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Automatic Intent-based Classification of Citizen-to-Government Tweets

Social networking technologies offer opportunities for governments to engage with citizens. However, the inability to filter relevant citizens' messages out of the vast amount of available social media content lessens their impact. In this paper, we propose a set of categories encapsulating the different citizens' intents when directing messages to public institutions, e.g., complaining, making requests, and proposing solutions to existing problems. We present a novel artificial intelligence approach, built upon natural language processing and machine learning algorithms, that enables the categorisation of citizens' messages into such intents automatically, and at scale. Through an empirical evaluation on a Twitter dataset, we show the effectiveness of our approach in terms of categorisation performance. We also discuss the value of the presented solution, as a novel tool for governments to achieve a more effective and informed communication with citizens.