Towards Less Biased Web Search

Abstract

Web search engines now serve as essential assistant to help users make decisions in different aspects. Delivering correct and impartial information is a crucial functionality for search engines as any false information may lead to unwise decision and thus undesirable consequences. Unfortunately, a recent study revealed that Web search engines tend to provide biased information with most results supporting users' beliefs conveyed in queries regardless of the truth. In this paper we propose to alleviate bias in Web search through predicting the topical polarity of documents, which is the overall tendency of one document regarding whether it supports or disapproves the belief in query. By applying the prediction to balance search results, users would receive less biased information and therefore make wiser decision. To achieve this goal, we propose a novel textual segment extraction method to distill and generate document feature representation, and leverage convolution neural network, an effective deep learning approach, to predict topical polarity of documents. We conduct extensive experiments on a set of queries with medical indents and demonstrate that our model performs empirically well on identifying topical polarity with satisfying accuracy. To our best knowledge, our work is the first on investigating the mitigation of bias in Web search and could provide directions on future research.