DocumentCode :
2773979
Title :
Predicting Reciprocity in Social Networks
Author :
Cheng, Justin ; Romero, Daniel M. ; Meeder, Brendan ; Kleinberg, Jon
Author_Institution :
Comput. Sci., Cornell Univ., Ithaca, NY, USA
fYear :
2011
fDate :
9-11 Oct. 2011
Firstpage :
49
Lastpage :
56
Abstract :
In social media settings where users send messages to one another, the issue of reciprocity naturally arises: does the communication between two users take place only in one direction, or is it reciprocated? In this paper we study the problem of reciprocity prediction: given the characteristics of two users, we wish to determine whether the communication between them is reciprocated or not. We approach this problem using decision trees and regression models to determine good indicators of reciprocity. We extract a network based on directed @-messages sent between users on Twitter, and identify measures based on the attributes of nodes and their network neighborhoods that can be used to construct good predictors of reciprocity. Moreover, we find that reciprocity prediction forms interesting contrasts with earlier network prediction tasks, including link prediction, as well as the inference of strengths and signs of network links.
Keywords :
decision trees; regression analysis; social networking (online); Twitter; decision trees; directed @-messages; network extraction; reciprocity prediction; regression models; social media settings; social networks; Accuracy; Computer science; Decision trees; Educational institutions; Media; Twitter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4577-1931-8
Type :
conf
DOI :
10.1109/PASSAT/SocialCom.2011.110
Filename :
6113094
Link To Document :
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