Do Characters Abuse More Than Words?

Mehdad, Yashar; Tetreault, Joel

doi:10.18653/v1/w16-3638

Cited by 140 publications

(93 citation statements)

References 12 publications

(16 reference statements)

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“…Existing methods primarily cast the problem as a supervised document classification task [36]. These can be divided into two categories: one relies on manual feature engineering that are then consumed by algorithms such as SVM, Naive Bayes, and Logistic Regression [2,9,11,16,20,24,[38][39][40][41][42] (classic methods); the other represents the more recent deep learning paradigm that employs neural networks to automatically learn multi-layers of abstract features from raw data [14,27,31,37] (deep learning methods).…”

Section: Methods Of Hate Speech Detection and Related Problemsmentioning

confidence: 99%

Hate speech detection: A solved problem? The challenging case of long tail on Twitter

2019

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In recent years, the increasing propagation of hate speech on social media and the urgent need for effective countermeasures have drawn significant investment from governments, companies, and researchers. A large number of methods have been developed for automated hate speech detection online. This aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. However, we notice significant difference between the performance of the two (i.e., non-hate v.s. hate). In this work, we argue for a focus on the latter problem for practical reasons. We show that it is a much more challenging task, as our analysis of the language in the typical datasets shows that hate speech lacks unique, discriminative features and therefore is found in the 'long tail' in a dataset that is difficult to discover. We then propose Deep Neural Network structures serving as feature extractors that are particularly effective for capturing the semantics of hate speech. Our methods are evaluated on the largest collection of hate speech datasets based on Twitter, and are shown to be able to outperform the best performing method by up to 5 percentage points in macro-average F1, or 8 percentage points in the more challenging case of identifying hateful content.

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Section: Methods Of Hate Speech Detection and Related Problemsmentioning

confidence: 99%

Hate speech detection: A solved problem? The challenging case of long tail on Twitter

2019

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“…The features used in traditional machine learning approaches are the main aspects distinguishing different methods, and surface-level features such as bag of words, word-level and character-level n-grams, etc. have proven to be the most predictive features [11,13,22]. Apart from features, different algorithms such as Support Vector Machines [10], Naive Baye [16], and Logistic Regression [3,22], etc.…”

Section: Previous Workmentioning

confidence: 99%

“…To detect online hate speech, a large number of scientific studies have been dedicated by using Natural Language Processing (NLP) in combination with Machine Learning (ML) and Deep Learning (DL) methods [1,8,11,13,22,25]. Although supervised machine learning-based approaches have used different text mining-based features such as surface features, sentiment analysis, lexical resources, linguistic features, knowledge-based features or user-based and platformbased metadata [3,6,23], they necessitate a well-defined feature extraction approach.…”

Section: Introductionmentioning

confidence: 99%

A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media

Mozafari

Farahbakhsh

Crespi

2019

Studies in Computational Intelligence

253

173

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Generated hateful and toxic content by a portion of users in social media is a rising phenomenon that motivated researchers to dedicate substantial efforts to the challenging direction of hateful content identification. We not only need an efficient automatic hate speech detection model based on advanced machine learning and natural language processing, but also a sufficiently large amount of annotated data to train a model. The lack of a sufficient amount of labelled hate speech data, along with the existing biases, has been the main issue in this domain of research. To address these needs, in this study we introduce a novel transfer learning approach based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers). More specifically, we investigate the ability of BERT at capturing hateful context within social media content by using new finetuning methods based on transfer learning. To evaluate our proposed approach, we use two publicly available datasets that have been annotated for racism, sexism, hate, or offensive content on Twitter. The results show that our solution obtains considerable performance on these datasets in terms of precision and recall in comparison to existing approaches. Consequently, our model can capture some biases in data annotation and collection process and can potentially lead us to a more accurate model.

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“…Badjatiya et al (2017) implemented Gradient Boosted Decision Trees classifiers using word representations trained by deep learning models. Other researchers have investigated characterlevel representations and their effectiveness compared to word-level representations (Mehdad and Tetreault, 2016;Park and Fung, 2017).…”

Section: Related Workmentioning

confidence: 99%

Comparative Studies of Detecting Abusive Language on Twitter

Lee¹,

Yoon²,

Jung³

2018

Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)

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The context-dependent nature of online aggression makes annotating large collections of data extremely difficult. Previously studied datasets in abusive language detection have been insufficient in size to efficiently train deep learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much greater in size and reliability, has been released. However, this dataset has not been comprehensively studied to its potential. In this paper, we conduct the first comparative study of various learning models on Hate and Abusive Speech on Twitter, and discuss the possibility of using additional features and context data for improvements. Experimental results show that bidirectional GRU networks trained on word-level features, with Latent Topic Clustering modules, is the most accurate model scoring 0.805 F1.

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Do Characters Abuse More Than Words?

Cited by 140 publications

References 12 publications

Hate speech detection: A solved problem? The challenging case of long tail on Twitter

Hate speech detection: A solved problem? The challenging case of long tail on Twitter

A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media

Comparative Studies of Detecting Abusive Language on Twitter

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