Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach

TL;DR

This paper introduces an experimental design approach with unsupervised classification methods to distinguish endogenous and exogenous opinions in social networks, improving opinion prediction accuracy.

Contribution

It develops a novel subset selection framework based on weakly submodular functions for demarcating opinion types, with proven approximation guarantees.

Findings

Significant improvement in opinion forecasting accuracy.

Effective subset selection on real-world Twitter datasets.

Validation of methods on synthetic datasets.

Abstract

The networked opinion diffusion in online social networks (OSN) is often governed by the two genres of opinions - endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news, feeds etc. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this paper, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.