# Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach

arXiv: https://arxiv.org/abs/2102.05954 · HTML: https://mlsanity.com/paper/2102.05954/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Robust Regression via Hard Thresholding](https://mlsanity.com/paper/1506.02428/) (2015)
- [COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution](https://mlsanity.com/paper/1507.02293/) (2015)
- [Guarantees for Greedy Maximization of Non-submodular Functions with Applications](https://mlsanity.com/paper/1703.02100/) (2017)
- [Learning and Forecasting Opinion Dynamics in Social Networks](https://mlsanity.com/paper/1506.05474/) (2015)
- [The FAST Algorithm for Submodular Maximization](https://mlsanity.com/paper/1907.06173/) (2019)
- [On Submodularity and Controllability in Complex Dynamical Networks](https://mlsanity.com/paper/1404.7665/) (2014)
- [Submodular Observation Selection and Information Gathering for Quadratic Models](https://mlsanity.com/paper/1905.09919/) (2019)
- [A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks](https://mlsanity.com/paper/1709.08823/) (2017)
- [Cheshire: An Online Algorithm for Activity Maximization in Social Networks](https://mlsanity.com/paper/1703.02059/) (2017)
- [Approximate Supermodularity Bounds for Experimental Design](https://mlsanity.com/paper/1711.01501/) (2017)
- [Variational Policy for Guiding Point Processes](https://mlsanity.com/paper/1701.08585/) (2017)
- [Detecting Sockpuppets in Deceptive Opinion Spam](https://mlsanity.com/paper/1703.03149/) (2017)
- [Game-Theoretic Analysis of the Hegselmann-Krause Model for Opinion Dynamics in Finite Dimensions](https://mlsanity.com/paper/1412.6546/) (2014)
- [Distinguishing between Topical and Non-topical Information Diffusion Mechanisms in Social Media](https://mlsanity.com/paper/1603.04425/) (2016)

## Built on by (most cited first, 1 of 1)

- [Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection](https://mlsanity.com/paper/2210.11033/) (2022)

## Abstract (arXiv)

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.