Suchergebnisse

Several studies have shown how to approximately predict public opinion, such as in political elections, by analyzing user activities in blogging platforms and on-line social networks. The task is challenging for several reasons. Sample bias and automatic understanding of textual content are two of several non trivial issues. In this work we study how Twitter can provide some interesting insights concerning the primary elections of an Italian political party. State-of-the-art approaches rely on indicators based on tweet and user volumes, often including sentiment analysis. We investigate how to exploit and improve those indicators in order to reduce the bias of the Twitter users sample. We propose novel indicators and a novel content-based method. Furthermore, we study how a machine learning approach can learn correction factors for those indicators. Experimental results on Twitter data support the validity of the proposed methods and their improvement over the state of the art.

Several studies have shown how to approximately predict real-world phenomena, such as political elections, by analyzing user activities in micro-blogging platforms. This approach has proven to be interesting but with some limitations, such as the representativeness of the sample of users, and the hardness of understanding polarity in short messages. We believe that predictions based on social network analysis can be significantly improved by exploiting machine learning and complex network tools, where the latter pro- vides valuable high-level features to support the former in learning an accurate prediction function.

Several studies have shown how to approximately predict real-world phenomena, such as political elections, by ana- lyzing user activities in micro-blogging platforms. This ap- proach has proven to be interesting but with some limita- tions, such as the representativeness of the sample of users, and the hardness of understanding polarity in short mes- sages. We believe that predictions based on social network analysis can be significantly improved by exploiting machine learning and complex network tools, where the latter pro- vides valuable high-level features to support the former in learning an accurate prediction function.