The DOI matches its Crossref record.
Proceedings of the Web Conference 2021
Abstract
By linking to external websites, Wikipedia can act as a gateway to the Web.To date, however, little is known about the amount of traffic generated by Wikipedia's external links.We fill this gap in a detailed analysis of usage logs gathered from Wikipedia users' client devices.Our analysis proceeds in three steps: First, we quantify the level of engagement with external links, finding that, in one month, English Wikipedia generated 43M clicks to external websites, in roughly even parts via links in infoboxes, cited references, and article bodies.Official links listed in infoboxes have by far the highest click-through rate (CTR), 2.47% on average.In particular, official links associated with articles about businesses, educational institutions, and websites have the highest CTR, whereas official links associated with articles about geographical content, television, and music have the lowest CTR.Second, we investigate patterns of engagement with external links, finding that Wikipedia frequently serves as a stepping stone between search engines and third-party websites, effectively fulfilling information needs that search engines do not meet.Third, we quantify the hypothetical economic value of the clicks received by external websites from English Wikipedia, by estimating that the respective website owners would need to pay a total of $7-13 million per month to obtain the same volume of traffic via sponsored search.Overall, these findings shed light on Wikipedia's role not only as an important source of information, but also as a high-traffic gateway to the broader Web ecosystem.
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
Cited by
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
Automation of systematic literature reviews: A systematic literature review
van Dinter, R., Tekinerdogan, B., & Catal, C. (2021) · Information and Software Technology · doi:10.1016/j.infsof.2021.106589 · Cited by 432
Hadi Mogavi, R., Deng, C., Juho Kim, J., Zhou, P., D. Kwon, Y., Hosny Saleh Metwally, A., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S., et al. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100027 · Cited by 369
A Survey on Deep Hashing Methods
Luo, X., Wang, H., Wu, D., Chen, C., Deng, M., Huang, J., & Hua, X.-S. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532624 · Cited by 241
Toward better drug discovery with knowledge graph
Zeng, X., Tu, X., Liu, Y., Fu, X., & Su, Y. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.09.003 · Cited by 229
Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs
Chen, Z., Mao, H., Li, H., Jin, W., Wen, H., Wei, X., Wang, S., Yin, D., Fan, W., Liu, H., et al. (2024) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3655103.3655110 · Cited by 209
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., & Liu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01606-8 · Cited by 191
Himeur, Y., Sohail, S. S., Bensaali, F., Amira, A., & Alazab, M. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102746 · Cited by 139
Evolving techniques in cyber threat hunting: A systematic review
Mahboubi, A., Luong, K., Aboutorab, H., Bui, H. T., Jarrad, G., Bahutair, M., Camtepe, S., Pogrebna, G., Ahmed, E., Barry, B., et al. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.104004 · Cited by 94
A/B testing: A systematic literature review
Quin, F., Weyns, D., Galster, M., & Silva, C. C. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112011 · Cited by 83
Liu, J., Capurro, D., Nguyen, A., & Verspoor, K. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104466 · Cited by 66
Deepfake: definitions, performance metrics and standards, datasets, and a meta-review
Altuncu, E., Franqueira, V. N. L., & Li, S. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1400024 · Cited by 134
Session-based cyberbullying detection in social media: A survey
Yi, P., & Zubiaga, A. (2023) · Online Social Networks and Media · doi:10.1016/j.osnem.2023.100250 · Cited by 55
Learning towards conversational AI: A survey
Fu, T., Gao, S., Zhao, X., Wen, J.-R., & Yan, R. (2022) · AI Open · doi:10.1016/j.aiopen.2022.02.001 · Cited by 46
Horizontal Federated Recommender System: A Survey
Wang, L., Zhou, H., Bao, Y., Yan, X., Shen, G., & Kong, X. (2024) · ACM Computing Surveys · doi:10.1145/3656165 · Cited by 37
Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives
Fu, Z., Niu, X., & Maher, M. L. (2024) · ACM Computing Surveys · doi:10.1145/3605145 · Cited by 30
Pan, Q., Li, S., Zheng, T., Ma, F., & Huang, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123227 · Cited by 7
Chen, T., Ren, X., Lai, J., Tan, H., Liu, F., & Chan, W. K. V. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114389 · Cited by 3
(2024) · doi:10.1145/3630106.3659004 · Cited by 3
Examining Human-AI Collaboration for Co-Writing Constructive Comments Online
Shahid, F., Dittgen, M., Naaman, M., & Vashistha, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757591 · Cited by 3
Metadata
- Publisher
- ACM
- Published
- 2021-04-19
- OpenAlex
- W4213069590
- Cited by
- 1,488
- References
- 0
- Type
- paratext
- Language
- en
- Open access
- closed
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
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.
