The DOI matches its Crossref record.
Facebook Analytics, Advertising, and Marketing
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“However, there are a few mentions in the existing literature of the general effectiveness and limitations of LA.”
From Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting (2019) · cites this paper as [100]
In the citing paper
“Unfortunately, these studies do not provide any evidence on how this type of targeting influences the overall performance of ads.”
From Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting (2019) · cites this paper as [100]
Citations
Cites
Still being checked.
Cited by
Semeradova, T., & Weinlich, P. (2019) · IEEE Access · doi:10.1109/access.2019.2948401 · Cited by 39
“In 2014, Yu and Houg [100] discussed the potential of LA”
BackgroundInfluential
Metadata
- Authors
- Yu, D., & Houg, A.
- Corresponding author
- Yu, Dennis
- Venue
- Facebook Nation
- Publisher
- Springer New York
- Published
- 2014
- Pages
- 117-138
- OpenAlex
- W1909015
- Cited by
- 4
- References
- 0
- Citations by year
- 2021: 1 · 2020: 1 · 2019: 2
- FWCI
- 0.00
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Communication › Media and Digital Communication
- Social Sciences › Business, Management and Accounting › Business and International Management › SMEs Development and Digital Marketing
- Keywords
- Facebook advertising, social media marketing, content amplification, fan engagement, likes and comments, social media feedback
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 ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Facebook Analytics, Advertising, and Marketing. https://app.cochat.ai/papers/10.1007/978-1-4939-1740-2_6
- The paper
- Yu, D., & Houg, A. (2014). Facebook Analytics, Advertising, and Marketing. Facebook Nation. https://doi.org/10.1007/978-1-4939-1740-2_6
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.
