A Study of Factuality, Objectivity and Relevance: Three Desiderata in Large-Scale Information Retrieval?

TL;DR

This paper investigates how factuality and objectivity detection, enabled by NLP, can enhance large-scale information retrieval, showing that factuality significantly improves retrieval precision especially for uncurated web data.

Contribution

It introduces the first study of factuality and objectivity in IR, demonstrating their impact on retrieval effectiveness and analyzing their relation to document relevance.

Findings

Factuality correlates positively with document relevance.

Factuality improves retrieval precision by over 10%.

Objectivity has mixed effects on retrieval performance.

Abstract

Much of the information processed by Information Retrieval (IR) systems is unreliable, biased, and generally untrustworthy [1], [2], [3]. Yet, factuality & objectivity detection is not a standard component of IR systems, even though it has been possible in Natural Language Processing (NLP) in the last decade. Motivated by this, we ask if and how factuality & objectivity detection may benefit IR. We answer this in two parts. First, we use state-of-the-art NLP to compute the probability of document factuality & objectivity in two TREC collections, and analyse its relation to document relevance. We find that factuality is strongly and positively correlated to document relevance, but objectivity is not. Second, we study the impact of factuality & objectivity to retrieval effectiveness by treating them as query independent features that we combine with a competitive language modelling baseline. Experiments with 450 TREC queries show that factuality improves precision >10% over strong baselines, especially for uncurated data used in web search; objectivity gives mixed results. An overall clear trend is that document factuality & objectivity is much more beneficial to IR when searching uncurated (e.g. web) documents vs. curated (e.g. state documentation and newswire articles). To our knowledge, this is the first study of factuality & objectivity for back-end IR, contributing novel findings about the relation between relevance and factuality/objectivity, and statistically significant gains to retrieval effectiveness in the competitive web search task.