In the era of social media, the huge availability of digital data (e.g. posts sent through social networks or unstructured data scraped from websites) allows to develop new types of research in a wide range of fields. These types of data are characterized by some advantages such as reduced collection costs, short retrieval times and production of almost real-time outputs. Nevertheless, their collection and analysis can be challenging. For example, particular approaches are required for the selection of posts related to specific topics; moreover, retrieving the information we are interested in inside Twitter posts can be a difficult task.
The main aim of this paper is to propose an unsupervised dictionary-based method to filter tweets related to a specific topic, i.e. environment. We start from the tweets sent by a selection of Official Social Accounts clearly linked with the subject of interest. Then, a list of keywords is identified in order to set a topic-oriented dictionary. We test the performance of our method by applying the dictionary to more than 54 million geolocated tweets posted in Great Britain between January and May 2019.