Promoter Account Detection in Twitter

Twitter is an online social network and micro-blog that becomes an alternative media for sharing and getting information. In the political area, Twitter provides various features as a media to promote campaign and get a good imaging for political party or contestant. In order to get a good opinion from other users, the contestant can manipulate their success with a massive promotion. This promotion activity could lead to public opinion that is not consistent with the facts. So that, we need to determine whether this is promoter account or not. In this paper, we propose a new framework for promoter account detection. This framework based on twitter content to detect promoter account according to their existence in topic of promotion. This framework employs k-means approach in order to cluster topic of promotion based on twitter\u27s content. From each cluster, we evaluate the existence of promoter account. With very simple approach, the results obtained on experiment show that this framework is effective for promoter account detection

Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams

Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We introduce an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events. We describe a Web service that leverages this framework to track political memes in Twitter and help detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We present some cases of abusive behaviors uncovered by our service. Finally, we discuss promising preliminary results on the detection of suspicious memes via supervised learning based on features extracted from the topology of the diffusion networks, sentiment analysis, and crowdsourced annotations