Credibility of information refers to its believability or the believability of its sources. We explore the impact of credibility-inspired
indicators on the task of blog post retrieval, following the intuition that more credible blog posts are preferred by searchers.
Based on a previously introduced credibility framework for blogs, we define several credibility indicators, and divide them
into post-level (e.g., spelling, timeliness, document length) and blog-level (e.g., regularity, expertise, comments) indicators.
The retrieval task at hand is precision-oriented, and we hypothesize that the use of credibility-inspired indicators will
positively impact precision. We propose to use ideas from the credibility framework in a reranking approach to the blog post
retrieval problem: We introduce two simple ways of reranking the top n of an initial run. The first approach, Credibility-inspired
reranking, simply reranks the top n of a baseline based on the credibility-inspired score. The second approach, Combined reranking,
multiplies the credibility-inspired score of the top n results by their retrieval score, and reranks based on this score.
Results show that Credibility-inspired reranking leads to larger improvements over the baseline than Combined reranking, but
both approaches are capable of improving over an already strong baseline. For Credibility-inspired reranking the best performance
is achieved using a combination of all post-level indicators. Combined reranking works best using the post-level indicators
combined with comments and pronouns. The blog-level indicators expertise, regularity, and coherence do not contribute positively
to the performance, although analysis shows that they can be useful for certain topics. Additional analysis shows that a relative
small value of n (15-25) leads to the best results, and that posts that move up the ranking due to the integration of reranking
based on credibility-inspired indicators do indeed appear to be more credible than the ones that go down.