Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models

Ridenhour, Michael; Bagavathi, Arunkumar; Raisi, Elaheh; Krishnan, Siddharth

doi:10.1007/978-3-030-61255-9_20

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…With the development of large pre-trained transformer models such as BERT and XLNET (Devlin et al, 2019;Yang et al, 2019), several studies have explored the use of general pre-trained transformers in offensive language identification (Liu et al, 2019;Bucur et al, 2021) as well retrained or fine-tuned models on offensive language corpora such as HateBERT (Caselli et al, 2020). While the vast majority of studies address offensive language identification using English data (Yao et al, 2019;Ridenhour et al, 2020), several recent studies have created new datasets for various languages and applied computational models to identify such content in Arabic (Mubarak et al, 2021), Dutch (Tulkens et al, 2016), French (Chiril et al, 2019), German (Wiegand et al, 2018), Greek (Pitenis et al, 2020), Hindi (Bohra et al, 2018), Italian (Poletto et al, 2017), Portuguese (Fortuna et al, 2019), Slovene (Fišer et al, 2017), Spanish (Plazadel Arco et al, 2021), and Turkish (C ¸öltekin, 2020. A recent trend is the use of pre-trained multilingual models such as XLM-R (Conneau et al, 2019) to leverage available English resources to make predictions in languages with less resources (Plaza-del Arco et al, 2021;Zampieri, 2020, 2021c,b;Sai and Sharma, 2021).…”

Section: Related Workmentioning

confidence: 99%

Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi

Gaikwad¹,

Ranasinghe²,

Zampieri³

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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Section: Related Workmentioning

confidence: 99%

Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi

Gaikwad¹,

Ranasinghe²,

Zampieri³

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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“…The clear majority of studies on this topic deal with English (Malmasi and Zampieri, 2017;Yao et al, 2019;Ridenhour et al, 2020) partially motivated by the availability English resources (e.g. corpora, lexicon, and pre-trained models).…”

Section: Related Workmentioning

confidence: 99%

Multilingual Offensive Language Identification with Cross-lingual Embeddings

Ranasinghe

Zampieri

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

100

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Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g. hate speech, cyberbulling, and cyberaggression). The clear majority of these studies deal with English partially because most annotated datasets available contain English data. In this paper, we take advantage of English data available by applying cross-lingual contextual word embeddings and transfer learning to make predictions in languages with less resources. We project predictions on comparable data in Bengali, Hindi, and Spanish and we report results of 0.8415 F1 macro for Bengali, 0.8568 F1 macro for Hindi, and 0.7513 F1 macro for Spanish. Finally, we show that our approach compares favorably to the best systems submitted to recent shared tasks on these three languages, confirming the robustness of cross-lingual contextual embeddings and transfer learning for this task.

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“…2017; Yao et al, 2019;Ridenhour et al, 2020;Rosenthal et al, 2020) due to the the wide availability of language resources such as corpora and pre-trained models. In recent years, several studies have been published on identifying offensive content in other languages such as Arabic (Mubarak et al, 2020), Dutch (Tulkens et al, 2016), French (Chiril et al, 2019), Greek (Pitenis et al, 2020), Italian (Poletto et al, 2017), Portuguese (Fortuna et al, 2019), and Turkish (Çöltekin, 2020).…”

Section: Postmentioning

confidence: 99%

MUDES: Multilingual Detection of Offensive Spans

Ranasinghe¹,

Zampieri²

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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The interest in offensive content identification in social media has grown substantially in recent years. Previous work has dealt mostly with post level annotations. However, identifying offensive spans is useful in many ways. To help coping with this important challenge, we present MUDES, a multilingual system to detect offensive spans in texts. MUDES features pre-trained models, a Python API for developers, and a user-friendly web-based interface. A detailed description of MUDES' components is presented in this paper.

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Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models

Cited by 12 publications

References 20 publications

Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi

Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi

Multilingual Offensive Language Identification with Cross-lingual Embeddings

MUDES: Multilingual Detection of Offensive Spans

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