Phonetic detection for Hate Speech Spreaders on Twitter
Nowadays, hate messages have become the object of study on social media. Efficient and effective detection of hate profiles requires various scientific disciplines, such as computational linguistics and sociology. Here, we illustrate how we used lexical and phonetic features to determine if the auth...
- Autores:
-
Puertas, Edwin
Martinez-Santos, Juan Carlos
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12373
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12373
- Palabra clave:
- Feature extraction
Hate speech spreader
Phonetic feature
Phonetic syllable
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Phonetic detection for Hate Speech Spreaders on Twitter |
| title |
Phonetic detection for Hate Speech Spreaders on Twitter |
| spellingShingle |
Phonetic detection for Hate Speech Spreaders on Twitter Feature extraction Hate speech spreader Phonetic feature Phonetic syllable |
| title_short |
Phonetic detection for Hate Speech Spreaders on Twitter |
| title_full |
Phonetic detection for Hate Speech Spreaders on Twitter |
| title_fullStr |
Phonetic detection for Hate Speech Spreaders on Twitter |
| title_full_unstemmed |
Phonetic detection for Hate Speech Spreaders on Twitter |
| title_sort |
Phonetic detection for Hate Speech Spreaders on Twitter |
| dc.creator.fl_str_mv |
Puertas, Edwin Martinez-Santos, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Puertas, Edwin Martinez-Santos, Juan Carlos |
| dc.subject.keywords.spa.fl_str_mv |
Feature extraction Hate speech spreader Phonetic feature Phonetic syllable |
| topic |
Feature extraction Hate speech spreader Phonetic feature Phonetic syllable |
| description |
Nowadays, hate messages have become the object of study on social media. Efficient and effective detection of hate profiles requires various scientific disciplines, such as computational linguistics and sociology. Here, we illustrate how we used lexical and phonetic features to determine if the author spreads hate speech. This article presents a novel strategy for the characterization of the Twitter profile based on the generation of lexical and phonetic user features that serve as input to a set of classifiers. The results are part of our participation in the PAN 2021 in the CLEF in the task of Profiling Hate Speech Spreaders on Twitter |
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2021 |
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2021-09 |
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2023-07-21T20:47:24Z |
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2023-07-21T20:47:24Z |
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2023-07 |
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Puertas, E., Martinez-Santos, J.C. Phonetic detection for Hate Speech Spreaders on Twitter (2021) CEUR Workshop Proceedings, 2936, pp. 2118-2125. |
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16130073 |
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https://hdl.handle.net/20.500.12585/12373 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Puertas, E., Martinez-Santos, J.C. Phonetic detection for Hate Speech Spreaders on Twitter (2021) CEUR Workshop Proceedings, 2936, pp. 2118-2125. 16130073 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12373 |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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openAccess |
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8 páginas |
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application/pdf |
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Cartagena de Indias |
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CEUR Workshop Proceedings - vol. 2936 (2021) |
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Universidad Tecnológica de Bolívar |
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Puertas, Edwin5a1b1566-e112-43dc-8ac7-310ea9af8f05Martinez-Santos, Juan Carlos5c958644-c78d-401d-8ba9-bbd39fe773182023-07-21T20:47:24Z2023-07-21T20:47:24Z2021-092023-07Puertas, E., Martinez-Santos, J.C. Phonetic detection for Hate Speech Spreaders on Twitter (2021) CEUR Workshop Proceedings, 2936, pp. 2118-2125.16130073https://hdl.handle.net/20.500.12585/12373Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarNowadays, hate messages have become the object of study on social media. Efficient and effective detection of hate profiles requires various scientific disciplines, such as computational linguistics and sociology. Here, we illustrate how we used lexical and phonetic features to determine if the author spreads hate speech. This article presents a novel strategy for the characterization of the Twitter profile based on the generation of lexical and phonetic user features that serve as input to a set of classifiers. The results are part of our participation in the PAN 2021 in the CLEF in the task of Profiling Hate Speech Spreaders on Twitter8 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2CEUR Workshop Proceedings - vol. 2936 (2021)Phonetic detection for Hate Speech Spreaders on Twitterinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Feature extractionHate speech spreaderPhonetic featurePhonetic syllableCartagena de IndiasSchmidt, A., Wiegand, M. A Survey on Hate Speech Detection using Natural Language Processing (2017) SocialNLP 2017 - 5th International Workshop on Natural Language Processing for Social Media, Proceedings of the Workshop AFNLP SIG SocialNLP, pp. 1-10. Cited 698 times. https://aclanthology.org/volumes/W17-11/ ISBN: 978-194562642-5Ferrari, A., Consoli, A. (2016) Building accurate hav exploiting user profiling and sentiment analysis, pp. 1-595. Cited 2 times. ArXiv abs/1609.07302Fatima, M., Hasan, K., Anwar, S., Nawab, R.M.A. Multilingual author profiling on Facebook (2017) Information Processing and Management, 53 (4), pp. 886-904. Cited 59 times. doi: 10.1016/j.ipm.2017.03.005Puertas, E., Alvarado, J. A. Modelo que mejore la detección de polaridades hechas con word embedding con la ayuda de predictores fonéticos y el apoyo de elementos emocionales (2020) ENEDI-2020, pp. 95-104. ENEDI-2020 https://www.acofi.edu.co/eiei2020/wpcontent/uploads/2020/10/Memorias-ENEDIRangel, F., Rosso, P., Sarracén, G. L. D. L. P., Fersini, E., Chulvi, B. Profiling Hate Speech Spreaders on Twitter Task at PAN 2021 (2021) CLEF 2021 Labs and Workshops, pp. 1-7. Cited 21 times. Notebook Papers, CEUR-WS.orgBevendorff, J., Chulvi, B., Fersini, E., Heini, A., Kestemont, M., Kredens, K., Mayerl, M., (...), Zangerle, E. Overview of PAN 2022: Authorship Verification, Profiling Irony and Stereotype Spreaders, Style Change Detection, and Trigger Detection: Extended Abstract (2022) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13186 LNCS, pp. 331-338. Cited 50 times. https://www.springer.com/series/558 ISBN: 978-303099738-0 doi: 10.1007/978-3-030-99739-7_42Potthast, M., Gollub, T., Wiegmann, M., Stein, B. TIRA Integrated Research Architecture (2019) Information Retrieval Evaluation in a Changing World, The Information Retrieval Series, pp. 1-7. Cited 250 times. N. Ferro, C. Peters (Eds), Springer, Berlin Heidelberg New YorkFortuna, P., Nunes, S. A survey on automatic detection of hate speech in text (2018) ACM Computing Surveys, 51 (4), art. no. 3232676. Cited 527 times. http://dl.acm.org/citation.cfm?id=J204 doi: 10.1145/3232676Da Silva, S.C., Ferreira, T.C., Ramos, R.M.S., Paraboni, I. Data-driven and psycholinguistics-motivated approaches to hate speech detection (2020) Computacion y Sistemas, 24 (3), pp. 1179-1188. Cited 5 times. https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3478 doi: 10.13053/CYS-24-3-3478Rangel, F., Rosso, P. Overview of the 7th author profiling task at Pan 2019: Bots and gender profiling in twitter (2019) CEUR Workshop Proceedings, 2380. Cited 89 times. http://ceur-ws.org/Rangel, F., Giachanou, A., Ghanem, B., Rosso, P. Overview of the 8th author profiling task at pan 2020: Profiling fake news spreaders on twitter (2020) CLEF, pp. 1-7. Cited 32 times.Basile, V., Bosco, C., Fersini, E., Nozza, D., Patti, V., Rangel, F., Rosso, P., (...), Sanguinetti, M. SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter (2019) NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop, pp. 54-63. Cited 515 times. https://aclanthology.org/events/semeval-2019/#s19-2 ISBN: 978-195073706-2Cambria, E., Poria, S., Hazarika, D., Kwok, K. SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings (2018) 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 1795-1802. Cited 294 times. https://aaai.org/Library/AAAI/aaai18contents.php ISBN: 978-157735800-8Puertas, E. (2020) Embedding of phonetic syllables in english https://doi.org/10.5281/zenodo.4299251Puertas, E. (2020) Embedding of phonetic syllables in spanish https://doi.org/10.5281/zenodo.4299242Antonín, M. A. M., Delor, M. T., Màrquez, L., Bertran, M. Anotación semiautomática con papeles temáticos de los corpus cess-ece (2007) Procesamiento del Lenguaje Natural, pp. 67-76. Cited 5 times.Macleod, C., Ide, N., Grishman, R. The American National Corpus: A standardized resource for American English (2000) 2nd International Conference on Language Resources and Evaluation, LREC 2000. Cited 13 times. http://www.lrec-conf.org/proceedings/lrec2000/html/paper/p_all.htmMortensen, D. R., Dalmia, S., Littell, P. Epitran: Precision G2P for many languages (2018) Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), European Language Resources Association (ELRA), pp. 1-4628. N. C. C. chair), K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis, T. Tokunaga (Eds), Paris, FrancePuertas, E., Moreno-Sandoval, L. G., Plaza-del Arco, F. M., Alvarado-Valencia, J. A., Pomares-Quimbaya, A., Alfonso, L. Bots and gender profiling on twitter using sociolinguistic features (2019) CLEF (Working Notes), pp. 1-8.RANGEL, F., CHULVI, B., PEÑA, G. L. D. L., FERSINI, E., ROSSO, P. (2021) Profiling hate speech spreaders on twitter. 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