This paper presents the results of research carried out on the UNIOR Eye corpus, a corpus which has been built by downloading tweets related to environmental crimes. The corpus is made up of 228,412 tweets organized into four different subsections, each one concerning a specific environmental crime. For the current study, we focused on the subsection of waste crimes, composed of 86,206 tweets which were tagged according to the two labels alert and no alert. The aim is to build a model able to detect which class a tweet belongs to.

Monitoring Social Media to Identify Environmental Crimes through NLP - A Preliminary Study

Raffaele Manna;Antonio Pascucci;Wanda Punzi Zarino;Vincenzo Simoniello;Johanna Monti
2020-01-01

Abstract

This paper presents the results of research carried out on the UNIOR Eye corpus, a corpus which has been built by downloading tweets related to environmental crimes. The corpus is made up of 228,412 tweets organized into four different subsections, each one concerning a specific environmental crime. For the current study, we focused on the subsection of waste crimes, composed of 86,206 tweets which were tagged according to the two labels alert and no alert. The aim is to build a model able to detect which class a tweet belongs to.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11574/195234
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