Conference proceeding
Accurate Detection of Automatically Spun Content via Stylometric Analysis
2017 IEEE International Conference on Data Mining (ICDM), Vol.2017-, pp.425-434
11/2017
DOI: 10.1109/ICDM.2017.52
Abstract
Spammers use automated content spinning techniques to evade plagiarism detection by search engines. Text spinners help spammers in evading plagiarism detectors by automatically restructuring sentences and replacing words or phrases with their synonyms. Prior work on spun content detection relies on the knowledge about the dictionary used by the text spinning software. In this work, we propose an approach to detect spun content and its seed without needing the text spinner's dictionary. Our key idea is that text spinners introduce stylometric artifacts that can be leveraged for detecting spun documents. We implement and evaluate our proposed approach on a corpus of spun documents that are generated using a popular text spinning software. The results show that our approach can not only accurately detect whether a document is spun but also identify its source (or seed) document - all without needing the dictionary used by the text spinner.
Details
- Title: Subtitle
- Accurate Detection of Automatically Spun Content via Stylometric Analysis
- Creators
- Usman ShahidShehroze FarooqiRaza AhmadZubair ShafiqPadmini SrinivasanFareed Zaffar
- Resource Type
- Conference proceeding
- Publication Details
- 2017 IEEE International Conference on Data Mining (ICDM), Vol.2017-, pp.425-434
- Publisher
- IEEE
- DOI
- 10.1109/ICDM.2017.52
- ISSN
- 1550-4786
- eISSN
- 2374-8486
- Language
- English
- Date published
- 11/2017
- Academic Unit
- Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984003016202771
Metrics
7 Record Views