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4 Discussions and Conclusions

This paper presents an analysis of policing engagement via social media. The aim of our work is to understand what are the characteristics of the content posted by police forces that attracts higher attention levels. By understanding these characteristics we could provide guidelines to the police forces of when and how they should write their posts; so that police messages reach to larger audiences and increase engagement within the communities.

To analyse this content we propose an approach that combines ML techniques with semantic technologies. While ML techniques help us to understand the more discriminative language and time features of those posts generating attention, semantic technologies help us to better understand and categorise the topics emerging from the content. Our analyses show that, writing longer tweets, with positive sentiment, and sending them out before 4pm, was found to increase the probability of attracting attention. Additionally, tweets about weather, roads and infrastructures, mentioning locations are also likely to attract attention.

It is important to highlight that this is a preliminary study and therefore have several limitations. First of all, only one social media platform (Twitter) has been considered for this study. Other social media platforms, such as Facebook, or even news media articles, should be taken into account to have a better understanding of the citizens' engagement towards social media policing content. Secondly, only one police Twitter account has been selected for the analysis performed in this work. Engagement dynamics may vary across the accounts of different police institutions [4]. Finally, only retweets have been considered as engagement indicator. Other indicators, in particular replies, should be also considered.

Additionally to expanding the number of platforms, accounts, and engagement indicators, our future work includes a deeper exploration of how semantics can be used to understand policing content. In particular we aim to explore the relations among tweets via the semantic entities and concepts they share. Our final goal is to be able to analyse conceptual evolution of the posts over time periods.

Acknowledgments. Thanks to all @dorsetpolice for their contribution to this work.


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