skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Despotic Regimes Instilling Fear in Citizens to Suppress Protests
Fear of reprisals such as violence and punishment can inhibit citizens from speaking out, or make them more reluctant to act, in opposition to a repressive regime. Protests are one form of opposition, and their growth has been successfully modeled as an influence-based contagion process within a social network (representing a population). In these models, an individual joins a protest if a sufficient number of her neighbors has already joined. This required number of neighbors is often called a “threshold.” In this study, we model a regime’s ability to suppress protests by instilling fear in a subset of a population, and this fear is manifested by an increase in a person’s threshold. We consider different social networks, numbers of seed nodes, and amounts of fear. Through simulations, we present several results. For example, we demonstrate that, for the objective of reducing the size of a protest, inducing fear can be more advantageous than removing nodes from a network.  more » « less
Award ID(s):
1916670
PAR ID:
10310249
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the International Conference on Advances in Social Network Analysis and Mining
ISSN:
2473-991X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, Social network analysis and mining)
    Neighborhood effects have an important role in evacuation decision-making by a family. Owing to peer influence, neighbors evacuating can motivate a family to evacuate. Paradoxically, if a lot of neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of crime and looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold, independent cascade, and linear threshold models. Here, we propose a new threshold-based graph dynamical system model, 2mode-threshold, which captures this dichotomy. We study theoretically the dynamical properties of 2mode-threshold in different networks, and find significant differences from a standard threshold model. We build and characterize small world networks of Virginia Beach, VA, where nodes are geolocated families (households) in the city and edges are interactions between pairs of families. We demonstrate the utility of our behavioral model through agent-based simulations on these small world networks. We use it to understand evacuation rates in this region, and to evaluate the effects of modeling parameters on evacuation decision dynamics. Specifically, we quantify the effects of (1) network generation parameters, (2) stochasticity in the social network generation process, (3) model types (2mode-threshold vs. standard threshold models), (4) 2mode-threshold model parameters, (5) and initial conditions, on computed evacuation rates and their variability. An illustrative example result shows that the absence of looting effect can overpredict evacuation rates by as much as 50%. 
    more » « less
  2. ; ; ; (, PLOS ONE)
    Topaz, Chad M (Ed.)
    Ukraine’s tug-of-war between Russia and the West has had significant and lasting consequences for the country. In 2013, Viktor Yanukovych, the Ukrainian president aligned with Russia, opted against signing an association agreement with the European Union. This agreement aimed to facilitate trade and travel between the EU and Ukraine. This decision sparked widespread protests that coalesced in Kyiv’s Maidan Square, eventually becoming known as the Euromaidan protests. In this study, we analyze the protest data from 2013, sourced from Ukraine’s Center for Social and Labor Research. Despite the dataset’s limitations and occasional inconsistencies, we demonstrate the extraction of valuable insights and the construction of a descriptive model from such data. Our investigation reveals a pre-existing state of self-excitation within the system even before the onset of the Euromaidan protests. This self-excitation intensified during the Euromaidan protests. A statistical analysis indicates that the government’s utilization of force correlates with increased future protests, exacerbating rather than quelling the protest movement. Furthermore, we introduce the implementation of Hawkes process models to comprehend the spatiotemporal dynamics of the protest activity. Our findings highlight that, while protest activities spread across the entire country, the driving force behind the dynamics of these protests was the level of activity in Kyiv. Furthermore, in contrast to prior research that emphasized geographical proximity as a key predictor of event propagation, our study illustrates that the political alignment among oblasts, which are the distinct municipalities comprising Ukraine, had a more profound impact than mere geographic distance. This underscores the significance of social and cultural factors in molding the trajectory of political movements. 
    more » « less
  3. ; ; ; ; (, Social network analysis and mining)
    null (Ed.)
    Neighborhood e ects have an important role in evacuation decision-making by a family. Owing to peer influence, neighbors evacuating can motivate a family to evacuate. Paradoxically, if a lot of neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of crime and looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold, independent cascade, and linear threshold models. Here, we propose a new threshold-based graph dynamical system model, 2mode-threshold, which captures this dichotomy. We study theoretically the dynamical properties of 2mode-threshold in di fferent networks, and fi nd signi ficant diff erences from a standard threshold model. We build and characterize small world networks of Virginia Beach, VA, where nodes are geolocated families (households) in the city and edges are interactions between pairs of families. We demonstrate the utility of our behavioral model through agent-based simulations on these small world networks. We use it to understand evacuation rates in this region, and to evaluate the e ffects of modeling parameters on evacuation decision dynamics. Speci fically, we quantify the effects of (i) network generation parameters, (ii) stochasticity in the social network generation process, (iii) model types (2mode-threshold vs. stan- dard threshold models), (iv) 2mode-threshold model parameters, (v) and initial conditions, on computed evacuation rates and their variability. An illustrative example result shows that the absence of looting e ect can overpredict evacuation rates by as much as 50%. 
    more » « less
  4. ; ; ; (, American Political Science Review)
    null (Ed.)
    Larger protests are more likely to lead to policy changes than small ones are, but whether or not attendance estimates provided in news or generated from social media are biased is an open question. This letter closes the question: news and geolocated social media data generate accurate estimates of protest size variation. This claim is substantiated using cellphone location data from more than 10 million individuals during the 2017 United States Women’s March protests. These cellphone estimates correlate strongly with those provided in news media as well as three size estimates generated using geolocated tweets, one text-based and two based on images. Inferences about protest attendance from these estimates match others’ findings about the Women’s March. 
    more » « less
  5. ; (, Proceedings of the International AAAI Conference on Web and Social Media)
    This paper introduces the Multimodal Chile & Venezuela Protest Event Dataset (MMCHIVED). MMCHIVED contains city-day event data using a new source of data, text and images shared on social media. These data enables the improved measurement of theoretically important variables such as protest size, protester and state violence, protester demographics, and emotions. In Venezuela, MMCHIVED records many more protests than existing datasets. In Chile, it records slightly more events than the Armed Conflict Location and Events Dataset (ACLED). These extra events are from small cities far from Caracas and Santiago, an improvement of coverage over datasets that rely on newspapers, and the paper confirms they are true positives. While MMCHIVED covers protest events in Chile and Venezuela, the approach used in the paper is generalizable and could generate protest event data in 107 countries containing 97.14% of global GDP and 82.7% of the world's population. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email