The potential of automated classification to categorise police force tweeting behaviours: leading the way to large scale analysis

Police activity on social media has emerged as a significant and expanding area of research. However, the existing body of research has predominantly adopted qualitative methods or focused on small-scale samples for quantitative analysis. This study presents a novel approach to analysing police social media behaviours, employing automated classification methods to generate a substantial sample of categorised police tweets. Encompassing over 40,000 tweets from five United Kingdom forces, collected over a three-year period, this dataset represents one of the largest evaluated samples in the domain of police social media research. A core objective of this research is to investigate the extent to which police tweeting behaviours align with three common categories identified in the literature: providing information, engagement, and intelligence gathering. To achieve this, a two-pronged methodology is employed, combining manual content analysis and an applied automated classification approach. This comprehensive method aims to create a sample of classified police tweets, effectively representing their diverse tweeting behaviours. The classicisation process involves the training and testing of three automated models, namely naïve Bayes, logistic regression, and XGBoost, evaluating the accuracy of their results to ensure a robust and reliable classification outcome. Furthermore, the resulting sample is subject to additional in-depth analyses. The exploration encompasses various facets of tweet content, style, overall usage, and adaptability across different police forces. Additionally, the research considers public interactions with the police tweets. These analyses are conducted for each force and class, thereby establishing connections between social media interactions and their potential impact on highlighted agendas.