NDLI: Detecting collective attention spam

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Detecting collective attention spam

Content Provider	ACM Digital Library
Author	Lee, Kyumin Cheng, Zhiyuan Caverlee, James Kamath, Krishna Y.
Abstract	We examine the problem of collective attention spam, in which spammers target social media where user attention quickly coalesces and then collectively focuses around a phenomenon. Compared to many existing spam types, collective attention spam relies on the users themselves to seek out the content -- like breaking news, viral videos, and popular memes -- where the spam will be encountered, potentially increasing its effectiveness and reach. We study the presence of collective attention spam in one popular service, Twitter, and we develop spam classifiers to detect spam messages generated by collective attention spammers. Since many instances of collective attention are bursty and unexpected, it is difficult to build spam detectors to pre-screen them before they arise; hence, we examine the effectiveness of quickly learning a classifier based on the first moments of a bursting phenomenon. Through initial experiments over a small set of trending topics on Twitter, we find encouraging results, suggesting that collective attention spam may be identified early in its life cycle and shielded from the view of unsuspecting social media users.
Starting Page	48
Ending Page	55
Page Count	8
File Format	PDF
ISBN	9781450312370
DOI	10.1145/2184305.2184316
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social media Collective attention Spam
Content Type	Text
Resource Type	Article

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