Monitoring Social Media to Identify Environmental Crimes through NLP. A preliminary study
Authors: Raffaele Manna, Antonio Pascucci, Wanda Punzi Zarino, Vincenzo Simoniello, Johanna Monti (Università di Napoli L'Orientale, 2020)
Topic: Sentiment, Emotion, Irony, Hate

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.

Introduction
In the current era, social media represent the most common means of communication, especially thanks to the speed with which a post can go viral and reach in no time every corner of the globe. The speed with which information is produced creates an abundance of (linguistic) data, which can be monitored and handled with the use of hashtags (#). Hashtags are user-generated labels, which allow other users to track posts with a specific theme on Twitter. Moreover, social media such as Twitter can be powerful tools for identifying a variety of information sources related to people’s actions, decisions and opinions before, during and after broad scope events, such as environmental disasters like earthquakes, typhoons, volcanic eruptions, floods, droughts, forest fires, landslides AUTHOR. In light of the above, our aim is to monitor social media in order to detect environmental crimes. Our research is guided by the following question: can Natural Language Processing (NLP) represent a valuable ally to identify these kinds of crimes through the monitoring of social media? For this purpose, we compiled a corpus of tweets starting from a list of 41 terms related to environmental crimes, e.g. combustione illecita (illicit combustion), rifiuti radioattivi (radioactive waste), discarica abusiva (illegal dumping), and we used the Twitter API to download all the tweets (specifically 228,412) related to these terms introduced by hashtag. In this research, a special focus is dedicated to the tweets related to La terra dei fuochi (literally the Land of Fires) AUTHOR, a large area located between Naples and Caserta (in the South of Italy) victim of illegal toxic wastes dumped by organized crime for about fifty years and routinely burned to make space for new toxic wastes. In order to achieve our purpose, we trained different machine learning algorithms to classify report emergency text and user-generated reports. The paper is organized as follows: in Section~sec:related we discuss Related Work, in Section 2 we present the UNIOR Earth your Estate (UNIOR Eye) corpus. The case study is described in Section3 and Results are discussed in Section4. Conclusions are in Section~sec:conclusion along with directions for Future Work.

Conclusion
We presented a case study within the C4E project aimed at monitoring social media to provide support against environmental crimes. In particular, we described the UNIOR Eye corpus, in some sections still in progress, on which we tested four models with three different features extraction and construction techniques on a part of the corpus. We proposed two classifiers, namely SVM and MNB, with tf-idf features as the first experiment; then, SVM with C parameter tuning fed with sentence embeddings. These embeddings were built both using Italian pre-trained fastText model and using pre-trained DistilBert multilingual model. Our purpose was to classify alert tweets related to waste crimes vs no alert tweets. Future research will include the enlargement of the corpus, applications of NLP in the field of environmental protection as well as the analysis of contextual features related to environmental issues used as a medium to polarize public opinion (Karol, 2018).