Deception detection in Twitter

@article{Alowibdi2015DeceptionDI,
  title={Deception detection in Twitter},
  author={Jalal S. Alowibdi and Ugo A. Buy and Philip S. Yu and Sohaib Ghani and Mohamed F. Mokbel},
  journal={Social Network Analysis and Mining},
  year={2015},
  volume={5},
  pages={1-16}
}
Online Social Networks (OSNs) play a significant role in the daily life of hundreds of millions of people. However, many user profiles in OSNs contain deceptive information. Existing studies have shown that lying in OSNs is quite widespread, often for protecting a user’s privacy. In this paper, we propose a novel approach for detecting deceptive profiles in OSNs. We specifically define a set of analysis methods for detecting deceptive information about user genders and locations in Twitter… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 33 references

Tourism information

T. information, research centre
http://www. mas.gov.sa/.
View 4 Excerpts
Highly Influenced

Country travel advice and advisories

G. of canada
http://travel.gc.ca/ travelling/advisories.
View 3 Excerpts
Highly Influenced

Detecting deception in Online Social Networks

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) • 2014
View 7 Excerpts

Empirical Evaluation of Profile Characteristics for Gender Classification on Twitter

2013 12th International Conference on Machine Learning and Applications • 2013
View 4 Excerpts

Language independent gender classification on Twitter

2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013) • 2013
View 2 Excerpts

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