searchsim/cognitive-traces-stackoverflow

---
license: cc-by-4.0
task_categories:
- text-classification
- token-classification
language:
- en
tags:
- information-retrieval
- user-simulation
- cognitive-modeling
- information-foraging-theory
- search-logs
pretty_name: "Cognitive Traces — Stack Overflow"
size_categories:
- 100K<n<1M
---

# Cognitive Traces — Stack Overflow

## Dataset Description

This dataset contains **cognitive trace annotations** for the Stack Overflow dataset, produced by the multi-agent annotation framework described in:

> **Beyond the Click: A Framework for Inferring Cognitive Traces in Search**
> Saber Zerhoudi, Michael Granitzer. ECIR 2026.

Each user event (question, answer, comment, edit, vote) is annotated with a cognitive label from **Information Foraging Theory (IFT)**, along with the full annotation chain (analyst, critic, judge) and confidence scores.

## Dataset Statistics

| Metric | Value |
|--------|-------|
| Sessions | 18,629 |
| Events | 175,326 |
| Action Types | COMMENT, POST_ANSWER, POST_QUESTION, EDIT_INITIAL_BODY, EDIT_INITIAL_TAGS, EDIT_INITIAL_TITLE, EDIT_BODY, EDIT_TAGS, EDIT_TITLE, VOTE_BOUNTY_START, VOTE_UP (11) |
| Cognitive Labels | 6 (FollowingScent, ApproachingSource, ForagingSuccess, DietEnrichment, PoorScent, LeavingPatch) |

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("searchsim/cognitive-traces-stackoverflow")

# Access the data
print(ds["train"][0])

# Filter by cognitive label
struggling = ds["train"].filter(lambda x: x["cognitive_label"] == "PoorScent")
print(f"Events with PoorScent: {len(struggling)}")

# Get all events for a session
session = ds["train"].filter(lambda x: x["session_id"] == "so_session_1543")
for event in session:
print(f" {event["action_type"]}: {event["cognitive_label"]}")
```

## Column Schema

| Column | Type | Description |
|--------|------|-------------|
| `session_id` | string | Unique session identifier |
| `event_id` | string | Unique event identifier |
| `event_timestamp` | string | ISO timestamp |
| `action_type` | string | User action type (11 types, see above) |
| `content` | string | Event content (question body, answer text, comment, etc.) |
| `cognitive_label` | string | Final IFT cognitive label |
| `analyst_label` | string | Analyst agent's proposed label |
| `analyst_justification` | string | Analyst's reasoning |
| `critic_label` | string | Critic agent's proposed label |
| `critic_agreement` | string | Whether Critic agreed with Analyst |
| `critic_justification` | string | Critic's reasoning |
| `judge_justification` | string | Judge's final decision reasoning |
| `confidence_score` | float | Framework confidence (0–1) |
| `disagreement_score` | float | Analyst–Critic disagreement (0–1) |
| `flagged_for_review` | bool | Whether flagged for human review |
| `pipeline_mode` | string | Annotation pipeline mode |

## IFT Cognitive Labels

| Label | IFT Concept | Interpretation |
|-------|-------------|----------------|
| FollowingScent | Information scent following | User pursuing a promising trail |
| ApproachingSource | Source approaching | User converging on target information |
| ForagingSuccess | Successful foraging | User found desired information |
| DietEnrichment | Diet enrichment | User broadening information intake |
| PoorScent | Poor information scent | Trail quality deteriorating |
| LeavingPatch | Patch leaving | User abandoning current direction |

## Source Dataset

Based on the Stack Overflow Data Dump (Stack Exchange). Contains questions, answers, comments, edits, and votes from the Stack Overflow Q&A platform.

## Citation

```bibtex
@inproceedings{zerhoudi2026beyond,
title={Beyond the Click: A Framework for Inferring Cognitive Traces in Search},
author={Zerhoudi, Saber and Granitzer, Michael},
booktitle={Proceedings of the 48th European Conference on Information Retrieval (ECIR)},
year={2026}
}
```

## License

CC-BY-4.0. The cognitive annotations are released under Creative Commons Attribution 4.0. The underlying source datasets have their own licenses — please refer to the original dataset providers.

## Links

- [Paper](https://traces.searchsim.org/)
- [GitHub Repository](https://github.com/searchsim-org/cognitive-traces)
- [Annotation Tool](https://github.com/searchsim-org/cognitive-traces)

license: CC BY 4.0
task_categories:
- 文本分类（text-classification）
- 令牌分类（token-classification）
language:
- en
tags:
- 信息检索（information-retrieval）
- 用户模拟（user-simulation）
- 认知建模（cognitive-modeling）
- 信息觅食理论（information-foraging-theory）
- 搜索日志（search-logs）
pretty_name: "认知痕迹 — Stack Overflow"
size_categories:
- 100K<n<1M

# 认知痕迹 — Stack Overflow

## 数据集说明

本数据集为Stack Overflow数据集附带**认知痕迹标注（cognitive trace annotations）**，由下述文献中提及的多智能体标注框架生成：

> **《超越点击：搜索中认知痕迹的推断框架》**
> Saber Zerhoudi、Michael Granitzer，发表于ECIR 2026。

每个用户事件（包括提问、回答、评论、编辑、投票）均被标注了来自**信息觅食理论（Information Foraging Theory，IFT）**的认知标签，并附带完整的标注链（分析师、评审员、评判员）与置信度分数。

## 数据集统计

| 指标 | 数值 |
|--------|-------|
| 会话数 | 18,629 |
| 事件数 | 175,326 |
| 动作类型 | COMMENT（评论）、POST_ANSWER（提交回答）、POST_QUESTION（提交提问）、EDIT_INITIAL_BODY（初始正文编辑）、EDIT_INITIAL_TAGS（初始标签编辑）、EDIT_INITIAL_TITLE（初始标题编辑）、EDIT_BODY（正文编辑）、EDIT_TAGS（标签编辑）、EDIT_TITLE（标题编辑）、VOTE_BOUNTY_START（悬赏发起投票）、VOTE_UP（点赞），共11种 |
| 认知标签 | 6种（FollowingScent、ApproachingSource、ForagingSuccess、DietEnrichment、PoorScent、LeavingPatch） |

## 快速上手

python
from datasets import load_dataset

ds = load_dataset("searchsim/cognitive-traces-stackoverflow")

# 访问数据
print(ds["train"][0])

# 按认知标签筛选
struggling = ds["train"].filter(lambda x: x["cognitive_label"] == "PoorScent")
print(f"Events with PoorScent: {len(struggling)}")

# 获取单个会话的所有事件
session = ds["train"].filter(lambda x: x["session_id"] == "so_session_1543")
for event in session:
print(f" {event["action_type"]}: {event["cognitive_label"]}")

## 列结构

| 列名 | 数据类型 | 描述 |
|--------|------|-------------|
| `session_id` | 字符串 | 唯一会话标识符 |
| `event_id` | 字符串 | 唯一事件标识符 |
| `event_timestamp` | 字符串 | ISO格式时间戳 |
| `action_type` | 字符串 | 用户动作类型（共11种，详见上文） |
| `content` | 字符串 | 事件内容（提问正文、回答文本、评论等） |
| `cognitive_label` | 字符串 | 最终IFT认知标签 |
| `analyst_label` | 字符串 | 分析师智能体提出的标签 |
| `analyst_justification` | 字符串 | 分析师的标注理由 |
| `critic_label` | 字符串 | 评审员智能体提出的标签 |
| `critic_agreement` | 字符串 | 评审员是否同意分析师的标注 |
| `critic_justification` | 字符串 | 评审员的标注理由 |
| `judge_justification` | 字符串 | 评判员的最终决策理由 |
| `confidence_score` | 浮点型 | 框架置信度（范围0–1） |
| `disagreement_score` | 浮点型 | 分析师与评审员的分歧度（范围0–1） |
| `flagged_for_review` | 布尔型 | 是否标记为需人工审核 |
| `pipeline_mode` | 字符串 | 标注流水线模式 |

## IFT认知标签

| 标签 | IFT概念 | 含义阐释 |
|-------|-------------|----------------|
| FollowingScent | 信息追踪（Information scent following） | 用户沿优质线索展开探索 |
| ApproachingSource | 逼近信息源（Source approaching） | 用户逐步定位目标信息 |
| ForagingSuccess | 觅食成功（Successful foraging） | 用户成功获取所需信息 |
| DietEnrichment | 拓展信息摄入（Diet enrichment） | 用户拓宽信息获取范围 |
| PoorScent | 线索质量低下（Poor information scent） | 探索线索的质量下降 |
| LeavingPatch | 放弃当前路径（Patch leaving） | 用户终止当前探索方向 |

## 源数据集

本数据集基于Stack Overflow数据转储（Stack Exchange），包含Stack Overflow问答平台的提问、回答、评论、编辑记录与投票数据。

## 引用

bibtex
@inproceedings{zerhoudi2026beyond,
title={Beyond the Click: A Framework for Inferring Cognitive Traces in Search},
author={Zerhoudi, Saber and Granitzer, Michael},
booktitle={Proceedings of the 48th European Conference on Information Retrieval (ECIR)},
year={2026}
}

## 许可证

CC BY 4.0。本数据集的认知标注采用知识共享署名4.0协议发布。底层源数据集拥有各自的许可证，请参考原始数据集提供者的相关说明。

## 相关链接

- [论文](https://traces.searchsim.org/)
- [GitHub仓库](https://github.com/searchsim-org/cognitive-traces)
- [标注工具](https://github.com/searchsim-org/cognitive-traces)