How spam features change in twitter and the impact to machine learning based detection

Tingmin Wu, Derek Wang, Sheng Wen, Yang Xiang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


Twitter Spam is a critical problem and current solution is mainly about machine learning based detection. However, recent studies found that the spam features are continuously changing day by day (called ‘Spam Drift’ problem), which may significantly affect the performance of the detection. In this paper, we carried out a real-data driven study to explored the ‘Spam Drift’ problem and its impact to machine learning based detection. Our study found that only a small group of spam features will continuously change. The results also suggested a counter-intuitive conclusion that the ‘Spam Drift’ problem does not have serious impact on spam detection Precision (SP) and non-spam detection Recall (NR), two metrics that industries prioritise in practice.

Original languageEnglish
Title of host publicationInformation Security Practice and Experience
Subtitle of host publication13th International Conference, ISPEC 2017 Melbourne, VIC, Australia, December 13–15, 2017 Proceedings
EditorsJoseph K. Liu, Pierangela Samarati
Place of PublicationCham Switzerland
Number of pages7
ISBN (Electronic)9783319723594
ISBN (Print)9783319723587
Publication statusPublished - 2017
Externally publishedYes
EventInformation Security Practice and Experience Conference 2017 - Deakin Downtown, Melbourne, Australia
Duration: 13 Dec 201715 Dec 2017
Conference number: 13th (Website) (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInformation Security Practice and Experience Conference 2017
Abbreviated titleISPEC 2017
Internet address


  • Security
  • Spam
  • Twitter

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