Upload final WI-IAT 2026 ragebait classifier

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language:
  - ja
license: mit
library_name: transformers
pipeline_tag: text-classification
base_model: rinna/japanese-roberta-base
tags:
  - japanese
  - ragebait
  - social-media
  - binary-classification
model-index:
  - name: Japanese X Ragebait - Rinna RoBERTa Base
    results:
      - task:
          type: text-classification
          name: Binary text classification
        dataset:
          name: Japanese X Ragebait test split
          type: custom
        metrics:
          - type: accuracy
            value: 0.829
            name: Accuracy
          - type: precision
            value: 0.8309859154929577
            name: Precision (YES)
          - type: recall
            value: 0.826
            name: Recall (YES)
          - type: f1
            value: 0.8289984609861489
            name: Macro-F1

Japanese X Ragebait - Rinna RoBERTa Base

This is the Rinna RoBERTa component of the three-model ragebait detector from “From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X” (WI-IAT 2026). It is a binary Japanese text classifier fine-tuned from rinna/japanese-roberta-base.

Labels

NO (0): non-ragebait
YES (1): ragebait

The labels are research annotations and should not be treated as facts about a post or its author.

Training

Labeled split: 16,558 posts, balanced between YES and NO
Train/validation partition: 14,902 / 1,656
Held-out test split: 2,000 posts, balanced between YES and NO
Epochs: 3
Learning rate: 3e-5
Batch size: 32
Maximum sequence length: 256
Warmup ratio: 0.1
Weight decay: 0.01
Seed: 42
Best checkpoint criterion: validation Macro-F1

The public dataset repository contains only Post IDs and labels, not redistributed post text: ZhiyangQi/japanese-x-ragebait.

Test results

Metric	Value
Accuracy	82.90%
Precision (`YES`)	83.10%
Recall (`YES`)	82.60%
Macro-F1	82.90%

The confusion matrix is [[832, 168], [174, 826]], ordered as NO, YES. Full training history and unrounded values are in training_results.json.

Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_id = "ZhiyangQi97/japanese-x-ragebait-rinna-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "分類したい日本語テキスト"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
with torch.inference_mode():
    prediction = model(**inputs).logits.argmax(dim=-1).item()

print(model.config.id2label[prediction])

Install transformers, torch, and sentencepiece before loading the tokenizer.

Limitations and intended use

This model is intended for non-commercial academic research on Japanese ragebait. The training labels were created with an LLM-assisted pipeline and may contain errors. Ragebait is contextual and intent-based; performance may degrade on other periods, platforms, languages, or domains. Do not use this model to profile, rank, target, identify, harass, moderate, or make consequential decisions about individual users.

License

The base model is distributed under the MIT License. See the base model card for its terms and documentation.

Citation

@inproceedings{qi2026ragebait,
  title = {From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X},
  author = {Qi, Zhiyang and Ito, Kazuhiro and Chen, Jinghui and Nakamura, Hibiki and Chen, Zhangxuan and Murata, Erina and Chujyo, Masaki and Toriumi, Fujio},
  booktitle = {WI-IAT 2026},
  year = {2026}
}