Hate Speech Detection Based on Sentiment Knowledge Sharing

Zhou, Xianbing; Yang, Yong; Fan, Xiaochao; Ren, Guanghui; Yun-feng, Song; Diao, Yuanan; Yang, Liang; Lin, Hongfei

doi:10.18653/v1/2021.acl-long.556

Cited by 31 publications

(16 citation statements)

References 19 publications

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“…In an effort to mitigate the need for extensive annotation some works use transformers to generate more samples, e.g., (Vidgen et al 2020b;Minkov 2020, 2021). Zhou et al (2021) integrate features from external resources to support the model performance.…”

Section: Related Workmentioning

confidence: 99%

Going Extreme: Comparative Analysis of Hate Speech in Parler and Gab

Israeli¹,

Tsur²

2022

Preprint

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Social platforms such as Gab and Parler, branded as 'freespeech' networks, have seen a significant growth of their user base in recent years. This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit. In this work we provide the first large scale analysis of hate-speech on Parler. We experiment with an array of algorithms for hate-speech detection, demonstrating limitations of transfer learning in that domain, given the illusive and ever changing nature of the ways hate-speech is delivered. In order to improve classification accuracy we annotated 10K Parler posts, which we use to fine-tune a BERT classifier. Classification of individual posts is then leveraged for the classification of millions of users via label propagation over the social network. Classifying users by their propensity to disseminate hate, we find that hate mongers make 16.1% of Parler active users, and that they have distinct characteristics comparing to other user groups. We find that hate mongers are more active, more central and express distinct levels of sentiment and convey a distinct array of emotions like anger and sadness. We further complement our analysis by comparing the trends discovered in Parler and those found in Gab. To the best of our knowledge, this is among the first works to analyze hate speech in Parler in a quantitative manner and on the user level, and the first annotated dataset to be made available to the community.

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Section: Related Workmentioning

confidence: 99%

Going Extreme: Comparative Analysis of Hate Speech in Parler and Gab

Israeli¹,

Tsur²

2022

Preprint

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“…India Article 19 (1) of the constitution of India protects the freedom of speech and expression. However, article 19 (2) states that to protect sovereignty, integrity, and security of the state, to protect decency and morality, defamation and incitement to an event, some restriction can be imposed…”

Section: Germanymentioning

confidence: 99%

Transformer based Ensemble Learning to Hate Speech Detection Leveraging Sentiment and Emotion Knowledge Sharing

Kapil¹,

Ekbal²

2022

Artificial Intelligence Trends

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In recent years, the increasing propagation of hate speech on social media has encouraged researchers to address the problem of hateful content identification. To build an efficient hate speech detection model, a large number of annotated data is needed to train the model. To solve this approach we utilized eleven datasets from the hate speech domain and compared different transformer encoder-based approaches such as BERT, and ALBERT in single-task learning and multi-task learning (MTL) framework. We also leveraged the eight sentiment and emotion analysis datasets in the training to enrich the features in the MTL setting. The stacking based ensemble of BERT-MTL and ALBERT-MTL is utilized to combine the features from best two models. The experiments demonstrate the efficacy of the approach by attaining state-of-the-art results in all the datasets. The qualitative and quantitative error analysis was done to figure out the misclassified tweets and the effect of models on the different data sets.

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“…In an effort to mitigate the need for extensive annotation some works use transformers to generate more samples, e.g., (Vidgen et al, 2020b;Wullach et al, 2020Wullach et al, , 2021. Zhou et al (2021) integrate features from external resources to support the model performance.…”

Section: Related Workmentioning

confidence: 99%

Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler

Israeli¹,

Tsur²

2022

Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)

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Social platforms such as Gab and Parler, branded as 'free-speech' networks, have seen a significant growth of their user base in recent years. This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit. In this work we provide the first large scale analysis of hate-speech on Parler. We experiment with an array of algorithms for hate-speech detection, demonstrating the limitations of transfer learning in that domain, given the illusive and ever changing nature of the ways hate-speech is delivered. In order to improve classification accuracy we annotated 10K Parler posts, which we use to fine-tune a BERT classifier. Classification of individual posts is then leveraged for the classification of millions of users via label propagation over the social network. Classifying users by their propensity to disseminate hate, we find that hate mongers make about 16% of Parler active users, and that they have distinct characteristics comparing to other user groups. We find that hate mongers are more active, more central, express distinct levels of sentiment, and convey a distinct array of emotions like anger and sadness. We further complement our analysis by comparing the trends observed in Parler to those found in Gab. To the best of our knowledge, this is among the first works to analyze hate speech in Parler in a quantitative manner and on the user level.

show abstract

Hate Speech Detection Based on Sentiment Knowledge Sharing

Cited by 31 publications

References 19 publications

Going Extreme: Comparative Analysis of Hate Speech in Parler and Gab

Going Extreme: Comparative Analysis of Hate Speech in Parler and Gab

Transformer based Ensemble Learning to Hate Speech Detection Leveraging Sentiment and Emotion Knowledge Sharing

Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler

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