Calculating expertise confidence based on content and social proximity

1. A method, comprising:

executing, via a processor, a document-oriented search based on a query in an index of documents to generate a set of document results, each document in the set of document results is associated with at least one potential expert;

analyzing, via the processor, the document results to produce a list of potential experts;

calculating, via the processor, an expertise score for each potential expert based on a calculated content score and metadata score for each potential expert;

calculating, via the processor, an evidence diversity score for each potential expert;

calculating, via the processor, a confidence score for each potential expert based on a diversity-constrained content score and a diversity-constrained metadata score for each potential expert, wherein:

the diversity-constrained content score is calculated using the evidence diversity score, comprising a predetermined threshold number of different activities associated with the potential expert, and the content score for the potential expert;

the diversity-constrained metadata score is calculated using the evidence diversity score and the metadata score for the potential expert;

the content score is calculated based on a number of different content document types and associations associated with the potential expert, the content document types and associations are gathered by parsing websites and stored in a data repository;

the metadata score is calculated based on profile-related information associated with the potential expert; and

the confidence score is further calculated based on a social score, wherein the processor is configured to generate a representation of connections between the potential experts, the social score for each potential expert is calculated using the representation of connections and based on a number of connections to other potential experts; and

sending a list of potential experts whose confidence scores are above a confidence score threshold to a client device.

2. The method of claim 1 , further comprising:

selecting, via the processor, a predetermined number of potential experts whose expertise scores meeting a threshold expertise score from the list of potential experts;

calculating, via the processor, the evidence diversity score for each selected expert; and

calculating, via the processor, a confidence score for each selected expert using the evidence diversity score for each selected expert.

3. The method of claim 2 , further comprising sorting the list of potential experts by the expertise scores, wherein the predetermined number of selected experts being selected from the sorted list of potential experts.

4. The method of claim 1 , further comprising calculating the diversity-constrained content score comprising constraining the content score based on the evidence diversity score and calculating the diversity-constrained metadata score comprising constraining the metadata score based on the evidence diversity score.

5. The method of claim 1 , wherein the confidence scores are calculated based on preconfigured thresholds.

6. The method of claim 1 , wherein the query indicates an expertise, and the confidence score is used to indicate a level of certainty in the expertise for each potential expert.

7. The method of claim 1 , wherein the list of potential experts is filtered according to the confidence scores and sorted by the expertise scores.