nllg/arg_emo_fallacy

--- license: cc-by-4.0 task_categories: - text-classification - zero-shot-classification language: - en size_categories: - 1K<n<10K --- This dataset accompanies the paper [Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection](https://arxiv.org/abs/2510.09695). It includes annotations for logical fallacy labels, emotion categories, and argument convincingness ratings. Please refer to the paper and its [repository](https://github.com/NL2G/EMCONA-UTN/tree/main/emotion_fallacy) for more details. If you use this dataset, please include the following citation： ``` @misc{chen2026emotionallychargedlogicallyblurred, title={Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection}, author={Yanran Chen and Lynn Greschner and Roman Klinger and Michael Klenk and Steffen Eger}, year={2026}, eprint={2510.09695}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2510.09695}, } ``` Relevant columns in the `.tsv` file: | Column | Description | Notes | |---|---|---| | `id_ori` | ID of the original argument (source item). | All rows derived from the same original argument share the same `id_ori`. | | `id_gen` | ID of the variant within an original argument group. | Values range from **-1 to 3** (**-1** = original argument; **0–3** = synthetic variants). | | `batch` | Annotation batch ID. | Values: **1–20**. | | `model_gen` | Model used to generate the synthetic argument. | For original arguments (`id_gen = -1`), this is `"N/A"`. | | `strategy_gen` | Emotional framing strategy used for synthetic generation. | | | `emotion_gen` | Target emotion specified for synthetic generation. | | | `fallacy_gold` | Gold fallacy label from the original dataset. | All variants from the same original argument share this label. | | `argument` | Argument text (original or synthetic). | | | `claim` | Claim associated with the argument (generated by LLMs). | All variants from the same original argument share the same claim. | | `emo_0`, `emo_1`, `emo_2` | Emotion labels from annotators 0/1/2. | | | `fallacy_0`, `fallacy_1`, `fallacy_2` | Fallacy labels from annotators 0/1/2. | | | `conv_0`, `conv_1`, `conv_2` | Convincingness ratings from annotators 0/1/2. | If an annotator judged the claim does **not** match the argument, they skip this rating and the dataset uses the placeholder value **`100`**. | | `conv_zscore_0`, `conv_zscore_1`, `conv_zscore_2` | Z-score–normalized convincingness ratings per annotator. | Same missing/skip rule as above: **`100`** indicates “not annotated”. | | `emo_best_annotator`, `fallacy_best_annotator`, `conv_best_annotator` | Annotator ID with the highest agreement within a batch. | Values: **0, 1, 2**. | | `emo_final`, `fallacy_final`, `conv_final`| Final labels. | Majority vote + best-annotator rule / average (as defined in the paper). |--- 许可协议：CC BY 4.0（Creative Commons Attribution 4.0 International） 任务类别： - 文本分类 - 零样本分类（Zero-shot Classification） 语言： - 英语 样本规模： - 1000 < 样本数 < 10000 --- 本数据集配套论文《情感裹挟，逻辑失准：AI驱动的情感框架削弱人类谬误检测能力》（原标题：Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection，链接：https://arxiv.org/abs/2510.09695）。数据集包含逻辑谬误标签、情感类别以及论证说服力评分的标注数据。更多细节请参阅该论文及其配套代码仓库（链接：https://github.com/NL2G/EMCONA-UTN/tree/main/emotion_fallacy）。 若使用本数据集，请引用如下文献： @misc{chen2026emotionallychargedlogicallyblurred, title={Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection}, author={Yanran Chen and Lynn Greschner and Roman Klinger and Michael Klenk and Steffen Eger}, year={2026}, eprint={2510.09695}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2510.09695}, } `.tsv`格式文件中的相关字段如下： | 字段名 | 字段说明 | 备注 | |---|---|---| | `id_ori` | 原始论证（源条目）的ID | 同一原始论证衍生的所有行均共享相同的`id_ori` | | `id_gen` | 原始论证组内变体的ID | 取值范围为**-1至3**（**-1**代表原始论证；**0至3**代表合成变体） | | `batch` | 标注批次ID | 取值范围为**1至20** | | `model_gen` | 用于生成合成论证的模型 | 针对原始论证（`id_gen = -1`），该字段值为`"N/A"` | | `strategy_gen` | 合成生成过程中使用的情感框架策略 | | | `emotion_gen` | 合成生成时指定的目标情感 | | | `fallacy_gold` | 原始数据集中的金标准谬误标签 | 同一原始论证的所有变体均共享该标签 | | `argument` | 论证文本（可为原始论证或合成论证） | | | `claim` | 与该论证关联的主张（由大语言模型（LLM）生成） | 同一原始论证的所有变体均共享相同的主张 | | `emo_0`, `emo_1`, `emo_2` | 分别来自标注者0、1、2的情感标签 | | | `fallacy_0`, `fallacy_1`, `fallacy_2` | 分别来自标注者0、1、2的谬误标签 | | | `conv_0`, `conv_1`, `conv_2` | 分别来自标注者0、1、2的说服力评分 | 若标注者判定主张与论证不匹配，则跳过该评分，数据集将使用占位符值**`100`** | | `conv_zscore_0`, `conv_zscore_1`, `conv_zscore_2` | 经Z分数标准化后的各标注者的说服力评分 | 遵循与上述一致的缺失/跳过规则：**`100`**代表“未标注” | | `emo_best_annotator`, `fallacy_best_annotator`, `conv_best_annotator` | 某一批次内一致性最高的标注者ID | 取值为**0、1、2** | | `emo_final`, `fallacy_final`, `conv_final` | 最终标签 | 采用多数投票+最优标注者规则/平均值计算（详见论文中的定义） |