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Article analysis:

The article is generally reliable and trustworthy, as it provides a detailed overview of an automatic approach for ranking answers in CQAs based on quality and recency of content. The article also provides a thorough evaluation of nine learning to rank algorithms, showing that Coordinate Ascent and LambdaMart have the best performance in this task. Furthermore, a feature analysis is provided which has shown that features related to the age of the response contributed to improving the ranking performance taking recency and quality into account.

The article does not appear to be biased or one-sided, as it presents both sides equally by providing an overview of existing works on answer ranking as well as presenting its own proposed approach for automatically ranking answers according to their quality and content’s recency. The article also does not appear to contain any unsupported claims or missing points of consideration, as it provides a detailed description of its proposed approach along with an evaluation of nine learning to rank algorithms and a feature analysis which shows how features related to the age of the response contribute towards improving the ranking performance taking both criteria into account.

The article does not appear to contain any promotional content or partiality either, as it objectively presents its proposed approach without favoring any particular algorithm or feature over another. Additionally, possible risks are noted throughout the article such as low-quality information due to loose edit control in CQA environments, subjective user voting mechanisms, etc., making sure readers are aware of potential risks associated with using such approaches for answer ranking in CQAs.

In conclusion, overall this article appears reliable and trustworthy with no major issues regarding biasness or one-sided reporting being present within it.