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Malicious Behaviour Analysis on Twitter Through the Lens of User Interest

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Data Mining (AusDM 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 845))

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Abstract

Evolving behaviours by spammers on online social networks continue to be a big challenge; this phenomenon has consistently received attention from researchers in terms of how they can be combated. On micro-blogging communities, such as Twitter, spammers intentionally change their behavioural patterns and message contents to avoid detection. Understanding the behavior of spammers is important for developing effective approaches to differentiate spammers from legitimate users. Due to the dynamic and inconsistent behaviour of spammers, the problem should be considered from two different levels to properly understand this type of behaviour and differentiate it from that of legitimate users. The first level pertains to the content, and the second, to the users’ demographics. In this paper, we first examine Twitter content relating to a particular topic, extracted from one hashtag, for a dataset comprising both spammers and legitimate users in order to characterise user behaviour with respect to that topic. We then investigate the users’ demographic data with a focus on the users’ profile description and how it relates to their tweets. The result of this experiment confirms that, in addition to the content level, users’ demographic data can present an alternative approach to identify the different behaviours of both spammers and legitimate users; moreover, it can be used to detect spammers.

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Notes

  1. 1.

    http://www.followthehashtag.com/datasets/nasdaq-100-companies-free-twitter-dataset/.

  2. 2.

    https://www.virustotal.com/.

  3. 3.

    https://www.mywot.com/.

  4. 4.

    https://www.mywot.com/wiki/API.

  5. 5.

    http://www.followthehashtag.com/datasets/nasdaq-100-companies-free-twitter-dataset/.

  6. 6.

    https://support.twitter.com/articles/64986.

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Authors and Affiliations

  1. Faculty of Science and Engineering, Queensland University of Technology Australia, Brisbane City, 4000, Australia

    Bandar Alghamdi, Yue Xu & Jason Watson

  2. Institute of Public Administration, Riyadh City, 11141, Saudi Arabia

    Bandar Alghamdi

Authors
  1. Bandar Alghamdi
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  2. Yue Xu
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  3. Jason Watson
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Corresponding authors

Correspondence to Bandar Alghamdi , Yue Xu or Jason Watson .

Editor information

Editors and Affiliations

  1. RMIT University, Melbourne, Victoria, Australia

    Yee Ling Boo

  2. University of Wollongong, Wollongong, New South Wales, Australia

    David Stirling

  3. La Trobe University, Melbourne, Victoria, Australia

    Lianhua Chi

  4. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia

    Lin Liu

  5. La Trobe University, Melbourne, Victoria, Australia

    Kok-Leong Ong

  6. Microsoft Pty Ltd, Singapore, Singapore

    Graham Williams

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Alghamdi, B., Xu, Y., Watson, J. (2018). Malicious Behaviour Analysis on Twitter Through the Lens of User Interest. In: Boo, Y., Stirling, D., Chi, L., Liu, L., Ong, KL., Williams, G. (eds) Data Mining. AusDM 2017. Communications in Computer and Information Science, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-13-0292-3_15

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  • DOI: https://doi.org/10.1007/978-981-13-0292-3_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0291-6

  • Online ISBN: 978-981-13-0292-3

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