Unveiling protest-related social networks on Twitter: A comprehensive analysis of topic differences and temporal patterns within protest-related social networks in the Netherlands.

Abstract

The number of protests in the Netherlands has risen significantly in recent years, posing challenges for the Dutch police to ensure safety during these events. The increasing frequency of unannounced protests and the personnel shortages within the organization further exacerbate the complexities of ensuring public safety during such events. This necessitates research to better comprehend the motivations behind protests and effectively prepare for future events. This study addresses this need by utilizing open source Twitter data to perform social network analyses. The research is conducted in three phases: an exploratory phase, a method development phase, and an evaluation phase. In the exploratory phase, the possibilities of using social network analyses within the context of protest-related social networks are investigated. Building on the experiences from this phase, a method called PReSNA is developed for analyzing protest-related social networks. Following the design science research approach, the PReSNA method is then evaluated using a separate data set covering two large protests in March 2023. The findings reveal significant differences in hashtag usage prior to protests, providing valuable insights into the possibility to detect topics that can trigger future protests. Moreover, significant differences are observed in the characteristics of groups engaged in protest-related Twitter conversations, shedding light on the factors driving activity within these groups. The results of this study demonstrate the value of open source data in gaining insights into protest behavior. Furthermore, these findings highlight the potential for future research within this area. Overall, this research contributes to the understanding of protest-related social networks and offers a methodological framework for analyzing such networks. By leveraging open source data and social network analysis techniques, this study provides valuable insights for researchers and practitioners in the field of protest management.