Work

FNLP412@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs using Transfer Learning and Synthetic Data Augmentation

Year: 2026

Type: conference-paper

Abstract: Our paper explores several machine learning methods for detecting toxic language in gaming-related chat utterances.We start with the GameTox dataset, perform some data preprocessing and augment the minority classes with LLM-generated synthetic data.We then set a baseline using a classic Logistic Regression model and continue to explore several approaches to surpassing it, by leveraging the leading multilingual transformer models (XLM-RoBERTa and DeBERTa-V3) to classify our test data.We achieve a top result of 0.6725 Macro-F1 (2 nd place on shared task leaderboard) using a MDeBERTa-V3 model which we pretrained on the Jigsaw dataset for 1 epoch and then fine-tuned on our GameTox data for 5 epochs.

Source: Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA)

DOI: 10.18653/v1/2026.eeuca-1.10

Author: Mihai Radu Radulescu

Institution: University of Bucharest

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7166836336