Overview of the HASOC track at FIRE 2019

Mandl, Thomas; Modha, Sandip; Majumder, Prasenjit; Patel, Daksh; Dave, Mohana; Mandlia, Chintak; Patel, Aditya

doi:10.1145/3368567.3368584

Cited by 160 publications

(72 citation statements)

References 3 publications

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“…A second key distinction concerns the source from which data are retrieved. The microblogging platform Twitter 11 is by far the most exploited source, due to the relatively reduced length of texts and to a friendly policy on making data publicly available: 32 resources contain tweets, one of which (Olteanu et al 2018) also features posts from the social aggregator Reddit 12 , one (Nascimento et al 2019) also retrieves comments from the 55chan 13 imageboard, while in two works (Bosco et al 2018;Mandl et al 2019 2018use sentences from the well-known white-suprematist forum Stormfront; the dataset released for the Hate Speech Hackathon 15 contains posts from the Wikipedia Topical focus: Abusiveness (5); Aggressiveness (2); Anti-Roma (1); Child sexual abuse (1); Cyberbullying (2); Flames (1); Harassment (1); Homophobia (4); HS (36); Islamophobia (2); Obscenity, Profanity (3); Offensiveness (13); Personal Attacks (1); Racism (6); Sexism, Misogyny (9); Threats, Violence (1); Toxicity (1); White supremacy (1). Nearly all the resources feature user-generated public contents, mostly microblog posts, often retrieved with a keyword-based approach and mostly using words with a negative polarity.…”

Section: Data Sourcementioning

confidence: 99%

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Resources and benchmark corpora for hate speech detection: a systematic review

Poletto

Basile

Sanguinetti

et al. 2020

Lang Resources & Evaluation

217

210

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Hate Speech in social media is a complex phenomenon, whose detection has recently gained significant traction in the Natural Language Processing community, as attested by several recent review works. Annotated corpora and benchmarks are key resources, considering the vast number of supervised approaches that have been proposed. Lexica play an important role as well for the development of hate speech detection systems. In this review, we systematically analyze the resources made available by the community at large, including their development methodology, topical focus, language coverage, and other factors. The results of our analysis highlight a heterogeneous, growing landscape, marked by several issues and venues for improvement.

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Section: Data Sourcementioning

confidence: 99%

“…Finally, two competitions explicitly focused on the identification of both HS and offensive language, i.e. HASOC at FIRE 2019 (Mandl et al 2019) and HSD, the HS detection task on Vietnamese at VLSP campaign in 2019 (Vu et al 2019).…”

Section: Shared Tasksmentioning

confidence: 99%

Resources and benchmark corpora for hate speech detection: a systematic review

Poletto

Basile

Sanguinetti

et al. 2020

Lang Resources & Evaluation

217

210

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“…As mentioned earlier, the six publicly available datasets [4][5][6][7][8][9] are manually curated and are of modest size. In the below text, we discuss the characteristics and the generation process of each of these datasets.…”

Section: Related Datasetsmentioning

confidence: 99%

“…The same process has been adopted for code-mixed and pure Hindi language. Since the sample space of [9] and [4] is small, we have tried to update these datasets with our own curated samples.…”

Section: Data Gathering Processmentioning

confidence: 99%

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Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media

Vashistha¹,

Zubiaga²

2020

Preprint

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The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.

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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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Overview of the HASOC track at FIRE 2019

Cited by 160 publications

References 3 publications

Resources and benchmark corpora for hate speech detection: a systematic review

Resources and benchmark corpora for hate speech detection: a systematic review

Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media

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

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