience while spending less of our budget. Then, we present algorithms to identify expert users on a given query topic and categorize these experts to finely understand their diversified expertise. This is imperative for word of mouth marketing where individuals have to be targeted precisely. We evaluate our algorithms and solutions for both problems on a large dataset from Twitter attesting to their eciency and accuracy compared with alternate approaches.

Identifying the k most influential individuals in a social network is a well-studied problem. The objective is to detect k individuals in a (social) network who will influence the maximum number of people, if they are independently convinced of adopting a new strategy (product, idea, etc). There are cases in real life, however, where we aim to instigate groups instead of individuals to trigger network diffusion. Such cases abound, e.g., billboards, TV commercials and newspaper ads are utilized extensively to boost the popularity and raise awareness.

Data available through social media and content sharing platforms present opportunities for analysis and mining. In the context of social networks, it is interesting to formalize and locate bursts of activities amongst users, related to a particular event and to report sets of socially connected users participating in such bursts. Such collections present new opportunities for understanding social events, and render new ways of online marketing.

Online types of expression in the form of social networks, micro-blogging, blogs and rich content sharing platforms have proliferated in the last few years. Such proliferation contributed to the vast explosion in online data sharing we are experiencing today. One unique aspect of online data sharing is tags manually inserted by content generators to facilitate content description and discovery (e.g., hashtags in tweets). In this paper we focus on these tags and we study and propose algorithms that make use of tags in order to automatically organize and categorize this vast collection of socially contributed and tagged information. In particular, we take a holistic approach in organizing such tags and we propose algorithms to partition as well as rank this information collection. Our partitioning algorithms aim to segment the entire collection of tags (and the associated content) into a specified number of partitions for specific problem constraints. In contrast our ranking algorithms aim to identify few partitions fast, for suitably defined ranking functions. We present a detailed experimental study utilizing the full twitter firehose (set of all tweets in the Twitter service) that attests to the practical utility and effectiveness of our overall approach. We also present a detailed qualitative study of our results.

Among different traffic classification approaches, Deep Packet Inspection (DPI) methods are considered as the most accurate. These methods, however, have two drawbacks: (i) they are not efficient since they use complex regular expressions as protocol signatures, and (ii) they require manual intervention to generate and maintain signatures, partly due to the signature complexity. In this paper, we present CUTE, an automatic traffic classification method, which relies on sets of weighted terms as protocol signatures. The key idea behind CUTE is an observation that, given appropriate weights, the occurrence of a specific term is more important than the relative location of terms in a flow. This observation is based on experimental evaluations as well as theoretical analysis, and leads to several key advantages over previous classification techniques: (i) CUTE is extremely faster than other classification schemes since matching flows with weighed terms is significantly faster than matching regular expressions; (ii) CUTE can classify network traffic using only the first few bytes of the flows in most cases; and (iii) Unlike most existing classification techniques, CUTE can be used to classify partial (or even slightly modified) flows. Even though CUTE replaces complex regular expressions with a set of simple terms, using theoretical analysis and experimental evaluations (based on two large packet traces from tier-one ISPs), we show that its accuracy is as good as or better than existing complex classification schemes, i.e. CUTE achieves precision and recall rates of more than 90%. Additionally, CUTE can successfully classify more than half of flows that other DPI methods fail to classify.