Understanding and Analyzing Inappropriately Targeting Language in Online Discourse: A Comparative Annotation Study

TL;DR

This study develops a comprehensive annotation framework combining human and AI inputs to detect inappropriately targeting language in online conversations, revealing the importance of context and uncovering new targeting categories.

Contribution

It introduces a novel multi-source annotation approach and provides a comparative analysis of human and AI methods for identifying nuanced hate speech.

Findings

Context significantly affects hate speech detection accuracy.

ChatGPT struggles with nuanced and subtle targeting language.

New categories like social belief and body image were identified.

Abstract

This paper introduces a method for detecting inappropriately targeting language in online conversations by integrating crowd and expert annotations with ChatGPT. We focus on English conversation threads from Reddit, examining comments that target individuals or groups. Our approach involves a comprehensive annotation framework that labels a diverse data set for various target categories and specific target words within the conversational context. We perform a comparative analysis of annotations from human experts, crowd annotators, and ChatGPT, revealing strengths and limitations of each method in recognizing both explicit hate speech and subtler discriminatory language. Our findings highlight the significant role of contextual factors in identifying hate speech and uncover new categories of targeting, such as social belief and body image. We also address the challenges and subjective judgments involved in annotation and the limitations of ChatGPT in grasping nuanced language. This study provides insights for improving automated content moderation strategies to enhance online safety and inclusivity.