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DOI: 10.14569/IJACSA.2016.070777
PDF

Sentiment Based Twitter Spam Detection

Author 1: Nasira Perveen
Author 2: Malik M. Saad Missen
Author 3: Qaisar Rasool
Author 4: Nadeem Akhtar

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 7, 2016.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Spams are becoming a serious threat for the users of online social networks especially for the ones like of twitter. twitter’s structural features make it more volatile to spam attacks. In this paper, we propose a spam detection approach for twitter based on sentimental features. We perform our experiments on a data collection of 29K tweets with 1K tweets for 29 trending topics of 2012 on twitter. We evaluate the usefulness of our approach by using five classifiers i.e. BayesNet, Naive Bayes, Random Forest, Support Vector Machine (SVM) and J48. Naive Bayes, Random Forest, J48 and SVM spam detections performance improved with our all proposed features combination. The results demonstrate that proposed features provide better classification accuracy when combined with content and user-oriented features.

Keywords: sentiment analysis; spam detection; twitter

Nasira Perveen, Malik M. Saad Missen, Qaisar Rasool and Nadeem Akhtar, “Sentiment Based Twitter Spam Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 7(7), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070777

@article{Perveen2016,
title = {Sentiment Based Twitter Spam Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070777},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070777},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {7},
author = {Nasira Perveen and Malik M. Saad Missen and Qaisar Rasool and Nadeem Akhtar}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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