With millions of users tweeting around the world, real time search systems and different types of mining tools are emerging to allow people tracking the repercussion of events and news on Twitter. Trending topics, the most talked about items on Twitter at a given point in time, have been seen as an opportunity to generate traffic and revenue. Spammers post tweets containing typical words of a trending topic and URLs, usually obfuscated by URL shortness, that lead users to completely unrelated websites. This kind of spam can contribute to de-value real time search services unless mechanisms to fight and stop spammers can be found. To solve this issue, we propose to take tweet text features along with user-based features. We have evaluated our approach with natural language processing and the naïve-Bayes machine learning algorithm.

Twitter, tweets, spam, navie bayes, natural lanugage processing

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