Finding of experts using behavioral aspects

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    As the social networks emerged like Twitter, the process of exploring experts has become an interesting topic. However, previous methods can never be used to learn about topic experts in Twitter. Some of the new methods make use of the relations existing between Twitter lists and users for exploring experts. A probabilistic method has been developed to explore the relations (i.e. follower, user-list and list-list relations) for finding experts. A Semi-Supervised Graph-based Ranking (SSGR) method is used to find the users global authority. Between users and given query a local relevance is computed. By understanding the global authority and local relevance of users, all of them are ranked and those with high scores for the ranking are retrieved which constitute the expert extraction. On the other hand a behavior extraction is done with respect to understandability, level of detail and writing style which contributes to the feature set. This feature extraction leads to the SVM (Support Vector Model) classification. Finally a behavioral oriented expert ranking is done by uncovering expert extraction and SVM classification which constitute the topic experts in Twitter.

     

     


  • Keywords


    Twitter; Expert Finding; SSGR (Semi-Supervised Graph-Based Ranking) SVM (Support Vector Model) Behavioral Oriented Expert Ranking.

  • References


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Article ID: 15510
 
DOI: 10.14419/ijet.v7i2.33.15510




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