Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models

Narayanan, Venkit, Pranav; Wilson, Shomir

doi:10.48550/arxiv.2111.13259

Cited by 3 publications

(3 citation statements)

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“…Detection of toxicity can diverge across different detection systems. Although the research by Venkit and Wilson (2021) focused on individuals with disabilities, a secondary discovery they made in their research was that different systems (DistilBERT, Perspective [Google API], TextBlob, and VADER) produced different levels of sentiment score for identical statements. Their paper provides some support for the ideas of others to build consensus systems of automated detection of toxicity, but also highlights limitations shared across all those systems to prioritize and overestimate the toxicity of certain terms without sensitivity to context.…”

Section: Measuring and Detecting Toxicitymentioning

confidence: 99%

Measuring Toxicity Toward Women in Game-Based Communities

Belskie,

Zhang,

Hemminger

2023

Journal of Electronic Gaming and Esports

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Prior research into gaming toxicity in game-specific Reddit communities nearly always considers toxicity in aggregate, and so provides very few clues for a valid coding scheme for isolating toxic language and triggers that specifically target women gamers. Existing research offers a starting place for devising valid methods for measuring and detecting toxic language and toxic triggers within specified data sets, but that research is less useful is its applicability to game-related forms of toxicity targeting women gamers. Where this research had originally hoped to develop an automated method for scoring, limitations with automated detection of toxicity discussed within the paper prompted a shift to what the authors identify as a key intermediate step—better accuracy in toxicity detection by automated means—that will contribute to future achievements in reducing toxicity toward women and other targeted groups in gaming communities. This paper is intended to aid projects that aim to incrementally improve our understanding of toxicity toward women in games and game communities and how to effectively measure it. The conclusion of this research ultimately hopes to contribute to providing information to inform policies that create a safer and more respectful gaming environment for all gamers.

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Section: Measuring and Detecting Toxicitymentioning

confidence: 99%

Measuring Toxicity Toward Women in Game-Based Communities

Belskie,

Zhang,

Hemminger

2023

Journal of Electronic Gaming and Esports

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“…A disability is one of the big concerns that continues to cause difficulties. Disability remains a source of frustration; meanwhile, it is viewed as a limitation, a cognitive handicap, mental and physical, that limits the individual's growth and involvement (Venkit and Wilson, 2021). Consequently, excessive efforts are given in eradicating this type of restriction.…”

Section: Introductionmentioning

confidence: 99%

“…This FastText words embedding method supports this method. Venkit and Wilson (2021) have addressed the discrimination against persons with disability, PWD, performed by SA, and toxicity classifier methods. The authors offer an inspection of sentiments and toxicity analysis approaches to comprehend briefly how they distinguish PWD.…”

Section: Introductionmentioning

confidence: 99%

Textual Emotion Analysis-based Disabled People Talking Using Improved Metaheuristics with Deep Learning Techniques for Intelligent Systems

Alshahrani,

Yaseen,

Drar

2023

Journal of Disability Research

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Due to the complexity of generalizing and modeling the series of brain signals, detecting emotions in people with sensory disabilities still continues to be challenging. Hence, brain–computer interface technology was used to study the emotions and behavior of people based on brain signals. Emotion analysis is a widely used and robust data mining analysis method. It provides an excellent opportunity to monitor, evaluate, determine, and understand the sentiments of consumers with respect to a product or a service. Yet, a recognition model of emotions in people with visual disabilities has not been evaluated, even though previous studies have already proposed the classification of emotions in people with sensory disabilities using machine learning approaches. Therefore, this study introduces a new salp swarm algorithm with deep recurrent neural network-based textual emotion analysis (SSADRNN-TEA) technique for disabled persons. The major intention of the SSADRNN-TEA technique was to focus on the detection and classification of emotions that exist in social media content. In this work, the SSADRNN-TEA technique undergoes preprocessing to make the input data compatible with the latter stages of processing and BERT word embedding process is applied. Moreover, deep recurrent neural network (DRNN) model is exploited. Finally, SSA is exploited for the optimal adjustment of the DRNN hyperparameters. A widespread experiment is involved in simulating the real-time performance of the SSADRNN-TEA method. The experimental values revealed the improved performance of the SSADRNN-TEA technique in terms of several evaluation metrics.

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A Preliminary Cross-Language Investigation of Empathy in # Disability Discourse on Twitter

AlMeraj

Husain

2023

Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments

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Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models

Cited by 3 publications

References 0 publications

Measuring Toxicity Toward Women in Game-Based Communities

Measuring Toxicity Toward Women in Game-Based Communities

Textual Emotion Analysis-based Disabled People Talking Using Improved Metaheuristics with Deep Learning Techniques for Intelligent Systems

A Preliminary Cross-Language Investigation of Empathy in # Disability Discourse on Twitter

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