A Comparison of Machine Learning Approaches for Detecting Misogynistic Speech in Urban Dictionary

Lynn, Theo; Endo, Patrícia Takako; Rosati, Pierangelo; Silva, Ivanovitch; Santos, Guto Leoni; Ging, Debbie

doi:10.1109/cybersa.2019.8899669

Cited by 20 publications

(8 citation statements)

References 42 publications

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“…Several recent studies evidence the growing interest of the scientific community on automatic detection of hate speech, mainly for English [7][8][9][10][11][12]. This research area has grown mainly thanks to the competitions organized at SemEval [13] (e.g., HatEval, OffensEval, and Toxic Spans Detection) and other venues, such as TRAC [14] and HASOC [15].…”

Section: Related Workmentioning

confidence: 99%

A Language Model for Misogyny Detection in Latin American Spanish Driven by Multisource Feature Extraction and Transformers

et al. 2021

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Creating effective mechanisms to detect misogyny online automatically represents significant scientific and technological challenges. The complexity of recognizing misogyny through computer models lies in the fact that it is a subtle type of violence, it is not always explicitly aggressive, and it can even hide behind seemingly flattering words, jokes, parodies, and other expressions. Currently, it is even difficult to have an exact figure for the rate of misogynistic comments online because, unlike other types of violence, such as physical violence, these events are not registered by any statistical systems. This research contributes to the development of models for the automatic detection of misogynistic texts in Latin American Spanish and contributes to the design of data augmentation methodologies since the amount of data required for deep learning models is considerable.

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Section: Related Workmentioning

confidence: 99%

A Language Model for Misogyny Detection in Latin American Spanish Driven by Multisource Feature Extraction and Transformers

et al. 2021

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“…Studies Online detection [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [9], [39], [40], [41], [42], [43], [8], [44], [45], [46], [47], [48], [49] Offline detection [50], [51], [52], [53], [54], [7], [55], [56] Safety [57], [13], [58], [59], [60], [61],…”

Section: Categorymentioning

confidence: 99%

“…This is followed by image or video files (17.3%), signals (11.5%), voice recordings (1.9%), and user input (1.9%). Methods for data collection included the Twitter API [21], Facebook Graph API [33], Urban Dictionary API [30], existing datasets [24], manual retrieval of online data [28], or experiments [55]. • Features engineering: As seen in Fig.…”

Section: ) Answer To the Second Research Questionmentioning

confidence: 99%

A Systematic Review of Computer Science Solutions for Addressing Violence Against Women and Children

et al. 2021

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Violence against women and children are public health issues of pandemic proportions. It is estimated that one in every three women worldwide have experienced physical, emotional or sexual violence. Similarly, each year one of two children are victims of some form of violence, including domestic aggression and bullying. Due to the widespread use of the Internet and social media, women and children are also now vulnerable to other types of violence such as cyber-bullying and online sexual or emotional harassment. To help alleviate these social problem, the use of computer sciences and related technologies has been leveraged in recent years. The Internet of Things, artificial intelligence, ubiquitous and mobile computing, pattern recognition, cloud computing, and similar technologies have been used to formulate solutions to detect and prevent violent acts. In this paper, a systematic review of some of the efforts that can help address the problem of violence against women and children is presented. This paper describes the current state-of-the-art of these contributions and identifies trends, architectures, technologies, and current open challenges. The survey was developed using a literature review of academic documents published from 2010 to 2020. The contributions were categorized in four application domains: online detection, offline detection, safety and education. Additionally, these contributions were further categorized based on the computer science approaches and technologies used: artificial intelligence, Internet of Things and digital serious games.

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“…One problem is the lack of high-quality datasets to train machine learning models, which would enable the creation of efficient and scalable automated detection systems . Previous research has primarily used Twitter data and there is a pressing need for other platforms to be researched Lynn et al (2019a). Notably, de-spite social scientific studies that show online misogyny is pervasive on some Reddit communities, to date a training dataset for misogyny has not been created with Reddit data.…”

Section: Introductionmentioning

confidence: 99%

“…Lynn et al (2019b) provide a dataset of 2k Urban Dictionary definitions of which half are labelled as misogynistic. In Lynn et al (2019a) they show that deep learning techniques had greater accuracy in detecting misogyny than conventional machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

An Expert Annotated Dataset for the Detection of Online Misogyny

Guest¹,

Vidgen²,

Mittos³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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Online misogyny is a pernicious social problem that risks making online platforms toxic and unwelcoming to women. We present a new hierarchical taxonomy for online misogyny, as well as an expert labelled dataset to enable automatic classification of misogynistic content. The dataset consists of 6,567 labels for Reddit posts and comments. As previous research has found untrained crowdsourced annotators struggle with identifying misogyny, we hired and trained annotators and provided them with robust annotation guidelines. We report baseline classification performance on the binary classification task, achieving accuracy of 0.93 and F1 of 0.43. The codebook and datasets are made freely available for future researchers.

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A Comparison of Machine Learning Approaches for Detecting Misogynistic Speech in Urban Dictionary

Cited by 20 publications

References 42 publications

A Language Model for Misogyny Detection in Latin American Spanish Driven by Multisource Feature Extraction and Transformers

A Language Model for Misogyny Detection in Latin American Spanish Driven by Multisource Feature Extraction and Transformers

A Systematic Review of Computer Science Solutions for Addressing Violence Against Women and Children

An Expert Annotated Dataset for the Detection of Online Misogyny

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