This study explored whether interactions between social movements yield polarization or encourage dialogue using the cases of the Black Lives Matter (BLM) and Me Too (MT) movements. The project had 3 objectives: (a) pioneer a novel research agenda on the dynamics of movement interactions, filling a void in political science, sociology, communication, and race and gender studies; (b) devise an algorithm to aid stakeholders, e.g. citizens, activists and NGOs, in identifying divisive issues and crafting messages to navigate sensitivities; and (c) advance computational social science (CSS) within political science and at the host university by establishing a CSS lab at Bogazici.
Studying how social movements interact with each other and with their countermovements is crucial for understanding and mitigating polarization, especially on issues like race and gender since even basic demands for equality can be misconstrued and weaponized by extremist groups. Recent events, e.g. the Kyle Rittenhouse trial, demonstrate how right-wing groups can distort BLM's messages. Understanding how these distortions occur is essential for developing strategies to counter them. By analyzing social movement interactions, we can identify how disagreements escalate, how misinformation spreads, and how echo chambers amplify extreme views. Understanding these processes helps develop more effective strategies for de-escalating tensions and fostering dialogue. In a polarized environment, NGOs and activists must frame messages clearly and concisely, while being mindful of how their words might be misinterpreted. Dia-Pol’s algorithm helps identify the key themes and concerns in public discourse. This information, now available on the Dia-Pol platform, can be used to strategically craft messages. Also, political science, sociology, communication, and data science play important roles in understanding and mitigating polarization. To foster interdisciplinary collaboration and promote CSS, this project organized workshops and training. This project utilized open science practices (GitLab) for data analysis. It developed a novel theory to study variations within countermovements, examined the effect of BLM protests on state-level policing reforms and court decisions, and studied the media coverage of BLM protests.
