Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Understanding the expression of grievances in the Arabic Twitter-sphere using machine learning

Understanding the expression of grievances in the Arabic Twitter-sphere using machine learning The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context.Design/methodology/approachTo understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated.FindingsThe machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong.Practical implicationsBorum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people.Originality/valueIn combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Criminological Research, Policy and Practice Emerald Publishing

Understanding the expression of grievances in the Arabic Twitter-sphere using machine learning

Download PDF
12 pages

Loading next page...
 
/lp/emerald-publishing/understanding-the-expression-of-grievances-in-the-arabic-twitter-WalL0q0NsI
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2056-3841
DOI
10.1108/jcrpp-02-2019-0009
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context.Design/methodology/approachTo understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated.FindingsThe machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong.Practical implicationsBorum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people.Originality/valueIn combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.

Journal

Journal of Criminological Research, Policy and Practice – Emerald Publishing

Published: Jul 9, 2019

Keywords: Data mining; Machine learning; Twitter; Social network analysis; Grievances; Online radicalization

References

  • Understanding online radicalisation using data science
    Al-Saggaf, Y.
  • A malicious use of a clustering algorithm to threaten the privacy of a social networking site user
    Al-Saggaf, Y.; Islam, Z.
  • Data mining and privacy of social network sites’ users: implications of the data mining problem
    Al-Saggaf, Y.; Islam, Z.
  • Doing ethnography from within a constructivist paradigm to explore virtual communities in Saudi Arabia
    Al-Saggaf, Y.; Williamson, K.
  • Venting negative emotions on Twitter and the number of followers and followees
    Al-Saggaf, Y.; Utz, S.; Lin, R.
  • Online communities in Saudi Arabia: an ethnographic study
    Al-Saggaf, Y.; Williamson, K.; Weckert, J.
  • Online radicalisation and the Muslim diaspora
    Aly, A.
  • Fishermen or swarm dynamics? Should we understand jihadist online-radicalization as a top-down or bottom-up process?
    Baaken, T.; Schlegel, L.
  • Combining social network analysis and sentiment analysis to explore the potential for online radicalisation: social network analysis and mining
    Bermingham, A.; Conway, M.; McInerney, L.; O’Hare, N.; Smeaton, A.F.
  • Radicalization into violent extremism I: a review of social science theories
    Borum, R.
  • Pathways to violent extremism in the digital era
    Edwards, C.; Gribbon, L.
  • News media coverage of Islam and Muslims in Australia: an opinion survey among Australian Muslims
    Ewart, J.; Cherney, A.; Murphy, K.
  • Topic modeling of tweets in R: a tutorial and methodology
    Graham, T.; Ackland, R.
  • Topicmodels: an R package for fitting topic models
    Grun, B.; Hornik, K.
  • Investigating topic modelling for therapy dialogue analysis
    Howes, C.; Purver, M.; McCabe, R.
  • A new understanding of terrorism using cognitive dissonance principles
    Maikovich, A.K.
  • The staircase to terrorism: a psychological exploration
    Moghaddam, F.
  • Application of a density based clustering technique on biomedical datasets
    Rahman, M.A.; Islam, M.Z.
  • Mining pro-ISIS radicalisation signals from social media users
    Rowe, M.; Saif, H.
  • A preliminary radicalisation framework based on social engineering techniques
    Sabouni, S.; Cullen, A.; Armitage, L.
  • Automatic detection of cyber-recruitment by violent extremists
    Scanlon, J.R.; Gerber, M.S.
  • Forecasting violent extremist cyber recruitment
    Scanlon, J.R.; Gerber, M.S.
  • Research on radicalisation: topics and themes
    Schmidt, A.
  • Questioning the concept of ‘new terrorism
    Spencer, A.
  • About
  • Preventing violent extremism: a review of the literature, studies in conflict & terrorism
    Stephens, W.; Sieckelinck, S.; Boutellier, H.
  • Developing an explanatory model for the process of online radicalisation and terrorism
    Torok, R.
  • Discourses of terrorism: the role of Internet technologies (social media and online propaganda) on Islamic radicalisation, extremism and recruitment post 9/11
    Torok, R.
  • The association between discrimination and violence among Somali Canadian youth
    Ungar, M.; Hadfield, K.; Amarasingam, A.; Morgan, S.; Grossman, M.
  • Confronting Islamic Jihadist movements
    Upal, M.A.
  • Tracking on-line radicalization using investigative data mining
    Wadhwa, P.; Bhatia, M.P.S.
  • Explaining violent radicalization in Western Muslims: a four factor model
    Yusoufzai, K.; Emmerling, F.