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Code and data belonging to our CSCW 2018 paper: "Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest".
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crawler
disclosure-analysis
user-study
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README.md
affiliate_markting_links.txt

README.md

Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest

This is a release of the data and code for the research paper "Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest". The paper will appear at the ACM Computer Supported Collaborative Work and Social Computing (CSCW 2018) conference.

Authors: Arunesh Mathur, Arvind Narayanan, Marshini Chetty

Paper: Available on arXiv and the ACM Digital Library

Blog Post: Available on Medium

Overview

The repository has three primary components:

  • crawler/: Contains the YouTube and Pinterest datasets, along with the code used to sample them
  • disclosure-analysis/: Contains the code to extract disclosures from the affiliate marketing content in YouTube and Pinterest
  • user-study/: Contains data from the YouTube and Pinterest experiments, along with the code to run the statistical analyses
  • affiliate_markting_links.txt: Contains Adblock Plus-style filters corresponding to the affiliate marketing URLs we discovered

Please navigate to each directory for a more detailed explanation.

Citation

Please use the following BibTeX to cite our paper:

@article{Mathur2018Endorsements,
author = {Mathur, Arunesh and Narayanan, Arvind and Chetty, Marshini},
title = {Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest},
journal = {Proc. ACM Hum.-Comput. Interact.},
issue_date = {November 2018},
volume = {2},
number = {CSCW},
year = {2018},
issn = {2573-0142},
pages = {119:1--119:26},
articleno = {119},
numpages = {26},
url = {http://doi.acm.org/10.1145/3274388},
doi = {10.1145/3274388},
acmid = {3274388},
publisher = {ACM},
address = {New York, NY, USA},
}

Funding

This research was funded by NSF grant CNS-1664786.

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