{"id":159875,"date":"2010-01-04T00:00:00","date_gmt":"2010-01-04T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/detecting-spam-in-a-twitter-network\/"},"modified":"2018-10-16T21:37:26","modified_gmt":"2018-10-17T04:37:26","slug":"detecting-spam-in-a-twitter-network","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/detecting-spam-in-a-twitter-network\/","title":{"rendered":"Detecting spam in a Twitter network"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Spam becomes a problem as soon as an online communication medium becomes popular. Twitter\u2019s behavioral and structural properties make it a fertile breeding ground for spammers to proliferate. In this article we examine spam around a one-time Twitter meme\u2014\u201crobotpickuplines\u201d. We show the existence of structural network differences between spam accounts and legitimate users. We conclude by highlighting challenges in disambiguating spammers from legitimate users.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Spam becomes a problem as soon as an online communication medium becomes popular. Twitter\u2019s behavioral and structural properties make it a fertile breeding ground for spammers to proliferate. In this article we examine spam around a one-time Twitter meme\u2014\u201crobotpickuplines\u201d. We show the existence of structural network differences between spam accounts and legitimate users. 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