The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Twitter spam detection: Survey of new approaches and comparative study
Open accessAbstract
Abstract Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have proposed many detection and defence methods in order to protect Twitter users from spamming activities. Particularly in the last three years, many innovative methods have been developed, which have greatly improved the detection accuracy and efficiency compared to those which were proposed three years ago. Therefore, we are motivated to work out a new survey about Twitter spam detection techniques. This survey includes three parts: 1) A literature review on the state-of-art: this part provides detailed analysis (e.g. taxonomies and biases on feature selection) and discussion (e.g. pros and cons on each typical method); 2) Comparative studies: we will compare the performance of various typical methods on a universal testbed (i.e. same datasets and ground truths) to provide a quantitative understanding of current methods; 3) Open issues: the final part is to summarise the unsolved challenges in current Twitter spam detection techniques. Solutions to these open issues are of great significance to both academia and industries. Readers of this survey may include those who do or do not have expertise in this area and those who are looking for deep understanding of this field in order to develop new methods.
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Self-citationSurvey on Astroturfing Detection and Analysis from an Information Technology Perspective
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Metadata
- Authors
- Wu, T., Wen, S., Xiang, Y., & Zhou, W.
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 76
- Pages
- 265-284
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W2768745333
- Cited by
- 175
- References
- 12
- FWCI
- 37.19
- Type
- journal article
- Language
- en
- Open access
- green
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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.
