Efficient Detection of Content Polluters in Social Networks

Han, Jin Seop; Park, Byung Joon

doi:10.1007/978-94-007-5860-5_119

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…A number of techniques are employed in these papers including classification, user-report-based filtering, behavioral analysis, and in some cases friend-graph analysis. But, more researchers proposed social spam detection framework for social network sites using various techniques [32,63,87,104]. Our work differs from them on efficient information integration solution and cross-domain classification using the relationship between objects on social media.…”

Section: Related Workmentioning

confidence: 99%

Analysis and detection of low quality information in social networks

2014

2014 IEEE 30th International Conference on Data Engineering Workshops

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To my family and all those who have supported me.iii ACKNOWLEDGEMENTS When I was a little boy, my parents always told me to study hard and learn more in school. But I never thought that I would study abroad and earn a Ph.D. degree at United States at that time. The journey to obtain a Ph.D. is quite long and full of peaks and valleys. Fortunately, I have received great helps and guidances from many people through the journey, which is also the source of motivation for me to move forward.First and foremost I should give thanks to my advisor, Prof. Calton Pu. He is not only a great research mentor guiding and inspiring me to diving deeply in the area of cyber security, but also an awesome friend who is willing to share his rich experience in life and study. He encouraged and trained me to be a great researcher by pushing me to exceed my own boundaries and excel in all aspects of what I did during the Ph.D. I really appreciate his patience on my research progress, as well as his amazing comments along my exploration within the area of cyber security. His underlying gentleness and dedication to research will continue to have a significant impact on my future.

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Section: Related Workmentioning

confidence: 99%