Detecting and Monitoring Hate Speech in Twitter

Pereira-Kohatsu, Juan Carlos; Quijano-Sánchez, Lara; Liberatore, Federico; Camacho‐Collados, Miguel

doi:10.3390/s19214654

Cited by 136 publications

(84 citation statements)

References 69 publications

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“…This also means that our discussion of other problems regarding hate speech detection are limited, and the purpose of this discussion is mostly to provide proper context. This also means that further comments on efforts towards introducing universally accepted hate speech definitions, handling bias, or dealing with unbalanced data (particularly in terms of the difficulty it introduces to data collection and annotation [68]) are outside of the scope of this paper.…”

Section: Delimitationsmentioning

confidence: 99%

“…Below, we detail three such corpora that we used in our experiments, including the corpus used in the HASOC 2019 challenge we primarily target to solve. Here, for the sake of repeatability, as tweets (especially those that are offensive in nature) may be erased from twitter [68] we only considered self-contained datasets, where tweets are also made available for download (as opposed to only ids being uploaded). This means that some popular datasets had to be excluded from our examination [86,87].…”

Section: Labeled Corporamentioning

confidence: 99%

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Challenges of Hate Speech Detection in Social Media

2021

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The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has the potential to gravely damage our society, and severely harm marginalized people or groups. A major arena for spreading hate speech online is social media. This significantly contributes to the difficulty of automatic detection, as social media posts include paralinguistic signals (e.g. emoticons, and hashtags), and their linguistic content contains plenty of poorly written text. Another difficulty is presented by the context-dependent nature of the task, and the lack of consensus on what constitutes as hate speech, which makes the task difficult even for humans. This makes the task of creating large labeled corpora difficult, and resource consuming. The problem posed by ungrammatical text has been largely mitigated by the recent emergence of deep neural network (DNN) architectures that have the capacity to efficiently learn various features. For this reason, we proposed a deep natural language processing (NLP) model—combining convolutional and recurrent layers—for the automatic detection of hate speech in social media data. We have applied our model on the HASOC2019 corpus, and attained a macro F1 score of 0.63 in hate speech detection on the test set of HASOC. The capacity of DNNs for efficient learning, however, also means an increased risk of overfitting. Particularly, with limited training data available (as was the case for HASOC). For this reason, we investigated different methods for expanding resources used. We have explored various opportunities, such as leveraging unlabeled data, similarly labeled corpora, as well as the use of novel models. Our results showed that by doing so, it was possible to significantly increase the classification score attained.

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Section: Delimitationsmentioning

confidence: 99%

Section: Labeled Corporamentioning

confidence: 99%

Challenges of Hate Speech Detection in Social Media

2021

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“…Hate speech are often marked by negative meanings [25]. Negative terms such as negative polarity scores and lexicons, emoticons and emojis have been used for sentiment analysis [26]. In the contexts of hate speech detection, count indicators of the negative sentiment features except negative polarity score, which relies on mathematical formulae were joined to the count of English, Afrikaans and IsiZulu slur words in Hatebase [27]…”

Section: ) Negative Sentiment-based Featuresmentioning

confidence: 99%

Evaluating Machine Learning Techniques for Detecting Offensive and Hate Speech in South African Tweets

Oriola

Kotzé

2020

IEEE Access

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In recent times, South Africa has been witnessing insurgence of offensive and hate speech along racial and ethnic dispositions on Twitter. Popular among the South African languages used is English. Although, machine learning has been successfully used to detect offensive and hate speech in several English contexts, the distinctiveness of South African tweets and the similarities among offensive, hate and free speeches require domain-specific English corpus and techniques to detect the offensive and hate speech. Thus, we developed an English corpus from South African tweets and evaluated different machine learning techniques to detect offensive and hate speech. Character n-gram, word n-gram, negative sentiment, syntactic-based features and their hybrid were extracted and analyzed using hyper-parameter optimization, ensemble and multi-tier meta-learning models of support vector machine, logistic regression, random forest, gradient boosting algorithms. The results showed that optimized support vector machine with character ngram performed best in detection of hate speech with true positive rate of 0.894, while optimized gradient boosting with word n-gram performed best in detection of hate speech with true positive rate of 0.867. However, their performances in detection of other threatening classes were poor. Multi-tier meta-learning models achieved the most consistent and balanced classification performance with true positive rates of 0.858 and 0.887 for hate speech and offensive speech, respectively as well as true positive rate of 0.646 for free speech and overall accuracy of 0.671. The error analysis showed that multi-tier meta-learning model could reduce the misclassification error rate of the optimized models by 34.26%. INDEX TERMS Machine learning, South Africa, Twitter, hate speech, offensive speech. OLUWAFEMI ORIOLA received the B.Sc. degree (Hons.

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“…No context or meta-data are given, which might make these tasks somewhat unrealistic. Others have also used the network structure of social media to detect problematic content [39]. Platforms can use all meta-data of a post and a user.…”

Section: Hate Speech Task Definition Of Hasocmentioning

confidence: 99%

Tracking Hate in Social Media: Evaluation, Challenges and Approaches

et al. 2020

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This paper presents online hate speech as a societal and computational challenge. Offensive content detection in social media is considered as a multilingual, multi-level, multi-class classification problem for three Indo-European languages. This research problem is offered to the community through the HASOC shared task. HASOC intends to stimulate research and development in hate speech recognition across different languages. Three datasets (in English, German, and Hindi) were developed from Twitter and Facebook, and made available. This paper describes the creation of the multilingual datasets and the annotation method. We will present the numerous approaches based on traditional classifiers, deep neural models, and transfer learning models, along with features used for the classification. Results show that the best classifier for the binary classification might not perform best in the multi-class classification, and the performance of the same classifier varies across the languages. Overall, transfer learning models such as BERT, and deep neural models based on LSTMs and CNNs perform similar but better than traditional classifiers such as SVM. We will conclude the discussion with a list of issues that needs to be addressed for future datasets.

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Detecting and Monitoring Hate Speech in Twitter

Cited by 136 publications

References 69 publications

Challenges of Hate Speech Detection in Social Media

Challenges of Hate Speech Detection in Social Media

Evaluating Machine Learning Techniques for Detecting Offensive and Hate Speech in South African Tweets

Tracking Hate in Social Media: Evaluation, Challenges and Approaches

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