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Determining the Veracity of Rumours on Twitter
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Giasemidis et al. (2016) reported very good results using decision trees on 100 million tweets associated with 72 different rumours.
“Giasemidis et al. (2016) reported experiments run on 100 million tweets associated with 72 different rumours.Features and machine learning classifiers used in previous work were adopted in this case.The authors reported very good results using decision trees.”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Giasemidis et al. (2016)
Section 4.2 reports 96.6% validation accuracy for the Decision Tree, supporting the description of very good results. Appendix A.1 clarifies that although the authors collected about 100 million tweets, including users’ past tweets, only about 327,500 were part of the 72 rumours. The claim overstates how many tweets were associated with the rumours. (LLM, conf=0.94)
Experimental — accuracy not yet measuredBottom-up sampling strategies emerged more recently to collect a wider range of rumours by sifting through data to identify rumours rather than using known ones.
“Bottom-up sampling strategies have emerged more recently in studies that aimed at collecting a wider range of rumours, i.e., sifting through data to identify rumours, rather than rumours that are already known.”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Giasemidis et al. 2016
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Research works printed but not linked: 28
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Cited by
Sorted by citations
A survey on fake news and rumour detection techniques
Bondielli, A., & Marcelloni, F. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.035 · Cited by 658
Detection and Resolution of Rumours in Social Media: A Survey
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2019) · ACM Computing Surveys · doi:10.1145/3161603 · Cited by 862
“This is an approach that was used first by [Zubiaga et al. 2016] and subsequently by [Giasemidis et al. 2016].”
BackgroundMethodThe Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans
Zannettou, S., Sirivianos, M., Blackburn, J., & Kourtellis, N. (2019) · Journal of Data and Information Quality · doi:10.1145/3309699 · Cited by 86
Metadata
- Authors
- Giasemidis, G., Singleton, C., Agrafiotis, I., Nurse, J. R. C., Pilgrim, A., Willis, C., & Greetham, D. V.
- Affiliations
- University of Oxford (GB)
- BAE Systems (United Kingdom) (GB)
- University of Reading (GB)
- Corresponding author
- Giasemidis, Georgios
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 185-205
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2530362799
- Cited by
- 60
- References
- 0
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 2 · 2022: 7 · 2021: 11 · 2020: 9 · 2019: 12 · 2018: 8 · 2017: 3
- FWCI
- 23.62
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- Open Research Online (The Open University) (acceptedVersion)
- CentAUR (University of Reading) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Kent Academic Repository (University of Kent) (submittedVersion)
- Oxford University Research Archive (ORA) (University of Oxford) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Defence Science and Technology Group
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- rumor detection, misinformation, Twitter, message classification, social network analysis, user profiling, time windows
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