The reputation system is content-driven, as it measures the 
reputation of users on the basis of how their contributions fare, rather
than on the basis of user-to-user feedback.  The main idea is as 
follows:

• Authors whose contributions are preserved in full or in part gain reputation.

  In particular, users gain reputation when their reputations are

  preserved unchanged, but also (in slightly lower measure) when they are

  adapted, reworded, or re-formatted.

• Authors whose contributions are reverted, or almost entirely undone, lose reputation.

• “Competing to Share Expertise: the Taskcn Knowledge Sharing Community”Jiang Yang, Lada Adamic and Mark Ackerman 2008http://bibsi.cms.si.umich.edu/no… ;
• “Expertise networks in online communities: structure and algorithms” Jun Zhang, Mark Ackerman, Lada Adamic http://portal.acm.org/purchase.c…
  (paying access, send me a message);

In the latter they tests various approaches including Kleinberg Hits and PageRank.

FIDE, the international chess ranking system, uses ELO ratings to build reputation ranks. For that, a sufficient number of pairwise comparisons is needed.

For social platforms (Twitter, Facebook, etc.) PeerIndex, my company runs a topic-specific PageRank-like calculation based on sharing activity and some other features. For relevance, we do it on a topic-by-topic basis, extracted by topic-modelling. We refer to this as resonance and it essentially shows the trustworthiness or resonance of a user on a given topic.

As an example visit http://www.peerindex.net/topic/v… 
The column marked “Res.” shows the resonance of people listed in the topic of venture capital. The scores run 1 to 100 and maps a fixed distribution of the PageRank of nodes in the venture-capital network. 

Every user, e.g. http://www.peerindex.net/infoarb…, is associated to his or her resonance on a list of Topics (in little green boxes). We calculate those for several thousands of topics. Where the network is too small, we don’t.

This isn’t exactly the published implementation of PageRank, but it’s close.

1. A user answers a question on Wisdio. The corresponding topic of the question is then listed in a user’s knowledge profile. (List of all areas of knowledge the user has interacted in.)
2. Based upon user votes, the quality of answer affects how a user ranks amongst other users who have shared knowledge in similar categories. Votes from users who are highly ranked in the corresponding category have more weight than those with lower ranks.
3. The ranking/scores from all answers are then aggregated to assign users a Wisdio Authority Rating score between 1-100. A perfect 100 is near impossible to receive since no person is truly knowledgeable about everything.

This system is fairly new, but I think it definitely has a great direction. Instead of showing influence of knowledge shared like Klout, Wisdio wants to show the reliability of knowledge shared.

• Discoverers
• Followers
• Spammers
• Trolls

The main point of the algorithm was to be spam resistant and to favor quality over quantity.