---
title: "wangkongqiang@EEUCA 2026 — paper by Wang et al. (2026)"
description: "This system paper describes a team's entry in the EEUCA 2026 shared task on detecting toxic intent in… 0.5776 accuracy. 19 references."
authors: ["Kongqiang Wang", "Peng Zhang", "Quingli Tan"]
year: 2026
venue: "Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)"
source: "https://aclanthology.org/2026.eeuca-1.11/"
doi: "10.18653/v1/2026.eeuca-1.11"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2026-wangkongqiang-eeuca-2026-understanding-toxic/"
---
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# wangkongqiang@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs

**Authors:** [Kongqiang Wang](https://md.research.elamur.ai/free/en/authors/kongqiang-wang/), [Peng Zhang](https://md.research.elamur.ai/free/en/authors/peng-zhang/) and [Quingli Tan](https://md.research.elamur.ai/free/en/authors/quingli-tan/) · **Year:** 2026 · **Venue:** [Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)](https://md.research.elamur.ai/free/en/venues/eeuca/) ([Workshop on Event Extraction and Understanding: Challenges and Applications 2026](https://md.research.elamur.ai/free/en/venues/eeuca/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eeuca-1.11/) · [PDF](https://aclanthology.org/2026.eeuca-1.11.pdf) · [DOI 10.18653/v1/2026.eeuca-1.11](https://doi.org/10.18653/v1/2026.eeuca-1.11) · [OpenAlex](https://openalex.org/W7166801212)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
This system paper describes a team's entry in the EEUCA 2026 shared task on detecting toxic intent in multiplayer game chat. Every player message is classified into one of six categories: a benign Non-toxic class or one of five toxic classes, namely Threats, Extremism, Hate and Harassment, Insults and Flaming, and Other Offensive Texts. The data come from the GameTox dataset (NAACL 2025), which contains World of Tanks chat. Of 53,000 collected utterances, 42,963 English ones carry intent labels, and the label distribution is heavily skewed toward non-toxic messages.

The authors compared two approaches:
- **Encoder models:** fine-tuned classifiers (ALBERT, BERT, ERNIE 2.0, RoBERTa variants), some with RNN, CNN or LSTM heads, plus a hard-voting ensemble.
- **Instruction-tuned LLMs:** fine-tuned Qwen2 at the 1.5B and 7B sizes.

The Qwen2 7B model clearly outperformed the encoder baselines, reaching a test Macro F1 of 0.5776 and placing 8th. The work shows that LLM fine-tuning helps with rare toxic categories under strong imbalance. Even so, the task remains hard, because the strongest team scored about 0.70 Macro F1.

## Key points
- **Dataset:** GameTox (World of Tanks chat logs). Non-toxic messages make up about 80.7% of labeled utterances, while Threats has only 53 examples and Extremism only 23.
- **Best submission:** fine-tuned Qwen2 7B, with test Macro F1 0.5776, Accuracy 0.9075, Macro Precision 0.6847 and Macro Recall 0.5343. It ranked 8th of 35 teams on the leaderboard.
- **Encoder baselines:** these stayed far behind. The best was simcse-roberta-lstm at Macro F1 0.4234, followed by bert-base-uncased at 0.4205. Qwen2 1.5B reached 0.4525.
- **Ensemble:** the hard-voting bagging ensemble of four RoBERTa-based models did not improve Macro F1, scoring 0.372.
- **Stated limitations:** the system ignores conversational context, and limited compute restricted the authors to the 7B model. The code is available on GitHub.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Yunnan University](https://md.research.elamur.ai/free/en/universities/yunnan-university/) — University, Kunming, China · 20 works · 40 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,954 works · [🤝 Communication](https://md.research.elamur.ai/free/en/shelves/social-sciences/communication/) · 104 works

🧭 Topics (1): [🛡️ Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · 587 works

## Cite

Cite as: Kongqiang Wang, Peng Zhang and Quingli Tan. 2026. wangkongqiang@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs. Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2026wangkongqiang,
  title = {{wangkongqiang@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs}},
  author = {Kongqiang Wang, Peng Zhang and Quingli Tan},
  booktitle = {Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.eeuca-1.11},
  url = {https://doi.org/10.18653/v1/2026.eeuca-1.11}
}
```

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- [next → WaveDetect: Robust Framework for Machine-Generated Text Detection via Wavelet Transform](https://md.research.elamur.ai/free/en/papers/liu-2026-wavedetect-robust-framework-machine-generated/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/55/)

## References

19 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*