# Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions

arXiv: https://arxiv.org/abs/2208.06620 · HTML: https://mlsanity.com/paper/2208.06620/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 94 references that are in the map.
- First posted on arXiv 13 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity](https://mlsanity.com/paper/1602.06033/) (2016)
- [Learning and Forecasting Opinion Dynamics in Social Networks](https://mlsanity.com/paper/1506.05474/) (2015)
- [Feature Driven and Point Process Approaches for Popularity Prediction](https://mlsanity.com/paper/1608.04862/) (2016)
- [SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations](https://mlsanity.com/paper/1711.01679/) (2017)
- [NELA-GT-2019: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/2003.08444/) (2020)
- [Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions](https://mlsanity.com/paper/2109.00302/) (2021)
- [Estimating Attention Flow in Online Video Networks](https://mlsanity.com/paper/1908.07123/) (2019)
- [RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions](https://mlsanity.com/paper/2101.04751/) (2021)
- [Identifying Cost-effective Debunkers for Multi-stage Fake News Mitigation Campaigns](https://mlsanity.com/paper/2203.16854/) (2022)
- [Online Popularity under Promotion: Viral Potential, Forecasting, and the Economics of Time](https://mlsanity.com/paper/1703.01012/) (2017)
- [Interval-censored Hawkes processes](https://mlsanity.com/paper/2104.07932/) (2021)
- [Correlated Cascades: Compete or Cooperate](https://mlsanity.com/paper/1510.00936/) (2015)
- [Practical Guidelines for Ideology Detection Pipelines and Psychosocial Applications](https://mlsanity.com/paper/2208.04097/) (2022)
- [Information Interaction Profile of Choice Adoption](https://mlsanity.com/paper/2104.13695/) (2021)
- [Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks](https://mlsanity.com/paper/1901.07987/) (2019)
- [On the Complexity of Opinions and Online Discussions](https://mlsanity.com/paper/1802.06807/) (2018)
- [Identifying exogenous and endogenous activity in social media](https://mlsanity.com/paper/1808.00810/) (2018)

## Built on by (most cited first, 4 of 4)

- [Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement](https://mlsanity.com/paper/2502.04655/) (2025)
- [Mario at EXIST 2025: A Simple Gateway to Effective Multilingual Sexism Detection](https://mlsanity.com/paper/2507.10996/) (2025)
- [X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents](https://mlsanity.com/paper/2508.16021/) (2025)
- [Estimating Online Influence Needs Causal Modeling! Counterfactual Analysis of Social Media Engagement](https://mlsanity.com/paper/2505.19355/) (2025)

## Abstract (arXiv)

Online extremism has severe societal consequences, including normalizing hate speech, user radicalization, and increased social divisions. Various mitigation strategies have been explored to address these consequences. One such strategy uses positive interventions: controlled signals that add attention to the opinion ecosystem to boost certain opinions. To evaluate the effectiveness of positive interventions, we introduce the Opinion Market Model (OMM), a two-tier online opinion ecosystem model that considers both inter-opinion interactions and the role of positive interventions. The size of the opinion attention market is modeled in the first tier using the multivariate discrete-time Hawkes process; in the second tier, opinions cooperate and compete for market share, given limited attention using the market share attraction model. We demonstrate the convergence of our proposed estimation scheme on a synthetic dataset. Next, we test OMM on two learning tasks, applying to two real-world datasets to predict attention market shares and uncover latent relationships between online items. The first dataset comprises Facebook and Twitter discussions containing moderate and far-right opinions about bushfires and climate change. The second dataset captures popular VEVO artists' YouTube and Twitter attention volumes. OMM outperforms the state-of-the-art predictive models on both datasets and captures latent cooperation-competition relations. We uncover (1) self- and cross-reinforcement between far-right and moderate opinions on the bushfires and (2) pairwise artist relations that correlate with real-world interactions such as collaborations and long-lasting feuds. Lastly, we use OMM as a testbed for positive interventions and show how media coverage modulates the spread of far-right opinions.