Partisan Asymmetries in Online Political Activity

We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered partisan community structure. Using cluster membership as a high-fidelity (87% accuracy) proxy for political affiliation, we characterize a wide range of differences in the behavior, communication and social connectivity of left- and right-leaning Twitter users. We find that in contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.Comment: 17 pages, 10 figures, 6 table

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

DNA-inspired online behavioral modeling and its application to spambot detection

We propose a strikingly novel, simple, and effective approach to model online user behavior: we extract and analyze digital DNA sequences from user online actions and we use Twitter as a benchmark to test our proposal. We obtain an incisive and compact DNA-inspired characterization of user actions. Then, we apply standard DNA analysis techniques to discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports our proposal, showing its effectiveness and viability. To the best of our knowledge, we are the first ones to identify and adapt DNA-inspired techniques to online user behavioral modeling. While Twitter spambot detection is a specific use case on a specific social media, our proposed methodology is platform and technology agnostic, hence paving the way for diverse behavioral characterization tasks