caa a22 4500 445871288 CHVBK 20180317145512.0 cr unu---uuuuu 170323e20111101xx s 000 0 eng 10.1007/s10618-010-0210-x doi (NATIONALLICENCE)springer-10.1007/s10618-010-0210-x Leveraging social media networks for classification [Elektronische Daten] [Lei Tang, Huan Liu] Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors' latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes. The Author(s), 2011 Social media nationallicence Social network analysis nationallicence Relational learning nationallicence Within-network classification nationallicence Collective inference nationallicence Tang Lei Advertising Sciences, Yahoo! Labs, 95054, Santa Clara, CA, USA aut Liu Huan Computer Science and Engineering, Arizona State University, 85287, Tempe, AZ, USA aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/3(2011-11-01), 447-478 1384-5810 23:3<447 2011 23 10618 https://doi.org/10.1007/s10618-010-0210-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0210-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Tang Lei Advertising Sciences, Yahoo! Labs, 95054, Santa Clara, CA, USA aut NATIONALLICENCE 700 1- Liu Huan Computer Science and Engineering, Arizona State University, 85287, Tempe, AZ, USA aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/3(2011-11-01), 447-478 1384-5810 23:3<447 2011 23 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer