Twitter spam detection based on deep learning

Tingmin Wu, Shigang Liu, Jun Zhang, Yang Xiang

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

41 Citations (Scopus)

Abstract

Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.

Original languageEnglish
Title of host publicationProceedings of the Australasian Computer Science Week Multiconference
EditorsXiao Liu , Dong Yuan
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Electronic)9781450347686
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventAustralasian Symposium on Parallel and Distributed Computing 2017 - Geelong, Australia
Duration: 2 Feb 20172 Feb 2017
Conference number: 15th
https://web.archive.org/web/20170827074824/http://www.ozcom.org/auspdc2017/ (Website - workshop)
https://web.archive.org/web/20171226042118/http://acsw2017.deakin.edu.au/program/ (Website - main conference )

Conference

ConferenceAustralasian Symposium on Parallel and Distributed Computing 2017
Abbreviated titleAusPDC 2017
CountryAustralia
CityGeelong
Period2/02/172/02/17
Internet address

Keywords

  • Deep learning
  • Social network security
  • Twitter spam detection

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