Content-based trust and bias classification via biclustering

Siklósi, Dávid and Daróczy, Bálint Zoltán and Benczúr, András (2012) Content-based trust and bias classification via biclustering. In: ACM International Conference Proceeding Series, 2012-04-16, Lyon, Franciaország. 10.1145/2184305.2184314


Download (199kB) | Preview


In this paper we improve trust, bias and factuality classification over Web data on the domain level. Unlike the majority of literature in this area that aims at extracting opinion and handling short text on the micro level, we aim to aid a researcher or an archivist in obtaining a large collection that, on the high level, originates from unbiased and trustworthy sources. Our method generates features as Jensen-Shannon distances from centers in a host-term biclustering. On top of the distance features, we apply kernel methods and also combine with baseline text classifiers. We test our method on the ECML/PKDD Discovery Challenge data set DC2010. Our method improves over the best achieved text classification NDCG results by over 3--10% for neutrality, bias and trustworthiness. The fact that the ECML/PKDD Discovery Challenge 2010 participants reached an AUC only slightly above 0.5 indicates the hardness of the task.

Item Type: Conference or Workshop Item (-)
Additional Information: #Könyv Szerző ismeretlen
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: ?? R104a ??
Depositing User: EPrints Admin
Date Deposited: 18 Feb 2013 13:57
Last Modified: 05 Feb 2014 12:28

Update Item Update Item