| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 6857806 | Information Sciences | 2014 | 15 Pages |
Abstract
In this paper, we investigated the task of identifying different types of tip spam on a popular Brazilian LBSN system, namely Apontador. Based on a labeled collection of tips provided by Apontador as well as crawled information about users and locations, we identified three types of irregular tips, namely local marketing, pollution and, bad-mouthing. We leveraged our characterization study towards a classification approach able to differentiate these tips with high accuracy.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Helen Costa, Luiz H.C. Merschmann, FabrÃcio Barth, FabrÃcio Benevenuto,