NetAC, An Automatic Classifier of Online Hate Speech Comments
| Autor: | Cristiana Araújo, Constança Elias, Pedro Pinheiro, Maria Madalena Teixeira de Araújo, Pedro Rangel Henriques, Jorge Gonçalves |
|---|---|
| Přispěvatelé: | Universidade do Minho |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
media_common.quotation_subject Data analysis 050801 communication & media studies 050109 social psychology Context (language use) computer.software_genre Newspaper Style (sociolinguistics) Social media 0508 media and communications Linguistic analysis support tools 0501 psychology and cognitive sciences Hate speech media_common Social network business.industry 05 social sciences 16. Peace & justice Categorization Aggressive language Artificial intelligence Prejudice business computer Classifier (UML) Natural language processing |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030726591 WorldCIST (3) |
| DOI: | 10.1007/978-3-030-72660-7_47 |
| Popis: | Nowadays in many linguistic and social areas researchers collect the reaction of people to someone’s statements (newspaper articles or social network posts) with the intention of analyzing the speech style. From that analysis different conclusions can be inferred giving rise to a large number of social impact attitudes. However it is not enough to create a huge corpus of texts. It is necessary to process the collected statements and comments and resort to appropriate tools to extract the relevant terms from the texts and analyze their occurrences. This paper is about a statistical framework, NetAC, built in the context of NetLang Project to study prejudice discourse aiming at individual or group discrimination. Given a categorization table the tools included in NetAC search for frequency of occurrence of the keywords in each category and, based on the greatest frequency, propose a classification for each comment and for the overall text. Besides the main classifier, other features will be presented. This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: PTDC/LLT-LIN/29304/2017. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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