TY - JOUR
T1 - Leveraging social media networks for classification
AU - Tang, Lei
AU - Liu, Huan
N1 - Funding Information:
Acknowledgements This research is, in part, sponsored by the Air Force Office of Scientific Research grant FA95500810132. We thank BlogCatalog and Flickr for providing APIs. We acknowledge Xufei Wang and Munmun De Choudhury for their help with data collection. We also wish to acknowledge Subbarao Kambhampati and Pat Langley for their suggestions to improve this work. We thank the anonymous reviewers wholeheartedly for their expert opinions and constructive suggestions.
PY - 2011/11
Y1 - 2011/11
N2 - 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.
AB - 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.
KW - Collective inference
KW - Relational learning
KW - Social media
KW - Social network analysis
KW - Within-network classification
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U2 - 10.1007/s10618-010-0210-x
DO - 10.1007/s10618-010-0210-x
M3 - Article
AN - SCOPUS:80855134335
SN - 1384-5810
VL - 23
SP - 447
EP - 478
JO - Data Mining and Knowledge Discovery
JF - Data Mining and Knowledge Discovery
IS - 3
ER -