Detecting and visualizing hate speech in social media: A cyber Watchdog for surveillance

Modha, Sandip; Majumder, Prasenjit; Mandl, Thomas; Mandalia, Chintak

doi:10.1016/j.eswa.2020.113725

Cited by 67 publications

(22 citation statements)

References 24 publications

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“…The identification of offensive content still leaves the social questions unanswered: How to react? Different approaches have been proposed; they reach from deletion [42] to labeling [43] and to counter speech by either bots [44] or humans [45]. Societies need to find strategies adequate for their specific demands.…”

Section: Discussionmentioning

confidence: 99%

“…While research in this area has been gaining momentum [13], there is increasing evidence that social media platforms still struggle to keep up with the demand for technology, particularly for languages other than English [14]. For example, a recent article pointed out that Facebook does not have technology for identifying hate speech in the 22 official languages of India, its biggest market worldwide.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German

Mandl

Modha²,

et al. 2020

Forum for Information Retrieval Evaluation

Self Cite

103

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With the growth of social media, the spread of hate speech is also increasing rapidly. Social media are widely used in many countries. Also Hate Speech is spreading in these countries. This brings a need for multilingual Hate Speech detection algorithms. Much research in this area is dedicated to English at the moment. The HASOC track intends to provide a platform to develop and optimize Hate Speech detection algorithms for Hindi, German and English. The dataset is collected from a Twitter archive and pre-classified by a machine learning system. HASOC has two sub-task for all three languages: task A is a binary classification problem (Hate and Not Offensive) while task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY. Overall, 252 runs were submitted by 40 teams. The performance of the best classification algorithms for task A are F1 measures of 0.51, 0.53 and 0.52 for English, Hindi, and German, respectively. For task B, the best classification algorithms achieved F1 measures of 0.26, 0.33 and 0.29 for English, Hindi, and German, respectively. This article presents the tasks and the data development as well as the results. The best performing algorithms were mainly variants of the transformer architecture BERT. However, also other systems were applied with good success.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German

Mandl

Modha²,

et al. 2020

Forum for Information Retrieval Evaluation

Self Cite

103

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show abstract

“…Ambiguity of the definition of offensiveness is a serious problem. This inconsistency is visible in many reviews related to automatic detection of hate speech (Fortuna and Nunes, 2018;Schmidt and Wiegand, 2017;Alrehili, 2019;Poletto et al, 2020) or more specifically on aggressiveness detection (Sadiq et al, 2021;Modha et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection

Kanclerz¹,

Figas²,

Gruza³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers. They are commonly identified using classifiers solely based on textual content that generalize pre-agreed meanings of difficult problems. Such models provide the same results for each user, which leads to high misclassification rate observable especially for contentious, aggressive documents. Both document controversy and user nonconformity require new solutions. Therefore, we propose novel personalized approaches that respect individual beliefs expressed by either user conformity-based measures or various embeddings of their previous text annotations. We found that only a few annotations of most controversial documents are enough for all our personalization methods to significantly outperform classic, generalized solutions. The more controversial the content, the greater the gain. The personalized solutions may be used to efficiently filter unwanted aggressive content in the way adjusted to a given person.

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“…2 Once candidate tweets are collected, they are assessed and labeled by human annotators into pre-specified categories. The manual annotation of the examples provides with high-quality ground truth labeled datasets, yet it is costly (Modha et al, 2020). Accordingly, the available datasets each include only a few thousands of labeled examples.…”

Section: Hate Speech Datasetsmentioning

confidence: 99%

Character-level HyperNetworks for Hate Speech Detection

Wullach,

Adler,

Minkov

2021

Preprint

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The massive spread of hate speech, hateful content targeted at specific subpopulations, is a problem of critical social importance. Automated methods for hate speech detection typically employ state-of-the-art deep learning (DL)-based text classifiers-very large pre-trained neural language models of over 100 million parameters, adapting these models to the task of hate speech detection using relevant labeled datasets. Unfortunately, there are only numerous labeled datasets of limited size that are available for this purpose. We make several contributions with high potential for advancing this state of affairs. We present HyperNetworks for hate speech detection, a special class of DL networks whose weights are regulated by a small-scale auxiliary network. These architectures operate at character-level, as opposed to word-level, and are several magnitudes of order smaller compared to the popular DL classifiers. We further show that training hate detection classifiers using large amounts of automatically generated examples in a procedure named as data augmentation is beneficial in general, yet this practice especially boosts the performance of the proposed HyperNetworks. In fact, we achieve performance that is comparable or better than state-of-the-art language models, which are pre-trained and orders of magnitude larger, using this approach, as evaluated using five public hate speech datasets.

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Detecting and visualizing hate speech in social media: A cyber Watchdog for surveillance

Cited by 67 publications

References 24 publications

Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German

Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German

Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection

Character-level HyperNetworks for Hate Speech Detection

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