Detection of Hate Speech and Offensive Language in Twitter Data Using LSTM Model

Bisht, Akanksha; Singh, Annapurna; Bhadauria, H.S.; Virmani, Jitendra; Kriti, .

doi:10.1007/978-981-15-2740-1_17

Cited by 50 publications

(27 citation statements)

References 20 publications

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“…Agarwal, et al [13] proposed 4 different variants of RNN (GRNN, LRNN, GLRNN and UGRNN) to perform multimodal sentiment analysis including text, video, and audio. Bisht, et al [14] attained an accuracy of 86% by performing hate speech classification on a three-layer LSTM/Bi-LSTM model.…”

Section: Related Workmentioning

confidence: 99%

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Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network

Taradhita

Putra

2021

J. ICT Res. Appl.

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The rapid development of social media, added with the freedom of social media users to express their opinions, has influenced the spread of hate speech aimed at certain groups. Online based hate speech can be identified by the used of derogatory words in social media posts. Various studies on hate speech classification have been done, however, very few researches have been conducted on hate speech classification in the Indonesian language. This paper proposes a convolutional neural network method for classifying hate speech in tweets in the Indonesian language. Datasets for both the training and testing stages were collected from Twitter. The collected tweets were categorized into hate speech and non-hate speech. We used TF-IDF as the term weighting method for feature extraction. The most optimal training accuracy and validation accuracy gained were 90.85% and 88.34% at 45 epochs. For the testing stage, experiments were conducted with different amounts of testing data. The highest testing accuracy was 82.5%, achieved by the dataset with 50 tweets in each category.

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Section: Related Workmentioning

confidence: 99%

“…Recently, text classification models have been shifting towards deep learning models, such as CNN [4,11,12], RNN [13], GRU [10], LSTM and BiLSTM [14]. The Convolutional Neural Network (CNN) method is generally used in digital image processing but this does not rule out the possibility to use it in text processing [11].…”

Section: Introductionmentioning

confidence: 99%

Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network

Taradhita

Putra

2021

J. ICT Res. Appl.

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“…In other study, Bisht et al (2020) developed a LSTM model for the detection of hate speech and offensive language in Twitter data. The developed LSTM model include a single LSTM layer consisting set of input layers that gets the consecutive input.…”

Section: Related Work 21 Literature Reviewmentioning

confidence: 99%

“…Besides the social media platforms themselves, different studies have also made important attempts to identify abusive behaviors. For example, several studies has emerged to detect different form of abusive behaviors (Chen et al, 2012;Davidson et al, 2017;Nobata et al, 2016) such as hate speech (Siddiqua et al, 2019;Badjatiya et al, 2017;Djuric et al, 2015;Warner and Hirschberg, 2012;Waseem and Hovy, 2016), offensive language (Bisht et al, 2020;Mehdad and Tetreault, 2016;Xiang et al, 2012), sexist and racist language (Jha and Mamidi, 2017;Lozano et al, 2017), homophobia (Sanguinetti et al, 2018), cyberbullying (Balakrishnan et al, 2020;Chatzakou et al, 2017;Dinakar et al, 2011;Riadi, 2017), harassment and aggression (Bugueño and Mendoza, 2019;Espinoza and Weiss, 2019;Kim et al, 2020). Each form of these abusive behaviors in the different studies has its motive depending on the topics and individuals involved.…”

Section: Introductionmentioning

confidence: 99%

Hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks

Ayo

Folorunso

Ibharalu

et al. 2020

IJICC

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PurposeHate speech is an expression of intense hatred. Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors. Hate speech detection with social media data has witnessed special research attention in recent studies, hence, the need to design a generic metadata architecture and efficient feature extraction technique to enhance hate speech detection.Design/methodology/approachThis study proposes a hybrid embeddings enhanced with a topic inference method and an improved cuckoo search neural network for hate speech detection in Twitter data. The proposed method uses a hybrid embeddings technique that includes Term Frequency-Inverse Document Frequency (TF-IDF) for word-level feature extraction and Long Short Term Memory (LSTM) which is a variant of recurrent neural networks architecture for sentence-level feature extraction. The extracted features from the hybrid embeddings then serve as input into the improved cuckoo search neural network for the prediction of a tweet as hate speech, offensive language or neither.FindingsThe proposed method showed better results when tested on the collected Twitter datasets compared to other related methods. In order to validate the performances of the proposed method, t-test and post hoc multiple comparisons were used to compare the significance and means of the proposed method with other related methods for hate speech detection. Furthermore, Paired Sample t-Test was also conducted to validate the performances of the proposed method with other related methods.Research limitations/implicationsFinally, the evaluation results showed that the proposed method outperforms other related methods with mean F1-score of 91.3.Originality/valueThe main novelty of this study is the use of an automatic topic spotting measure based on naïve Bayes model to improve features representation.

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“…The detection of offensive language, cyberbullying and hate speech are tasks that are closely connected and often confused (Malmasi and Zampieri, 2018). Several machine learning models addressing hate speech or offensive language detection have been proposed in the last years; in particular deep learning models (Gambäck and Sikdar, 2017;Park and Fung, 2017;Badjatiya et al, 2017;Agrawal and Awekar, 2018;Bisht et al, 2020;Gertner et al, 2019;Pérez and Luque, 2019) have increased their popularity among researchers on this task. Despite the growing interest in the area, the models are usually trained and evaluated inside very specific English datasets, and their generalizability to other contexts or languages is still a challenge.…”

Section: Introductionmentioning

confidence: 99%

ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection

Pérez

Arango

Luque

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research 1 .

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Detection of Hate Speech and Offensive Language in Twitter Data Using LSTM Model

Cited by 50 publications

References 20 publications

Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network

Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network

Hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks

ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection

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