Difference between revisions of "Mining Latent Relations in Peer-Production Environments: a Case Study with Wikipedia Article Similarity and Controversy"

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'''Mining Latent Relations in Peer-Production Environments: a Case Study with Wikipedia Article Similarity and Controversy''' - scientific work related to Wikipedia quality published in 2012, written by Chenliang Li, Anwitaman Datta and Aixin Sun.
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'''Mining Latent Relations in Peer-Production Environments: a Case Study with Wikipedia Article Similarity and Controversy''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Chenliang Li]], [[Anwitaman Datta]] and [[Aixin Sun]].
  
 
== Overview ==
 
== Overview ==
As people participate actively in social networking and peer-production sites, there are additional, implicit relations that emerge from various user activities. Mining such latent relations, or wisdom of crowds, is in itself an important area of ongoing research, with both general as well as domain-specific custom-made techniques. In this paper, authors propose a new similarity measure, which authors call expert-based similarity to discover semantic relations among Wikipedia articles from the co-editorship perspective. Also, different kinds of relations among entities may reveal diverse information. Both to explore and expose such a premise, authors carry out a case study leveraging on multiple relations among Wikipedia articles. Specifically, authors use expert-based similarity as well as other standard similarity measures, to discern the influence and impact of several factors which are hypothysed to generate controversies in Wikipedia articles. In the context of Wikipedia-specific research, case study helps better differentiate the degree of impact of some of the possible causes of controversies.
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As people participate actively in [[social network]]ing and peer-production sites, there are additional, implicit relations that emerge from various user activities. Mining such latent relations, or wisdom of crowds, is in itself an important area of ongoing research, with both general as well as domain-specific custom-made techniques. In this paper, authors propose a new similarity measure, which authors call expert-based similarity to discover semantic relations among [[Wikipedia]] articles from the co-editorship perspective. Also, different kinds of relations among entities may reveal diverse information. Both to explore and expose such a premise, authors carry out a case study leveraging on multiple relations among Wikipedia articles. Specifically, authors use expert-based similarity as well as other standard similarity [[measures]], to discern the influence and impact of several factors which are hypothysed to generate controversies in Wikipedia articles. In the context of Wikipedia-specific research, case study helps better differentiate the degree of impact of some of the possible causes of controversies.

Revision as of 08:26, 14 March 2020

Mining Latent Relations in Peer-Production Environments: a Case Study with Wikipedia Article Similarity and Controversy - scientific work related to Wikipedia quality published in 2012, written by Chenliang Li, Anwitaman Datta and Aixin Sun.

Overview

As people participate actively in social networking and peer-production sites, there are additional, implicit relations that emerge from various user activities. Mining such latent relations, or wisdom of crowds, is in itself an important area of ongoing research, with both general as well as domain-specific custom-made techniques. In this paper, authors propose a new similarity measure, which authors call expert-based similarity to discover semantic relations among Wikipedia articles from the co-editorship perspective. Also, different kinds of relations among entities may reveal diverse information. Both to explore and expose such a premise, authors carry out a case study leveraging on multiple relations among Wikipedia articles. Specifically, authors use expert-based similarity as well as other standard similarity measures, to discern the influence and impact of several factors which are hypothysed to generate controversies in Wikipedia articles. In the context of Wikipedia-specific research, case study helps better differentiate the degree of impact of some of the possible causes of controversies.