FLOSS as a Source for Profanity and Insults: Collecting the Data

Squire, Megan; Gazda, Rebecca

doi:10.1109/hicss.2015.623

Cited by 22 publications

(13 citation statements)

References 21 publications

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“…Although, toxic contents have been found among SE interactions [1]- [5], most of the prior works in the SE domain focused on identifying software developers' sentiments [31]- [33]. Raman et al proposed the first SE domain specific toxicity detector, and used that to study unhealthy interactions among FOSS developers [16].…”

Section: B Prior Research On Toxicity Analysismentioning

confidence: 99%

“…Although Gitter is similar to Instant Relay Chat (IRC), Gitter's integration with Github repositories has made it popular among recent FOSS projects. Since prior research found toxic interactions among FOSS IRC channels [1], [35], we considered chat messages as one of our sources. We selected the gitter channel of the Ethereum project 6 , since it is one of the most active channels on Gitter.…”

Section: A Data Source Selectionmentioning

confidence: 99%

“…Prior research have found multiple evidence of toxic interactions, such as: profanity, insult, hate speech, identity attack, misogynistic remarks, flirtations, or sexual innuendos, among several Free / Open Source Software (FOSS) projects [1]- [5]. Toxic interactions may have serious repercussions on a FOSS project.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, researchers have been focusing on automatic identification of toxic online conversations [8]- [12] to prevent such negative incidents. Jigsaw (a unit of Google) 1 is on the forefront of this research with building a public API named the Perspective API 2 to automatically score perceived toxicity of a text. Kaggle 3 , in collaboration with the Jigsaw, started a competition called 'Toxic Comment Classification Challenge in 2018' 4 with the goal of building a classifier that can classify toxic contents better than the Perspective API.…”

Section: Introductionmentioning

confidence: 99%

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A Benchmark Study of the Contemporary Toxicity Detectors on Software Engineering Interactions

Sarker¹,

Turzo²,

Bosu³

2020

Preprint

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Automated filtering of toxic conversations may help an Open-source software (OSS) community to maintain healthy interactions among the project participants. Although, several general purpose tools exist to identify toxic contents, those may incorrectly flag some words commonly used in the Software Engineering (SE) context as toxic (e.g., 'junk', 'kill', and 'dump') and vice versa. To encounter this challenge, an SE specific tool has been proposed by the CMU Strudel Lab (referred as the 'STRUDEL' hereinafter) by combining the output of the Perspective API with the output from a customized version of the Stanford's Politeness detector tool. However, since STRUDEL's evaluation was very limited with only 654 SE text, its practical applicability is unclear. Therefore, this study aims to empirically evaluate the Strudel tool as well as four state-of-the-art general purpose toxicity detectors on a large scale SE dataset. On this goal, we empirically developed a rubric to manually label toxic SE interactions. Using this rubric, we manually labeled a dataset of 6,533 code review comments and 4,140 Gitter messages. The results of our analyses suggest significant degradation of all tools' performances on our datasets. Those degradations were significantly higher on our dataset of formal SE communication such as code review than on our dataset of informal communication such as Gitter messages. Two of the models from our study showed significant performance improvements during 10fold cross validations after we retrained those on our SE datasets. Based on our manual investigations of the incorrectly classified text, we have identified several recommendations for developing an SE specific toxicity detector.

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Section: B Prior Research On Toxicity Analysismentioning

confidence: 99%

Section: A Data Source Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Benchmark Study of the Contemporary Toxicity Detectors on Software Engineering Interactions

Sarker¹,

Turzo²,

Bosu³

2020

Preprint

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“…In [10] a method to produce test and training data sets for using in tasks for recognizing different types of human speech such as humor, sarcasm, insults and profanity. The construction process of relevant helper data sets such as lists of profanity, insults lists and list of projects with their codes of conduct is also described.…”

Section: State Of the Artmentioning

confidence: 99%

Dictionaries of Mexican Sexual Slang for NLP

Villarejo-Martínez

Castro-Sánchez

Sierra

2018

CLEIej

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In this paper the creation of two important relevant resources for the double entendre and humour recognition problem in Mexican Spanish is described: a morphological dictionary and a semantic dictionary. These were created from two sources: a corpus of albures (drawn from "Antología del albur") and a Mexican slang dictionary ("El chilangonario"). The morphological dictionary consists of 410 forms of words that corresponds to 350 lemmas. The semantic dictionary consists of 27 synsets that are associated to lemmas of morphological dictionary. Since both resources are based on Freeling library, they are easy to implement for tasks in Natural Language Processing. The motivation for this work comes from the need to address problems such as double entendre and computational humour. The usefulness of these disciplines has been discussed many times and it has been shown that they have a direct impact on user interfaces and mainly in human-computer interaction. This work aims to promote the scientific community to generate more resources about informal language in Spanish and other languages.

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