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Cited by 38Misinformation and Its ImpactsSpam and Phishing DetectionComplex Network Analysis Techniques

The DOI matches its Crossref record.

Behavior deviation: An anomaly detection view of rumor preemption

Chen, Weiling, Yeo, Chai Kiat, Lau, Chiew Tong, Lee, Bu Sung·2016·2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)
DOI 10.1109/iemcon.2016.7746262
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Abstract

Since information can spread rapidly and widely more than ever on Online Social Networks (OSNs), they have become new hot beds of false rumor diffusion. Due to the potential harm these false information may bring to the public, false rumor detection has become a significant but challenging research topic. While previous research work mostly views it as a classification task, we treat it as an anomaly detection problem. In this paper, false rumors are viewed as anomalies and we perform Factor analysis of mixed data (FAMD) on our proposed features to detect these anomalies. Two strategies based on Euclidean distance and Cosine similarity are proposed to describe the deviation degree. A rank based on deviation degree is computed which can facilitate further rumor detection. We show our method can achieve good performance and can shed light on automatic detection of false rumors on OSNs.

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Crossref · 10.1109/iemcon.2016.7746262

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  • Chen et al. (2016) treated veracity classification as anomaly detection where false rumours are anomalies, and reported significantly improved performance over K-means.

    “Chen et al. (2016) approached rumour veracity classification from a different angle.The authors treated it as an anomaly detection problem where false rumours are regarded as anomalies.”

    From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Chen et al. (2016)

Citations

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No references could be resolved yet.

Research works printed but not linked: 16

  1. 10.1145/2806416.2806607 · DOI
  2. 10.1007/978-3-319-25207-0_10 · DOI
  3. 10.1007/978-3-319-03260-3_26 · DOI
  4. 10.1109/JCSSE.2015.7219767 · DOI · Paper page
  5. 10.1145/2736277.2741637 · DOI · Paper page
  6. 10.1145/1015467.1015492 · DOI
  7. pagès, 2004, Analyse factorielle de données mixtes, Revue de Statistique Appliquée, 52, 93
  8. 10.1201/CRCTHERSER · DOI
  9. 10.1109/TKDE.2012.99 · DOI
  10. 10.1037/11503-000 · DOI
  11. kimmel, 2004, Rumors and rumor control A Manager's Guide to Understanding and Combatting Rumors
  12. 10.1145/2500853.2500860 · DOI
  13. 10.1016/j.ins.2013.11.016 · DOI · Paper page
  14. 10.1145/2806416.2806651 · DOI · Paper page
  15. 10.1145/1963405.1963500 · DOI · Paper page
  16. 10.1109/CEC.2015.7257176 · DOI

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Metadata

Authors
Chen, W., Yeo, C. K., Lau, C. T., & Lee, B. S.
Affiliations
Nanyang Technological University (SG)
ORCID
Venue
2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)
Publisher
IEEE
Published
2016-10
Pages
1-7
DOI
10.1109/iemcon.2016.7746262
OpenAlex
W2559060008
Cited by
38
References
0
Citations by year
2025: 1 · 2024: 3 · 2023: 5 · 2022: 4 · 2021: 3 · 2020: 4 · 2019: 7 · 2018: 7 · 2017: 3 · 2016: 1
FWCI
18.37
Type
conference paper
Language
en
Open access
closed
Locations
IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (publishedVersion)
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
anomaly detection, social networking sites, factor analysis of mixed data, deviation degree, Euclidean distance, cosine similarity, information dissemination

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