A Twitter Analytic Tool to Measure Opinion, Influence and Trust

Abstract

We are using combination of data-mining and text-mining techniques to measure opinion, influence, and trust spread in Social Networks within the domain of political elections. In particular we study the case of Twitter and focus on three 2012 presidential elections: the American, the French, and the Egyptian. We propose a new real-time analytic tool for elections. The proposed tool collects live tweet streams on frequent time intervals from followers of presidential candidates and computes a mix of qualitative and quantitative indicators to measure our three parameters (metrics dashboard). English, French and Arabic tweets are analyzed taking into consideration important context background information. We focus more on “Arabic,” and present three “Tweet Coloring” algorithms that speculate voters’ inclination and impression about the candidates and the influence of the candidates on the voters. On the other hand, three “Edge Coloring” algorithms are used to speculate on opinion, influence, and trust spread (cascading) through Twitter social network graphs. The algorithms compute inferred value ratings through polling neighbors and weighted averaging. A prototype of the proposed tool is implemented on-top of “Nodexl”; an open source template for Microsoft Excel that allows automated connection to a social network server and import (Using Twitter APIs) any data stream into the usual Excel environment. Tweet coloring and Edge coloring algorithms are implemented as Excel Macros with selective setting to either surface, shallow, or deep analysis parameters. Visualization graphs are provided that allow dynamic filtering, vertex grouping, adjusted appearance (zoom into areas of interest), graph metric calculations, etc. 