Human-centered neural reasoning for subjective content processing: Hate speech, emotions, and humor

Kazienko, Przemysław; Bielaniewicz, Julita; Gruza, Marcin; Kanclerz, Kamil; Karanowski, Konrad; Miłkowski, Piotr; Kocoń, Jan

doi:10.1016/j.inffus.2023.01.010

Cited by 17 publications

(11 citation statements)

References 36 publications

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“…Notably, an intensified focus on classifier ensembles and the efficiency of meta-learning strategies is essential for advancing this domain (Zampieri et al, 2019). In addition, a comprehensive exploration of the role of humor in HSD tasks is warranted (Fortuna & Nunes, 2018;Mathew et al, 2020) with the literature underscoring an enthusiastic interest in sarcasm detection, colloquially referred to as humor tweet detection, in some quarters (Bogireddy et al, 2023;Kazienko et al, 2023). Furthermore, considerations related to dialects are imperative to mitigate the risks of intentional racial biases in HSD, as cogently articulated by (Field et al, 1925).…”

Section: Discussion and Future Workmentioning

confidence: 99%

Hate speech detection in social media: Techniques, recent trends, and future challenges

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

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The realm of Natural Language Processing and Text Mining has seen a surge in interest from researchers in hate speech detection, leading to an increase in related studies. This analysis aims to create a valuable resource by summarizing the methods and strategies used to combat hate speech in social media. We perform a detailed review to achieve a deep knowledge of the hate speech detection landscape from 2018 to 2023, revealing global incidents of hate speech in 2022–2023. Sixty‐six relevant articles were selected for this review. Existing studies were analyzed and categorized into five method categories: Machine Learning, Deep Learning, Ensemble models, Graph Neural Networks, and Graph Convolutional Networks. These advancements can aid social networking services in identifying hate messages before being posted, reducing the risk of harassment. The review also covers available hate speech datasets and highlights research challenges, but it is clear that a definitive solution to this problem is yet to be found. Future research directions are recommended to address the ongoing challenges in Hate Speech Detection.This article is categorized under: Applications of Computational Statistics > Computational Linguistics Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Classification and Regression Trees (CART) Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining

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Section: Discussion and Future Workmentioning

confidence: 99%

Hate speech detection in social media: Techniques, recent trends, and future challenges

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

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“…The proposed model was able to reduce racial bias. The authors of [59] suggested a new idea of personalized, human-centered NLP that depends not only on the text but also on the user. c) Explainability…”

Section: B) Biasmentioning

confidence: 99%

Addressing Challenges in Hate Speech Detection using BERT-based Models: A Review

Aljawazeri,

Jasim

2024

Iraqi Journal For Computer Science and Mathematics

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The rapid growth of social media platforms has led to an increase in hate speech. This has promptedthe development of effective detection mechanisms that aim to mitigate the potential hazards and threats it poses tosociety. BERT (Bidirectional Encoder Representations from Transformers) has produced cutting-edge results inthis field. This review paper aims to identify and analyze the whole process of using the BERT model to tackle thechallenges associated with the hate speech detection problem. This academic discussion will begin by addressingthe training datasets and the preprocessing methods involved. Subsequently, the use of the BERT model will beexplored, followed by an examination of the contributions made to address the issues encountered. Finally, we willdiscuss the evaluation phase. The use of BERT included the application of two primary approaches. In the featurebased approach, BERT accepts textual input and generates its corresponding representation as output. The resultingoutput is then used as input for any classification model. The second approach involves the process of fine-tuningBERT using labeled datasets and then employing it directly for classification purposes. The controversial issuesand open challenges that appeared at each stage were discussed. The results indicate that in both approaches,BERT has shown its efficacy relative to other models under contention. However, there is a need for greaterattention and advancement to effectively solve the existing issues and constraints in the future.

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“…As a concept of contextual and human-centered processing, personalization in NLP was proposed by us and recently extensively explored in [19,21,20,81,82,83,84,85].…”

Section: Random Contextual Few-shot Personalizationmentioning

confidence: 99%

“…This intuition is based on the fact that the selection of human annotators by ChatGPT developers was mainly due to their high agreement [18]. At the same time, it is difficult to identify universal ground truth in tasks such as emotion prediction or text offensiveness, especially in a setting of personalized inference [19,20,21]. In addition, there is a chance that ChatGPT was not trained on many of the datasets we tested in our work.…”

Section: Introductionmentioning

confidence: 99%

Chatgpt: Jack of All Trades, Master of None

Kocoń

Cichecki

Kaszyca

et al. 2023

Preprint

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Human-centered neural reasoning for subjective content processing: Hate speech, emotions, and humor

Cited by 17 publications

References 36 publications

Hate speech detection in social media: Techniques, recent trends, and future challenges

Hate speech detection in social media: Techniques, recent trends, and future challenges

Addressing Challenges in Hate Speech Detection using BERT-based Models: A Review

Chatgpt: Jack of All Trades, Master of None

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