Collective Semi-Supervised Learning for User Profiling in Social Media
This work solves the problem of user profiling for personalization and recommendation in social media, but it is incremental as it builds on existing semi-supervised and collective learning methods.
The paper tackles the problem of inferring user attributes in social media by addressing limitations in integrating social relationships and labeled data scarcity, resulting in a novel Collective Semi-Supervised Learning (CSL) approach that demonstrated efficacy in experiments on Twitter data for attributes like account type and marital status.
The abundance of user-generated data in social media has incentivized the development of methods to infer the latent attributes of users, which are crucially useful for personalization, advertising and recommendation. However, the current user profiling approaches have limited success, due to the lack of a principled way to integrate different types of social relationships of a user, and the reliance on scarcely-available labeled data in building a prediction model. In this paper, we present a novel solution termed Collective Semi-Supervised Learning (CSL), which provides a principled means to integrate different types of social relationship and unlabeled data under a unified computational framework. The joint learning from multiple relationships and unlabeled data yields a computationally sound and accurate approach to model user attributes in social media. Extensive experiments using Twitter data have demonstrated the efficacy of our CSL approach in inferring user attributes such as account type and marital status. We also show how CSL can be used to determine important user features, and to make inference on a larger user population.