Raffaele Manna, A. Pascucci, Wanda Punzi Zarino, Vincenzo Simoniello, J. Monti
{"title":"Monitoring Social Media to Identify Environmental Crimes through NLP. A preliminary study","authors":"Raffaele Manna, A. Pascucci, Wanda Punzi Zarino, Vincenzo Simoniello, J. Monti","doi":"10.4000/books.aaccademia.8675","DOIUrl":null,"url":null,"abstract":"This paper presents the results of research carried out on the UNIOR Eye corpus, a corpus which has been built by down-loading tweets related to environmental crimes. The corpus is made up of 228,412 tweets organized into four different sub-sections, 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.","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4000/books.aaccademia.8675","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents the results of research carried out on the UNIOR Eye corpus, a corpus which has been built by down-loading tweets related to environmental crimes. The corpus is made up of 228,412 tweets organized into four different sub-sections, 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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