Same wavelength group identification from online social networks: A general framework

Abstract

Reacting to social issues or events through Online Social Networks has become a social habit. Social scientists have identified several network relationships and dimensions that induce homophily. Sentiments or opinions towards different issues have been observed as a key dimension which characterizes human behaviour. People usually express their sentiments towards various issues. Different persons from different walks of social life may share same opinion towards various issues. When these persons constitute a group, such groups can be conveniently termed same wavelength groups. We propose a novel framework based on sentiments and an algorithm to identify such same wavelength groups from online social networks like twitter. The proposed algorithm generates same wavelength groups in polynomial time for relatively small set of events. The analysis of such groups would be of help in unravelling their response patterns and behavioural features.