  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.
