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End of preview. Expand
in Dataset Viewer.
ShopTC-100K Dataset
The ShopTC-100K dataset is collected using TermMiner, an open-source data collection and topic modeling pipeline introduced in the paper:
If you find this dataset or the related paper useful for your research, please cite our paper:
@inproceedings{tsai2025harmful,
author = {Elisa Tsai and Neal Mangaokar and Boyuan Zheng and Haizhong Zheng and Atul Prakash},
title = {Harmful Terms and Where to Find Them: Measuring and Modeling Unfavorable Financial Terms and Conditions in Shopping Websites at Scale},
booktitle = {Proceedings of the ACM Web Conference 2025 (WWW ’25)},
year = {2025},
location = {Sydney, NSW, Australia},
publisher = {ACM},
address = {New York, NY, USA},
pages = {14},
month = {April 28-May 2},
doi = {10.1145/3696410.3714573}
}
Dataset Description
The dataset consists of sanitized terms extracted from 8,251 e-commerce websites with English-language terms and conditions. The websites were sourced from the Tranco list (as of April 2024). The dataset contains:
- 1,825,231 sanitized sentences
- 7,777 unique websites
- Four split files for ease of use:
ShopTC-100K
├── sanitized_split1.csv
├── sanitized_split2.csv
├── sanitized_split3.csv
├── sanitized_split4.csv
Data Sanitization Process
The extracted terms are cleaned and structured using a multi-step sanitization pipeline:
- HTML Parsing: Raw HTML content is processed to extract text from
<p>tags. - Sentence Tokenization: Text is split into sentences using a transformer-based tokenization model.
- Filtering: Short sentences (<10 words) and duplicates are removed.
- Preprocessing: Newline characters and extra whitespace are cleaned.
| Split File | Rows | Columns | Unique Websites |
|---|---|---|---|
| sanitized_split1.csv | 523,760 | 2 | 1,979 |
| sanitized_split2.csv | 454,966 | 2 | 1,973 |
| sanitized_split3.csv | 425,028 | 2 | 1,988 |
| sanitized_split4.csv | 421,477 | 2 | 1,837 |
Example Data
The dataset is structured as follows:
| URL | Paragraph |
|---|---|
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