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
Martínez-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:20.500.12585/10419
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/10419
http://ceur-ws.org/Vol-2936/paper-188.pdf
- Palabra clave:
- Phonetic syllable
Phonetic feature
Feature extraction
Hate speech spreader
LEMB
- 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 Phonetic syllable Phonetic feature Feature extraction Hate speech spreader LEMB |
| 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 Martínez-Santos, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Puertas, Edwin Martínez-Santos, Juan Carlos |
| dc.subject.keywords.spa.fl_str_mv |
Phonetic syllable Phonetic feature Feature extraction Hate speech spreader |
| topic |
Phonetic syllable Phonetic feature Feature extraction Hate speech spreader LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| 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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2021-09-24 |
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2022-01-28T20:01:16Z |
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2022-01-28T20:01:16Z |
| dc.date.submitted.none.fl_str_mv |
2022-01-27 |
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info:eu-repo/semantics/article |
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Puertas, Edwin & Martinez Santos, Juan Carlos. (2021). Phonetic Detection for Hate Speech Spreaders on Twitter Notebook for PAN at CLEF 2021. |
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https://hdl.handle.net/20.500.12585/10419 |
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http://ceur-ws.org/Vol-2936/paper-188.pdf |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Puertas, Edwin & Martinez Santos, Juan Carlos. (2021). Phonetic Detection for Hate Speech Spreaders on Twitter Notebook for PAN at CLEF 2021. Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/10419 http://ceur-ws.org/Vol-2936/paper-188.pdf |
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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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Colombia |
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Cartagena de Indias |
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CEUR Workshop Proceedings - vol. 2936 |
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Universidad Tecnológica de Bolívar |
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Puertas, Edwin5a1b1566-e112-43dc-8ac7-310ea9af8f05Martínez-Santos, Juan Carlos5c958644-c78d-401d-8ba9-bbd39fe77318Colombia2022-01-28T20:01:16Z2022-01-28T20:01:16Z2021-09-242022-01-27Puertas, Edwin & Martinez Santos, Juan Carlos. (2021). Phonetic Detection for Hate Speech Spreaders on Twitter Notebook for PAN at CLEF 2021.https://hdl.handle.net/20.500.12585/10419http://ceur-ws.org/Vol-2936/paper-188.pdfUniversidad 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 Twitter.8 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. 2936Phonetic Detection for Hate Speech Spreaders on Twitterinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Phonetic syllablePhonetic featureFeature extractionHate speech spreaderLEMBCartagena de IndiasA. Schmidt, M. Wiegand, A survey on hate speech detection using natural language processing, in: Proceedings of the fifth international workshop on natural language processing for social media, 2017, pp. 1–10.A. Ferrari, A. Consoli, Building accurate hav exploiting user profiling and sentiment analysis, ArXiv abs/1609.07302 (2016) 1–595.] M. Fatima, K. Hasan, S. Anwar, R. M. A. Nawab, Multilingual author profiling on facebook, Inf. Process. Manage. 53 (2017) 886–904. URL: https://doi.org/10.1016/j.ipm.2017.03.005. doi:10.1016/j.ipm.2017.03.005.E. Puertas, J. A. Alvarado, Modelo que mejore la detección de polaridades hechas con word embedding con la ayuda de predictores fonéticos y el apoyo de elementos emocionales., in: ENEDI-2020, ENEDI-2020, https://www.acofi.edu.co/eiei2020/wpcontent/uploads/2020/10/Memorias-ENEDI . . . , 2020, pp. 95–104.F. Rangel, P. Rosso, G. L. D. L. P. Sarracén, E. Fersini, B. Chulvi, Profiling Hate Speech Spreaders on Twitter Task at PAN 2021, in: CLEF 2021 Labs and Workshops, Notebook Papers, CEUR-WS.org, 2021, pp. 1–7J. Bevendorff, B. Chulvi, G. L. D. L. P. Sarracén, M. Kestemont, E. Manjavacas, I. Markov, M. Mayerl, M. Potthast, F. Rangel, P. Rosso, E. Stamatatos, B. Stein, M. Wiegmann, M. Wolska, , E. Zangerle, Overview of PAN 2021: Authorship Verification,Profiling Hate Speech Spreaders on Twitter,and Style Change Detection, in: 12th International Conference of the CLEF Association (CLEF 2021), Springer, 2021, pp. 1–7M. Potthast, T. Gollub, M. Wiegmann, B. Stein, TIRA Integrated Research Architecture, in: N. Ferro, C. Peters (Eds.), Information Retrieval Evaluation in a Changing World, The Information Retrieval Series, Springer, Berlin Heidelberg New York, 2019, pp. 1–7. doi:10.1007/978-3-030-22948-1\_5.P. Fortuna, S. Nunes, A survey on automatic detection of hate speech in text, ACM Computing Surveys (CSUR) 51 (2018) 1–30.S. Caetano da Silva, T. Castro Ferreira, R. M. Silva Ramos, I. Paraboni, Data driven and psycholinguistics motivated approaches to hate speech detection, Computación y Sistemas 24 (2020)F. Rangel, P. Rosso, Overview of the 7th author profiling task at pan 2019: bots and gender profiling in twitter, in: Working Notes Papers of the CLEF 2019 Evaluation Labs Volume 2380 of CEUR Workshop, 2019, pp. 1–7.F. Rangel, A. Giachanou, B. Ghanem, P. Rosso, Overview of the 8th author profiling task at pan 2020: Profiling fake news spreaders on twitter, in: CLEF, 2020, pp. 1–7.V. Basile, C. Bosco, E. Fersini, N. Debora, V. Patti, F. M. R. Pardo, P. Rosso, M. Sanguinetti, et al., Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter, in: 13th International Workshop on Semantic Evaluation, Association for Computational Linguistics, 2019, pp. 54–63.E. Cambria, S. Poria, D. Hazarika, K. Kwok, Senticnet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings, in: Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018, pp. 1–8.E. Puertas, Embedding of phonetic syllables in english, 2020. URL: https://doi.org/10.5281/ zenodo.4299251. doi:10.5281/zenodo.4299251.E. Puertas, Embedding of phonetic syllables in spanish, 2020. URL: https://doi.org/10.5281/ zenodo.4299242. doi:10.5281/zenodo.4299242.M. A. M. Antonín, M. T. Delor, L. Màrquez, M. Bertran, Anotación semiautomática con papeles temáticos de los corpus cess-ece, Procesamiento del Lenguaje Natural (2007) 67–76C. Macleod, N. Ide, R. Grishman, The american national corpus: A standardized resource for american english., in: LREC, 2000, pp. 1–7D. R. Mortensen, S. Dalmia, P. Littell, Epitran: Precision G2P for many languages, in: 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.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), European Language Resources Association (ELRA), Paris, France, 2018, pp. 1–4628E. Puertas, L. G. Moreno-Sandoval, F. M. Plaza-del Arco, J. A. Alvarado-Valencia, A. Pomares-Quimbaya, L. Alfonso, Bots and gender profiling on twitter using sociolinguistic features, CLEF (Working Notes) (2019) 1–8.F. RANGEL, B. CHULVI, G. L. D. L. PEÑA, E. FERSINI, P. ROSSO, Profiling hate speech spreaders on twitter, 2021. 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