Context-Aware Systems and Applications, and Nature of Computation and Communication. 7th EAI International Conference, ICCASA 2018, and 4th EAI International Conference, ICTCC 2018, Viet Tri City, Vietnam, November 22–23, 2018, Proceedings

Research Article

Context Based Algorithm for Social Influence Measurement on Twitter

  • @INPROCEEDINGS{10.1007/978-3-030-06152-4_12,
        author={Alaa Alsaig and Ammar Alsaig and Marwah Alsadun and Soudabeh Barghi},
        title={Context Based Algorithm for Social Influence Measurement on Twitter},
        proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 7th EAI International Conference, ICCASA 2018, and 4th EAI International Conference, ICTCC 2018, Viet Tri City, Vietnam, November 22--23, 2018, Proceedings},
        proceedings_a={ICCASA \& ICTCC},
        year={2019},
        month={1},
        keywords={Social influence measurement Context Twitter users influence},
        doi={10.1007/978-3-030-06152-4_12}
    }
    
  • Alaa Alsaig
    Ammar Alsaig
    Marwah Alsadun
    Soudabeh Barghi
    Year: 2019
    Context Based Algorithm for Social Influence Measurement on Twitter
    ICCASA & ICTCC
    Springer
    DOI: 10.1007/978-3-030-06152-4_12
Alaa Alsaig1,*, Ammar Alsaig1,*, Marwah Alsadun1,*, Soudabeh Barghi1,*
  • 1: Concordia University
*Contact email: al_alsai@encs.concordia.ca, a_alsaig@encs.concordia.ca, m_alsadu@encs.concordia.ca, s_arghi@encs.concordia.ca

Abstract

The social media became one of the most effective method for marketing and for information propagation. Therefore, measuring users influence is important for organizations to know which user to target to successfully spread a piece of information. Twitter is one of the social media tools that is used for information propagation. The current methods for measuring influence of Twitters users, use ranking algorithms that focus on specific criteria such as number of followers or tweets. However, different cases creates different needs in measuring influence. Each need could include different elements with different priority. One of these cases is local businesses which need to propagate information within a specific context such as location. That is, the most influential user for such a business is the one that has the highest number of followers that are located within the required location. Therefore, in this paper, we use the X algorithm for measuring users influence on Twitter by ranking users based on followers context that is represented by number of elements. Each element is given a weight to prioritize elements based on client demand.