Self-regulation of fundamental rights? The EU Code of Conduct on Hate Speech, related initiatives and beyond

Quintel, Teresa; Ullrich, Carsten A.

doi:10.4337/9781788976688.00019

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Seen this way, standardisation can be considered a way to replace self-regulatory solutions with co-regulatory ones in the private adjudication of fundamental rights. 145 Therefore, in the context of media freedom, despite well-founded concerns about delegating regulatory power to standard-setting bodies, standardisation stands as a more inclusive mechanism for regulating algorithms for content moderation than unmonitored private regulation while preventing state interference through public regulation. 146…”

Section: Marta Cantero Gamitomentioning

confidence: 99%

Consistent Regulatory and Self-Regulatory Mechanisms for Media Freedom in the Digital Single Market. the European Media Freedom Act (EMFA) as Meta-Regulation

Gamito

2022

SSRN Journal

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Section: Marta Cantero Gamitomentioning

confidence: 99%

Consistent Regulatory and Self-Regulatory Mechanisms for Media Freedom in the Digital Single Market. the European Media Freedom Act (EMFA) as Meta-Regulation

Gamito

2022

SSRN Journal

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“…Any unethical disparaging remark or expression targeted at an individual or group, based on identities such as race, sexual orientation, religion, or similar factors, is usually considered Hate Speech (HS) [1][2][3][4][5]. It is motivated by hatred or bias and aims to offend the target.…”

Section: Introductionmentioning

confidence: 99%

T5 for Hate Speech, Augmented Data, and Ensemble

Adewumi,

Sabry,

Abid

et al. 2023

Sci

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We conduct relatively extensive investigations of automatic hate speech (HS) detection using different State-of-The-Art (SoTA) baselines across 11 subtasks spanning six different datasets. Our motivation is to determine which of the recent SoTA models is best for automatic hate speech detection and what advantage methods, such as data augmentation and ensemble, may have on the best model, if any. We carry out six cross-task investigations. We achieve new SoTA results on two subtasks—macro F1 scores of 91.73% and 53.21% for subtasks A and B of the HASOC 2020 dataset, surpassing previous SoTA scores of 51.52% and 26.52%, respectively. We achieve near-SoTA results on two others—macro F1 scores of 81.66% for subtask A of the OLID 2019 and 82.54% for subtask A of the HASOC 2021, in comparison to SoTA results of 82.9% and 83.05%, respectively. We perform error analysis and use two eXplainable Artificial Intelligence (XAI) algorithms (Integrated Gradient (IG) and SHapley Additive exPlanations (SHAP)) to reveal how two of the models (Bi-Directional Long Short-Term Memory Network (Bi-LSTM) and Text-to-Text-Transfer Transformer (T5)) make the predictions they do by using examples. Other contributions of this work are: (1) the introduction of a simple, novel mechanism for correcting Out-of-Class (OoC) predictions in T5, (2) a detailed description of the data augmentation methods, and (3) the revelation of the poor data annotations in the HASOC 2021 dataset by using several examples and XAI (buttressing the need for better quality control). We publicly release our model checkpoints and codes to foster transparency.

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“…Such content is often considered harmful for a rationale and constructive debate [1]. Many countries have defined more and more detailed rules for dealing with offensive posts [2,3]. Companies and platforms are also concerned about problematic content.…”

Section: Introduction: Hate Speech and Its Identificationmentioning

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

105

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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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Self-regulation of fundamental rights? The EU Code of Conduct on Hate Speech, related initiatives and beyond

Abstract: 17 Recommendation 98/560/EC 17 on the development of the competitiveness of the European audio-visual and information services industry by promoting national frameworks aimed at achieving a comparable and effective level of protection of minors and human dignity.

Cited by 9 publications

References 4 publications

Consistent Regulatory and Self-Regulatory Mechanisms for Media Freedom in the Digital Single Market. the European Media Freedom Act (EMFA) as Meta-Regulation

Consistent Regulatory and Self-Regulatory Mechanisms for Media Freedom in the Digital Single Market. the European Media Freedom Act (EMFA) as Meta-Regulation

T5 for Hate Speech, Augmented Data, and Ensemble

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

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