An Evaluation of the Effect of Spam on Twitter Trending Topics

Author

Stafford, G. ; Yu, Louis Lei

Author_Institution

Dept. of Comput. Sci., Pomona Coll., Claremont, CA, USA

fYear

2013

fDate

8-14 Sept. 2013

Firstpage

373

Lastpage

378

Abstract

In this paper we investigate to what extent the trending topics in Twitter, a popular social network, are manipulated by spammers. Researchers have developed various models for spam detection in social media, but there has been little analysis on the effects of spam on Twitter´s trending topics. We gathered over 9 million tweets in Twitter´s hourly trending topics over a 7 day period and extracted tweet features identified by previous research as relevant to spam detection. Hand-labeling a random sample of 1500 tweets allowed us to train a moderately accurate naive Bayes classifier for tweet classification. Our findings suggest that spammers do not drive the trending topics in Twitter, but may opportunistically target certain topics for their messages.

Keywords

Bayes methods; pattern classification; social networking (online); Twitter trending topics; hand-labeling; naive Bayes classifier; social media; social network; spam detection; tweet classification; Feature extraction; Market research; Measurement; Twitter; Unsolicited electronic mail; Twitter; data mining; machine learning; social networking analysis; spam;

fLanguage

English

Publisher

ieee

Conference_Titel

Social Computing (SocialCom), 2013 International Conference on

Conference_Location

Alexandria, VA

Type

conf

DOI

10.1109/SocialCom.2013.58

Filename

6693355