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The networked opinion diffusion in online social networks (OSN) is oftengoverned by the two genres of opinions - endogenous opinions that are driven bythe influence of social contacts among users, and exogenous opinions which areformed by external effects like news, feeds etc. Accurate demarcation ofendogenous and exogenous messages offers an important cue to opinion modeling,thereby enhancing its predictive performance. In this paper, we design a suiteof unsupervised classification methods based on experimental design approaches,in which, we aim to select the subsets of events which minimize differentmeasures of mean estimation error. In more detail, we first show that thesesubset selection tasks are NP-Hard. Then we show that the associated objectivefunctions are weakly submodular, which allows us to cast efficientapproximation algorithms with guarantees. Finally, we validate the efficacy ofour proposal on various real-world datasets crawled from Twitter as well asdiverse synthetic datasets. Our experiments range from validating predictionperformance on unsanitized and sanitized events to checking the effect ofselecting optimal subsets of various sizes. Through various experiments, wehave found that our method offers a significant improvement in accuracy interms of opinion forecasting, against several competitors.

Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach