Proceedings Abstracts of the Twenty-Third International Joint Conference on Artificial Intelligence

What Users Care About: A Framework for Social Content Alignment / 1401
Lei Hou, Juanzi Li, Xiaoli Li, Jiangfeng Qu, Xiaofei Guo, Ou Hui, Jie Tang

With the rapid proliferation of social media, more and more people freely express their opinions (or comments) on news, products, and movies through online services such as forums, discussion groups, and microblogs. Those comments may be concerned with different aspects (topics) of the target Web document (e.g., a news page).It would be interesting to align the social comments to the corresponding subtopics contained in the Web document. In this paper, we propose a novel framework that is able to automatically detect the subtopics from a given Web document, and also align the associated social comments with the detected subtopics. This provides a new view of the Web standard document and its associated user generated content through topics, which facilitates the readers to quickly focus on those hot topics or grasp topics that they are interested in. Extensive experiments show that our proposed framework significantly outperforms the existing state-of-the-art methods in social content alignment.