Dia Pol Polarization or dialogue? A deep learning study of the Black lives matt...
Dia Pol Polarization or dialogue? A deep learning study of the Black lives matter and Me Too online social movements
This project studies whether interactions on social media between social movements working for race and gender equality and their countermovements trigger polarisation or dialogue by applying topic modelling and deep learning tech...
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Información proyecto Dia-Pol
Duración del proyecto: 31 meses
Fecha Inicio: 2021-04-07
Fecha Fin: 2023-11-21
Líder del proyecto
BOGAZICI UNIVERSITESI
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
145K€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
This project studies whether interactions on social media between social movements working for race and gender equality and their countermovements trigger polarisation or dialogue by applying topic modelling and deep learning techniques on big data. Specifically, I examine debates around the Black lives matter and Me Too movements in Europe with a focus on groups and individuals that are at their intersection––e.g., gay Black men, since such intersectional groups are subject to multiple and novel forms of domination due to the overlap of categories. I seek to find out whether dialogue dialogue forms around intersectional themes (e.g., respect to black gay men) or on less specific and controversial themes (e.g., gender equality) promoted by politically liberal and progressive users, and whether polarisation is associated with political conservativism. I aim to develop an algorithm to operate on an interactive online platform called Dia-Pol, to assist citizens, activists, NGOs, and decision makers, such as the European Commission, to find out which issues are the most polarising and how messages should be (re)shaped to address people’s sensitivities and avoid misunderstandings and deconstruct prejudices. This algorithm will generate information that can be incorporated into reports and studies, and will be applicable to other social media debates. This project is the first application of deep learning in political science and is one of the rare social science projects to use state-of-the-art computational techniques to test a rich theory. It foregrounds a powerful analytical tool that will be applicable to other social movement studies. Theoretically, it is the first study on intersectionality and echo chambers and on outcomes of movement interactions with their counter and synthetic movements. Therefore, its findings will start a novel research agenda. Also, the project will shift the focus away from studies of singular social movements to a comparative approach.