Social Identity Model of Protest Emergence, an Agent-Based Simulation Model, 2019-2022

Chueca Del Cerro, Cristina (2023). Social Identity Model of Protest Emergence, an Agent-Based Simulation Model, 2019-2022. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-856155

Secessionist movements are notorious for their abilities to mobilize people. Although these movements might use economic grievances or policy preferences to attract support, national identity remains at the core of every secessionist movement, justifying their right, as a nation, to become an independent self-governing state. As these divisions on the basis of national identity grow wider, animosity between groups grows, contributing to reducing social cohesion and escalating the political conflict. This thesis is interested in understanding the role national identity polarisation plays in the emergence of protests around independence movements. Much of the recent debate in political sciences has been regarding the role of social media's filtering algorithms in the emergence of polarisation as well as the existence or prevalence of the so-called echo chambers. There is a lack of consensus around the extent to which social media filtering algorithms and online echo chambers promote polarisation and how this in turns affects protest mobilisation. This thesis proposes a social simulation approach to the topic of protest mobilisation dynamics from a political communication perspective to understand how national identity polarisation, through social network configurations and the media environment, contribute to the emergence of protest mobilisation in a context where secessionist movements are present. I developed an agent-based simulation model of protest mobilisation and social identity to answer such question. This is an abstract model representing a society, like Catalonia, where protests are taking place and aims to explain the process by which social media platforms and individual social networks promote national identity polarisation and, ultimately, protest mobilisation.

The data deposited here includes the NetLogo simulation code, RStudio data cleaning and analyses, as well as the simulation output data.

Data description (abstract)

I developed an agent-based model (ABM) which is a social simulation method, to explain protest mobilisation through national identity polarisation and how social media and individual social networks contribute in this process. From the simulation code, written in NetLogo, I collected data from multiple simulation runs of various parameter combinations. Then, I cleaned and analysed such data using RStudio.

There are three types of data files. NetLogo files that contain the simulation code for my ABM. RStudio files that contain the code for data cleaning and data analyses I carried out on the simulation outputs. The last data type are csv excel files containing the simulation results collected for each of the parameter combinations.

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