Hate speech detection using brazilian imageboards

Nascimento, Gabriel; Carvalho, Flavio; Viana, Carlos Roberto; Guedes, Gustavo Paiva

doi:10.1145/3323503.3360619

Cited by 14 publications

(8 citation statements)

References 19 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%

“…In (Basile et al 2019;Fersini et al 2018a) a combined approach has been applied to collect the hateful and misogynous tweets, by monitoring potential victims of hate accounts, downloading the history of identified haters and filtering Twitter streams with keywords. In few other cases (see Nascimento et al (2019)), a sort of a priori classification is attributed to the texts according to the retrieval source, assuming that all the items collected from a given source can be considered hateful. Quite uniquely, Fišer et al 2017use a corpus extracted from an online platform that collects spontaneous reports by the Internet users of any material containing HS or child sexual abuse: the corpus is then checked by experts validation, assessing that more than 40% is not actually disturbing content and that only 3% can be considered illegal content.…”

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

272

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%

Section: Data Sourcementioning

confidence: 99%

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

Poletto

Basile

Sanguinetti

et al. 2020

Lang Resources & Evaluation

272

210

View full text Add to dashboard Cite

show abstract

“…The Brazilian Portuguese version contains 14,459 words labeled in 73 categories. Some recent studies have presented promising results using this dictionary (Carvalho et al, 2019; Nascimento et al, 2019).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

The Happy Level: A New Approach to Measure Happiness at Work Using Mixed Methods

Sender

Carvalho

Guedes

2021

International Journal of Qualitative Methods

Self Cite

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Happiness at Work is considered the Holy Grail of organizational sciences. The belief that happier workers are more productive leads to a win-win situation for both individuals and organizations. Nevertheless, years of research have not brought a convergent conclusion about the topic, mainly due to the lack of a widely accepted measure. Usually, questionnaires and self-report surveys are used; however, these methods embed shortcomings that allow studies’ results to be questioned. In order to overcome these shortcomings, the present study proposes a different approach to measure Happiness at Work, bringing mixed methods to encompass the complexity of the phenomenon. Based on work-life narratives and following Kahneman’s concepts, the proposed approach puts together Narrative Analysis and Sentiment Analysis. Although increasingly used to assess social media reviews, Sentiment Analysis is not yet applied to narratives related to Happiness at Work. Four methods to calculate the Happy Level indicator were tested on actual research data: one manual, through traditional coding processes, and three automatic methods to provide scalability. An example of the Happy Level application is also provided to illustrate how the indicator could improve analyses. The present study concludes that despite the manual method presents better results at this moment; the automatic ones are promising. The results also indicate paths for improvement of these methods.

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“…A systematic review of hate speech detection was carried out by Poletto et al (2021); the authors pointed out that there exists more datasets for English than other languages, 37 out of 64 are English datasets, for Portuguese, there are only 2 datasets found by those authors. The first dataset, called NCCVG 1 , was published in Nascimento et al (2019); it contains 7,671 entries from two data sources: Twitter and 55chan. The second dataset, called OFFCOMBR 2 , published in de Pelle and Moreira (2017), contains 1,250 comments posted on a news site in Brazil https://g1.globo.com/.…”

Section: Related Workmentioning

confidence: 99%

OLID-BR: Offensive Language Identification Dataset for Brazilian Portuguese

Trajano

Bordini

Vieira

2022

Preprint

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Social media has transformed the way society is connect. Although there are many advantages to such large scale connection, there are unquestionable drawbacks, for example in the way fake news is easily spread and in particular the vast dissemination of hate speech. Identifying offensive comments is a critical task for ensuring the safety of users, which is why industry and academia have been working on developing solutions to this problem. Previous work on the automatic detection of offensive comments focuses mainly on English, with very little work for languages such as Portuguese. In this paper, we present a new dataset for toxicity detection in Portuguese containing 6,354 (extendable to 13,538) comments annotated using a hierarchical scheme with multiple levels of granularity, which gives us the possibility of training models for multiple NLP tasks. The dataset is used to train machine learning models which can be useful to identify several aspects of hate speech. The results of the experiments show that the dataset is useful in the development of intelligent systems for the detection of offensive comments in Portuguese.

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Hate speech detection using brazilian imageboards

Cited by 14 publications

References 19 publications

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

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

The Happy Level: A New Approach to Measure Happiness at Work Using Mixed Methods

OLID-BR: Offensive Language Identification Dataset for Brazilian Portuguese

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