Ceasing hate with MoH: Hate Speech Detection in Hindi–English code-switched language

Sharma, Arushi; Kabra, Anubha; Jain, Minni

doi:10.1016/j.ipm.2021.102760

Cited by 49 publications

(15 citation statements)

References 31 publications

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“…In the year 2022, Arushi S., et al [21] proposed a study that focuses on identifying hate speech in Hindi-English Code-Switched languages. The authors' research entails experimenting with transformation strategies to obtain an accurate text representation.…”

Section: B Work Done On the Hate Speech Classification In Codementioning

confidence: 99%

Detection of Virulent Messages Written in Code-Mixed Hindi-English Language

Ramalingam,

Agarwal,

Singh

2023

Recent Trends in Data Science and Its Applications

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Social media as a medium of communication has opened countless possibilities for individuals around the globe to express their opinions on various topics ranging from politics to entertainment, thus reducing the gap in the accessibility of information. However, this has also led to social media becoming a hub for spreading hateful messages. These virulent messages spread online result in the rise of cases of cyberbullying, leaving an enduring impact upon the victims. Manually classifying and reporting these messages is a challenging task and has therefore created an interest in the research community to incorporate machine learning-based techniques to ease up the hand-operated process. The study's goal is to play a small auxiliary role in the prevention and ultimate eradication of cyberbullying. The proposed work in this research primarily focuses on the users from the Indian subcontinent by experimenting with Code-Mixed Hindi-English language. This research achieves this by performing the task of developing a Code-Mixed Hindi-English hate speech dataset containing 4014 tweets, out of which 2000 tweets contain hate, using the publically available tweets from the Twitter platform and classified on the basis of a set of guidelines. These languages are employed in the training of the models to create a more reliable hate speech prediction system for the targeted audience. The use of pre-trained multilingual models are explored by fine-tuning on the collected dataset. This research discovered an accuracy of 80.6 on the fine-tuned BERT Multilingual Base model when trained and tested on the combination of all the three languages and an accuracy of 82.2 on theXLM-RoBERTa model upon the same dataset. The goal of the study is to play a small auxiliary role in the prevention and ultimate eradication of cyberbullying.

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Section: B Work Done On the Hate Speech Classification In Codementioning

confidence: 99%

Detection of Virulent Messages Written in Code-Mixed Hindi-English Language

Ramalingam,

Agarwal,

Singh

2023

Recent Trends in Data Science and Its Applications

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“…Changing the order of the words in a sentence might change the meaning of the sentence; therefore, the text augmentation is slightly different from other augmentation techniques. As the Twitter data [19,22] that we have considered from Kaggle was not sufficient, we have increased the dataset size using nlpaug tool. This nlpaug [23] method uses word-embedding techniques and various augmenter strategies such as insertion and substitutions to augment the data on a character level, word level and sentence level.…”

Section: Dataset Preparation and Preprocessingmentioning

confidence: 99%

Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

Turki

Roy

2022

Applied Sciences

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A plethora of negative behavioural activities have recently been found in social media. Incidents such as trolling and hate speech on social media, especially on Twitter, have grown considerably. Therefore, detection of hate speech on Twitter has become an area of interest among many researchers. In this paper, we present a computational framework to (1) examine out the computational challenges behind hate speech detection and (2) generate high performance results. First, we extract features from Twitter data by utilizing a count vectorizer technique. Then, we provide the labeled dataset of constructed features to adopted ensemble methods, including Bagging, AdaBoost, and Random Forest. After training, we classify new tweet examples into one of the two categories, hate speech or non-hate speech. Experimental results show (1) that Random Forest has surpassed other methods by generating 95% using accuracy performance results and (2) word cloud displays the most prominent tweets that are responsible for hateful sentiments.

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“…The BERT model is an unsupervised deep bidirectional neural network that implements bidirectional transformer architecture. A BERT-based transfer learning approach has started to be used frequently in hate classification studies, as it leads to increased classification performance and reduced training time [78]. The transfer learning approach also provides ef-fective learning from limited labeled data with a pretrained model.…”

Section: M-bert Modelmentioning

confidence: 99%

Homophobic and Hate Speech Detection Using Multilingual-BERT Model on Turkish Social Media

Karayiğit

Akdağlı

Acı

2022

ITC

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Homophobic expressions are a form of insulting the sexual orientation or personality of people. Severe psychological traumas may occur in people who are exposed to this type of communication. It is important to develop automatic classification systems based on language models to examine social media content and distinguish homophobic discourse. This study aims to present a pre-trained Multilingual Bidirectional Encoder Representations from Transformers (M-BERT) model that can successfully detect whether Turkish comments on social media contain homophobic or related hate comments (i.e., sexist, severe humiliation, and defecation expressions). Comments in the Homophobic-Abusive Turkish Comments (HATC) dataset were collected from Instagram to train the detection models. The HATC dataset was manually labeled at the sentence level and combined with the Abusive Turkish Comments (ATC) dataset that has developed in our previous study. The HATC dataset has been balanced using the resampling method and two forms of the dataset (i.e., resHATC and original HATC) were used in the experiments. Afterward, the M-BERT model was compared with DL-based models (i.e., Long-Short Term Memory, Bidirectional Long-Short Term Memory (BiLSTM), Gated Recurrent Unit), Traditional Machine Learning (TML) classifiers (i.e., Support Vector Machine, Naive Bayes, Random Forest) and Ensemble Classifiers (i.e., Adaptive Boosting, eXtreme Gradient Boosting, Gradient Boosting) for the best model selection. The performance of the detection models was evaluated using F1-score, precision, and recall performance metrics. Results showed the best performance (homophobic F1-score: 82.64%, hateful F1-score: 91.75%, neutral F1-score: 96.08%, average F1-score: 90.15%) was achieved with the M-BERT model on the HATC dataset. The M-BERT detection model can increase the effectiveness of filters in detecting Turkish homophobic and related hate speech in social networks. It can be used to detect homophobic and related hate speech for different languages since the M-BERT model has multilingual pre-trained data.

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Ceasing hate with MoH: Hate Speech Detection in Hindi–English code-switched language

Cited by 49 publications

References 31 publications

Detection of Virulent Messages Written in Code-Mixed Hindi-English Language

Detection of Virulent Messages Written in Code-Mixed Hindi-English Language

Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

Homophobic and Hate Speech Detection Using Multilingual-BERT Model on Turkish Social Media

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