  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.
