Handling bias in toxic speech detection: A survey
Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors
such as the context, geography, socio-political climate, and background of the producers …
such as the context, geography, socio-political climate, and background of the producers …
Thirty years of research into hate speech: topics of interest and their evolution
A Tontodimamma, E Nissi, A Sarra, L Fontanella - Scientometrics, 2021 - Springer
The exponential growth of social media has brought with it an increasing propagation of
hate speech and hate based propaganda. Hate speech is commonly defined as any …
hate speech and hate based propaganda. Hate speech is commonly defined as any …
Hatexplain: A benchmark dataset for explainable hate speech detection
Hate speech is a challenging issue plaguing the online social media. While better models
for hate speech detection are continuously being developed, there is little research on the …
for hate speech detection are continuously being developed, there is little research on the …
You only prompt once: On the capabilities of prompt learning on large language models to tackle toxic content
The spread of toxic content online is an important problem that has adverse effects on user
experience online and in our society at large. Motivated by the importance and impact of the …
experience online and in our society at large. Motivated by the importance and impact of the …
Challenges of hate speech detection in social media: Data scarcity, and leveraging external resources
The detection of hate speech in social media is a crucial task. The uncontrolled spread of
hate has the potential to gravely damage our society, and severely harm marginalized …
hate has the potential to gravely damage our society, and severely harm marginalized …
[HTML][HTML] SocialHaterBERT: A dichotomous approach for automatically detecting hate speech on Twitter through textual analysis and user profiles
G del Valle-Cano, L Quijano-Sánchez… - Expert Systems with …, 2023 - Elsevier
Social media platforms have evolved into an online representation of our social interactions.
We may use the resources they provide to analyze phenomena that occur within them, such …
We may use the resources they provide to analyze phenomena that occur within them, such …
[HTML][HTML] How well do hate speech, toxicity, abusive and offensive language classification models generalize across datasets?
A considerable body of research deals with the automatic identification of hate speech and
related phenomena. However, cross-dataset model generalization remains a challenge. In …
related phenomena. However, cross-dataset model generalization remains a challenge. In …
VGCN-BERT: augmenting BERT with graph embedding for text classification
Much progress has been made recently on text classification with methods based on neural
networks. In particular, models using attention mechanism such as BERT have shown to …
networks. In particular, models using attention mechanism such as BERT have shown to …
Advances in machine learning algorithms for hate speech detection in social media: a review
NS Mullah, WMNW Zainon - IEEE Access, 2021 - ieeexplore.ieee.org
The aim of this paper is to review machine learning (ML) algorithms and techniques for hate
speech detection in social media (SM). Hate speech problem is normally model as a text …
speech detection in social media (SM). Hate speech problem is normally model as a text …
Toxic, hateful, offensive or abusive? what are we really classifying? an empirical analysis of hate speech datasets
The field of the automatic detection of hate speech and related concepts has raised a lot of
interest in the last years. Different datasets were annotated and classified by means of …
interest in the last years. Different datasets were annotated and classified by means of …