The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
On Predicting Deletions of Microblog Posts
Abstract
Among the many classification tasks on Twitter content, predicting whether a tweet will be deleted has to date received relatively little attention. Deletions occur for a variety of reasons, which can make the classification task challenging. Moreover, deletion prediction might serve different goals, the characteristics of which should be reflected in the evaluation design. This paper addresses the problem of deletion prediction by analyzing the distribution of deleted tweets, presenting a new evaluation framework, exploring tweet-based and user-based features, and reporting prediction scores.
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“This often makes it impractical to fully reconstruct datasets over time, leaving important holes (“Swiss Cheese” decay)”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Bagdouri and Oard, 2015
Citations
Cites
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
Feature hashing for large scale multitask learning
Weinberger, K., Dasgupta, A., Langford, J., Smola, A., & Attenberg, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553516 · Cited by 906
Classifying latent user attributes in twitter
Rao, D., Yarowsky, D., Shreevats, A., & Gupta, M. (2010) · Proceedings of the 2nd international workshop on Search and mining user-generated contents · doi:10.1145/1871985.1871993 · Cited by 655
(How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising
Starbird, K., & Palen, L. (2012) · Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work · doi:10.1145/2145204.2145212 · Cited by 409
RT to Win! Predicting Message Propagation in Twitter
Petrovic, S., Osborne, M., & Lavrenko, V. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14149 · Cited by 367
Censorship and deletion practices in Chinese social media
Bamman, D., O'Connor, B., & Smith, N. (2012) · First Monday · doi:10.5210/fm.v17i3.3943 · Cited by 288
Sparse kernel SVMs via cutting-plane training
Joachims, T., & Yu, C.-N. J. (2009) · Machine Learning · doi:10.1007/s10994-009-5126-6 · Cited by 131
"i read my Twitter the next morning and was astonished"
Sleeper, M., Cranshaw, J., Kelley, P. G., Ur, B., Acquisti, A., Cranor, L. F., & Sadeh, N. (2013) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2466448 · Cited by 110
Predicting Discussions on the Social Semantic Web
Rowe, M., Angeletou, S., & Alani, H. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21064-8_28 · Cited by 46
Research works printed but not linked: 1
- 10.5555/3121576.3121586 · doi:10.5555/3121576.3121586
Cited by
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
Metadata
- Authors
- Bagdouri, M., & Oard, D. W.
- Affiliations
- University of Maryland, College Park, MD, USA
- Venue
- Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2015-10-17
- Pages
- 1707-1710
- Cited by
- 12
- References
- 11
- Funders
- Qatar National Research Fund (NPRP 6-1377-1-257)
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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.
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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.
