The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations
Open accessAbstract
Targeted advertising has been subject to many privacy complaints from both users and policy makers.Despite this attention, users still have little understanding of what data the advertising platforms have about them and why they are shown particular ads.To address such concerns, Facebook recently introduced two transparency mechanisms: a "Why am I seeing this?" button that provides users with an explanation of why they were shown a particular ad (ad explanations), and an Ad Preferences Page that provides users with a list of attributes Facebook has inferred about them and how (data explanations).In this paper, we investigate the level of transparency provided by these two mechanisms.We first define a number of key properties of explanations and then evaluate empirically whether Facebook's explanations satisfy them.For our experiments, we develop a browser extension that collects the ads users receive every time they browse Facebook, their respective explanations, and the attributes listed on the Ad Preferences Page; we then use controlled experiments where we create our own ad campaigns and target the users that installed our extension.Our results show that ad explanations are often incomplete and sometimes misleading while data explanations are often incomplete and vague.Taken together, our findings have significant implications for users, policy makers, and regulators as social media advertising services mature.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
Goodman, B., & Flaxman, S. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2741 · Cited by 2,131
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766
Datta, A., Sen, S., & Zick, Y. (2016) · 2016 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2016.42 · Cited by 728
MyAdChoices: Bringing Transparency and Control to Online Advertising
Parra-Arnau, J., Achara, J. P., & Castelluccia, C. (2016) · arXiv · doi:10.48550/arxiv.1602.02046 · Cited by 20
Cited by
Deep Learning With Edge Computing: A Review
Chen, J., & Ran, X. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2921977 · Cited by 1,540
A Survey of Deep Learning Methods for Cyber Security
Berman, D. S., Buczak, A. L., Chavis, J. S., & Corbett, C. L. (2019) · Information · doi:10.3390/info10040122 · Cited by 569
Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020) · Electronics · doi:10.3390/electronics9071177 · Cited by 378
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342
Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder
Zavrak, S., & Iskefiyeli, M. (2020) · IEEE Access · doi:10.1109/access.2020.3001350 · Cited by 322
Metadata
- Authors
- Andreou, A., Venkatadri, G., Goga, O., Gummadi, K. P., Loiseau, P., & Mislove, A.
- Venue
- Proceedings 2018 Network and Distributed System Security Symposium
- Publisher
- Internet Society
- Published
- 2018
- OpenAlex
- W2793294490
- Cited by
- 1,477
- References
- 8
- FWCI
- 291.55
- Type
- conference paper
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations. https://app.cochat.ai/papers/10.14722/ndss.2018.23191
- The paper
- Andreou, A., Venkatadri, G., Goga, O., Gummadi, K. P., Loiseau, P., & Mislove, A. (2018). Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations. Proceedings 2018 Network and Distributed System Security Symposium. https://doi.org/10.14722/ndss.2018.23191
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.