When the levee breaks: A forecasting model of violent and nonviolent dissent

Pinckney, Jonathan; RezaeeDaryakenari, Babak

doi:10.1080/03050629.2022.2090933

Cited by 4 publications

(2 citation statements)

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“…Population size and growth can be a significant factor in the likelihood of conflict [40][41][42]. For instance, fast population growth may put a strain on the resources available and heighten the struggle for food, water, and land.…”

Section: Socioeconomic Conditioning Factorsmentioning

confidence: 99%

“…Political unrest can occasionally result from a drop in GDP because governments may find it difficult to provide the fundamental necessities for their populace. Thus, violence or conflicts may be influenced by GDP, which can be selected as a variable, i.e., predictor for conflict susceptibility [41,42]. Some studies suggest that GDP per capita (PPP) has less importance than other socioeconomic indicators.…”

Section: Socioeconomic Conditioning Factorsmentioning

confidence: 99%

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Identifying Conditioning Factors and Predictors of Conflict Likelihood for Machine Learning Models: A Literature Review

Obukhov

Brovelli

2023

IJGI

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In this research, we focused on armed conflicts and related violence. The study reviewed the use of machine learning to predict the likelihood of conflict escalation and the role of conditioning factors. The results showed that machine learning and predictive models could help identify conflict-prone locations and geospatial factors contributing to conflict escalation. The study found 46 relevant papers and emphasized the importance of considering unique predictors and conditioning factors for each conflict. It was found that the conflict susceptibility of a region is influenced principally by its socioeconomic conditions and its political/governance factors. We concluded that machine learning has the potential to be a valuable tool in conflict analysis and, therefore, it can be an asset in conflict mitigation and prevention, but the accuracy of the models depends on data quality and the careful selection of conditioning factors. Future research should aim to refine the methodology for more accurate prediction of the models.

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Section: Socioeconomic Conditioning Factorsmentioning

confidence: 99%

Section: Socioeconomic Conditioning Factorsmentioning

confidence: 99%

Identifying Conditioning Factors and Predictors of Conflict Likelihood for Machine Learning Models: A Literature Review

Obukhov

Brovelli

2023

IJGI

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Multidimensional geographic factors behind conflicts: a case study in Sudan

Gong,

Li,

Belabbes

et al. 2024

International Journal of Digital Earth

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Who Rallies Round the Flag? The Impact of the US Sanctions on Iranians’ Attitude toward the Government

RezaeeDaryakenari,

Ghafouri,

Kasap

2024

Foreign Policy Analysis

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While politicians often argue that economic sanctions can induce policy changes in targeted states by undermining elite and public support for the reigning government, the efficacy of these measures, particularly against non-democratic regimes, is debatable. We propose that, counterintuitively, economic sanctions can bolster rather than diminish support for the sanctioned government, even in non-democratic contexts. However, this support shift and its magnitude can differ across various political factions and depend on the nature of the sanctions. To empirically evaluate our theoretical expectations, we use supervised machine learning to scrutinize nearly 2 million tweets from over 1,000 Iranian influencers, assessing their responses to both comprehensive and targeted sanctions during Donald Trump’s presidency. Our analysis shows that comprehensive sanctions generally improved sentiments toward the Iranian government, even among its moderate oppositions, rendering them more aligned with the state’s stance. Conversely, while targeted sanctions elicited a milder rally-around-the-flag response, the identity of the targeted entity plays a crucial role in determining the scale of this reaction.

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When the levee breaks: A forecasting model of violent and nonviolent dissent

Cited by 4 publications

References 50 publications

Identifying Conditioning Factors and Predictors of Conflict Likelihood for Machine Learning Models: A Literature Review

Identifying Conditioning Factors and Predictors of Conflict Likelihood for Machine Learning Models: A Literature Review

Multidimensional geographic factors behind conflicts: a case study in Sudan

Who Rallies Round the Flag? The Impact of the US Sanctions on Iranians’ Attitude toward the Government

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