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Context Based Algorithm for Social Influence Measurement on Twitter

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Book cover Context-Aware Systems and Applications, and Nature of Computation and Communication (ICCASA 2018, ICTCC 2018)

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.

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Correspondence to Alaa Alsaig .

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Alsaig, A., Alsaig, A., Alsadun, M., Barghi, S. (2019). Context Based Algorithm for Social Influence Measurement on Twitter. In: Cong Vinh, P., Alagar, V. (eds) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICCASA ICTCC 2018 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 266. Springer, Cham. https://doi.org/10.1007/978-3-030-06152-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-06152-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-06151-7

  • Online ISBN: 978-3-030-06152-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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