Rangel, F.; Peña-Sarracén, GLDL.; Chulvi-Ferriols, MA.; Fersini, E.; Rosso, P. (2021). Profiling hate speech spreaders on twitter task at PAN 2021. CEUR. 1772-1789. http://hdl.handle.net/10251/190663
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/190663
Títol:
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Profiling hate speech spreaders on twitter task at PAN 2021
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Autor:
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Rangel, Francisco
Peña-Sarracén, Gretel Liz de la
Chulvi-Ferriols, María Alberta
Fersini, Elisabetta
Rosso, Paolo
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Entitat UPV:
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Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
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Data de difusió:
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Resum:
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[EN] This overview presents the Author Profiling shared task at PAN 2021. The focus of this year¿s task is on determining whether or not the author of a Twitter feed is keen to spread hate speech. The main aim is to show ...[+]
[EN] This overview presents the Author Profiling shared task at PAN 2021. The focus of this year¿s task is on determining whether or not the author of a Twitter feed is keen to spread hate speech. The main aim is to show the feasibility of automatically identifying potential hate speech spreaders on Twitter. For this purpose a corpus with Twitter data has been provided, covering the English and Spanish languages. Altogether, the approaches of 66 participants have been evaluated.
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Paraules clau:
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Hate speech
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Hate speech spreaders
,
Author profiling
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Natural language processing
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Artificial intelligence
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Drets d'ús:
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Reconocimiento (by)
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Font:
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Proceedings of the Working Notes of CLEF 2021, Conference and Labs of the Evaluation Forum, Bucharest, Romania, September 21st to 24th, 2021. (issn:
1613-0073
)
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Editorial:
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CEUR
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Versió de l'editor:
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https://ceur-ws.org/Vol-2936/
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Títol del congrés:
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12th Conference and Labs of the Evaluation Forum (CLEF 2021). Working Notes
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lloc del congrés:
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Online
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Data del congrés:
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Septiembre 21-24,2021
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Codi del Projecte:
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-096212-B-C31/ES/DESINFORMACION Y AGRESIVIDAD EN SOCIAL MEDIA: AGREGANDO INFORMACION Y ANALIZANDO EL LENGUAJE/
info:eu-repo/grantAgreement///PROMETEO%2F2019%2F121//DEEP LEARNING FOR ADAPTATIVE AND MULTIMODAL INTERACTION IN PATTERN RECOGNITION/
info:eu-repo/grantAgreement/COST//17124/
info:eu-repo/grantAgreement/MICINN//IDI-20210776/
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agraïments:
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First of all, we thank the participants: again 66 this year, as the previous year on Profiling Fake
News Spreaders! We have to thank also Martin Potthast, Matti Wiegmann, Nikolay Kolyada, and
Magdalena Anna Wolska for their ...[+]
First of all, we thank the participants: again 66 this year, as the previous year on Profiling Fake
News Spreaders! We have to thank also Martin Potthast, Matti Wiegmann, Nikolay Kolyada, and
Magdalena Anna Wolska for their technical support with the TIRA platform. We thank Symanto
for sponsoring again the award for the best performing system at the author profiling shared
task. The work of Francisco Rangel was partially funded by the Centre for the Development
of Industrial Technology (CDTI) of the Spanish Ministry of Science and Innovation under the
research project IDI-20210776 on Proactive Profiling of Hate Speech Spreaders - PROHATER
(Perfilador Proactivo de Difusores de Mensajes de Odio). The work of the researchers from
Universitat Politècnica de València was partially funded by the Spanish MICINN under the
project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE
news and HATE speech (PGC2018-096212-B-C31), and by the Generalitat Valenciana under
the project DeepPattern (PROMETEO/2019/121). This article is also based upon work from the
Dig-ForAsp COST Action 17124 on Digital Forensics: evidence analysis via intelligent systems
and practices, supported by European Cooperation in Science and Technology.
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Tipus:
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Comunicación en congreso
Artículo
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