OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 13109 Abstract-In a social context, the user is more and more an active contributor for producing social information. Then, he needs a tailored information reflecting his current needs and interests in every period of time. This aims to provide a better adaptation while accessing the information space by integrating users' interests dynamic. Indeed, users' interests may change and become "outdated" through time. So, an interest judged as relevant in a period of time may fluctuate in the next period of time. Moreover, analysing the classic user behaviour to deduce his current interests is a difficult task. In fact, his behaviour isn't always reflecting his real interests. In this paper, we propose a new approach for enriching the user profile in an evolutionary environment such as a social network. The enrichment takes into account: i) the social behaviour and more precisely the tagging behaviour (that reflects user's interests) and ii) the temporal information (that reflects the dynamic evolution of users' interests). Our approach focus on the concept of temperature that reflects the importance of a resource in each period of time. This concept is used to infer common interests of users tagging the same "important" resource. The originality of our approach relies on combining information tags, users and resources in a way that guarantees a better enrichment for the social user profile. Our approach has been tested and evaluated with the Delicious social database and shows interesting precision values.
Spam Social spammersTwitter stream a b s t r a c t Twitter is one of the most popular social platforms. It has changed the way of communication and in-formation dissemination through its real-time messaging mechanism. Recently, it has been used by re-searchers and industries as a new source of data for various intelligent systems, such as tweet sentiment analysis and recommendation systems, which require high data quality. However, due to its flexibility and popularity, Twitter has become the main target for spamming activities such as phishing legitimate users or spreading malicious software, which introduces new security issues and waste resources. There-fore, researchers have developed various machine-learning algorithms to reveal Twitter spam. However, as spammers have become smarter and more crafty, the characteristics of the spam tweets are varying over time making these methods inefficient to detect new spammers tricks and strategies. In addition, some of the employed methods (e.g. blacklisting) or spammer features (e.g. graph-based features) are extremely time-consuming, which hinders the ability to detect spammer activities in real-time. In this paper, we introduce a framework to deal with the volatility of the spam contents and new spamming patterns, called the spam drift. The framework combines the strength of unsupervised machine learning approach, which learns from unlabeled tweets, to retrain a real-time supervised tweet-level spam detec-tion model in a batch mode. A set of experiments on a largescale data set show the effectiveness of the proposed online unsupervised method in adaptively discovers and learns the patterns of new spam activities and achieve stable recall values reaching more than 95%. Although the average spam precision of our method is around 60%, the high spam recall values show the ability of our proposed method in reducing spam drift problems compared to traditional machine learning algorithms.
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