Hate Speech Detection Using Static BERT Embeddings

Rajput, Gaurav Kumar; Punn, Narinder Singh; Sonbhadra, Sanjay Kumar; Agarwal, Sonali

doi:10.1007/978-3-030-93620-4_6

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…With the advent of Transformers (Vaswani et al, 2017) structure and transfer learning in AI, BERT, which stands for Bidirectional Encoder Representations from Transformers (Devlin et al, 2018), becomes one of the most popular models for hate speech detection, and an increasing number of studies have shown the dominant performance of BERT in terms of detection tasks. (Liu et al, 2019;Vishwamitra et al, 2020;Li et al, 2021;Rajput et al, 2021;Caselli et al, 2020;Saleh et al, 2021;Mohtaj et al, 2022), or in general, text classification task. In particular, as the number of hate crimes against Asians skyrocketed during the COVID-19 pandemic, more studies of hate speech towards Asians and its detection appeared (Vidgen et al, 2020;Alshalan et al, 2020;Tahmasbi et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Speech Detection Task Against Asian Hate: BERT the Central, While Data-Centric Studies the Crucial

Lian¹

2022

Preprint

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With the epidemic continuing, hatred against Asians is intensifying in countries outside Asia, especially among the Chinese. Thus, there is an urgent need to detect and prevent hate speech towards Asians effectively. In this work, we first create COVID-HATE-2022, an annotated dataset that is an extension of the anti-Asian hate speech dataset on Twitter, including 2,035 annotated tweets fetched in early February 2022, which are labeled based on specific criteria, and we present the comprehensive collection of scenarios of hate and nonhate tweets in the dataset. Second, we finetune the BERT models based on the relevant datasets, and demonstrate strategies including 1) cleaning the hashtags, usernames being @, URLs, and emojis before the fine-tuning process, and 2) training with the data while validating with the "clean" data (and the opposite) are not effective for improving performance. Third, we investigate the performance of advanced fine-tuning strategies with 1) modelcentric approaches, such as discriminative finetuning, gradual unfreezing, and warmup steps, and 2) data-centric approaches, which incorporate data trimming and data augmenting, and show that both strategies generally improve the performance, while data-centric ones outperform the others, which demonstrate the feasibility and effectiveness of the data-centric approaches. 2 The hate crime data for 2020 is available on the FBI's Crime Data Explorer: https: //crime-data-explorer.app.cloud.gov 3 See https://www.who.int/docs/ default-source/coronaviruse/ covid19-stigma-guide.pdf

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Section: Related Workmentioning

confidence: 99%

Speech Detection Task Against Asian Hate: BERT the Central, While Data-Centric Studies the Crucial

Lian¹

2022

Preprint

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“…Text detection is one of the big challenges of NLP which still has many limitations to work with, especially for the Arabic language when compared to English. There are many types of text detection such as prediction of human behavior [ 10 ], hate speech detection [ 11 ], exploring halal tourism [ 12 ], gender detection [ 13 ], misogyny detection [ 14 ], sarcasm detection [ 15 ], detection and classification of psychopathic personality [ 16 ], fake news detection [ 17 ], and detection of dialectal in the Arabic language [ 18 ]. This study aims to design a model for automatically detecting misogyny and sarcasm using machine learning (ML) and deep learning (DL) methods with different benchmark datasets [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts

Muaad

Hanumanthappa

Benifa

et al. 2022

Computational Intelligence and Neuroscience

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Social media networking is a prominent topic in real life, particularly at the current moment. The impact of comments has been investigated in several studies. Twitter, Facebook, and Instagram are just a few of the social media networks that are used to broadcast different news worldwide. In this paper, a comprehensive AI-based study is presented to automatically detect the Arabic text misogyny and sarcasm in binary and multiclass scenarios. The key of the proposed AI approach is to distinguish various topics of misogyny and sarcasm from Arabic tweets in social media networks. A comprehensive study is achieved for detecting both misogyny and sarcasm via adopting seven state-of-the-art NLP classifiers: ARABERT, PAC, LRC, RFC, LSVC, DTC, and KNNC. To fine tune, validate, and evaluate all of these techniques, two Arabic tweets datasets (i.e., misogyny and Abu Farah datasets) are used. For the experimental study, two scenarios are proposed for each case study (misogyny or sarcasm): binary and multiclass problems. For misogyny detection, the best accuracy is achieved using the AraBERT classifier with 91.0% for binary classification scenario and 89.0% for the multiclass scenario. For sarcasm detection, the best accuracy is achieved using the AraBERT as well with 88% for binary classification scenario and 77.0% for the multiclass scenario. The proposed method appears to be effective in detecting misogyny and sarcasm in social media platforms with suggesting AraBERT as a superior state-of-the-art deep learning classifier.

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“…The classification architecture presented a transformer-based ensembled architecture consisting of a BERT pre-trained model and a language identification model. Further (Rajput et al, 2021), presented a simple classification model which initially created the static BERT (Devlin et al, 2018) embeddings matrix of the data to extract the contextual information of the data and then experimented with various Deep Neural Networks (DNN) to train a binary classifier. Motivated from the last year's best performing submission in LT-EDI-2021 using the transformers, we ensemble various transformers and utilize the predicted labels with voting.…”

Section: Introduction and Related Workmentioning

confidence: 99%

IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and Inclusion

Singh¹,

Motlíček²

2022

Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

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Social media platforms have been provoking masses of people. The individual comments affect a prevalent way of thinking by moving away from preoccupation with discrimination, loneliness, or influence in building confidence, support, and good qualities. This paper aims to identify hope in these social media posts. Hope significantly impacts the well-being of people, as suggested by health professionals. It reflects the belief to achieve an objective, discovers a new path, or become motivated to formulate pathways. In this paper we classify given a social media post, hope speech or not hope speech, using ensembled voting of BERT, ERNIE 2.0 and RoBERTa for English language with 0.54 macro F1-score (2 st rank). For non-English languages Malayalam, Spanish and Tamil we utilized XLM RoBERTA with 0.50, 0.81, 0.3 macro F1 score (1 st , 1 st ,3 rd rank) respectively. For Kannada, we use Multilingual BERT with 0.32 F1 score(5 th )position. We release our code-base here https: //github.com/Muskaan-Singh/ Hate-Speech-detection.git

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Hate Speech Detection Using Static BERT Embeddings

Cited by 13 publications

References 15 publications

Speech Detection Task Against Asian Hate: BERT the Central, While Data-Centric Studies the Crucial

Speech Detection Task Against Asian Hate: BERT the Central, While Data-Centric Studies the Crucial

Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts

IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and Inclusion

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