Identifying and Avoiding Biased Terminology in Online Threat Descriptions
📜 Abstract
We present a novel method to identify biased terminology in online threat descriptions written in natural language. Our approach uses linguistic and statistical techniques to analyze large datasets of text descriptions, identifying common patterns of biased language. We then provide guidelines for generating unbiased descriptions that anyone can confidently trust. Our model helps mitigate potential biases in crowdsourced data and has substantial implications for improving data quality in many applications of machine learning and natural language processing.
✨ Summary
This research paper proposes a methodology for detecting biased terminology in online threat descriptions using natural language processing. The authors leverage linguistic and statistical techniques to analyze text from datasets, aiming to improve the quality of data used in machine learning applications by reducing bias in descriptions. Their work builds on existing theories of computational linguistics and text classification, focusing on the detection of biased language patterns and offering guidelines for creating unbiased content. Although initial searches yield limited direct citations of this paper, it contributes to the broader discourse on bias detection in natural language processing. It likely influences ongoing research by enhancing methodologies to ensure data quality and reliability, relevant to fields such as sentiment analysis, text classification, and computational social science. Similar works can be found in publications related to linguistic bias and bias in natural language processing modeling. No other specific influential references or broader industry applications were identified in this initial search.