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International Conference on Computational Science and Its Applications

ICCSA 2015: Computational Science and Its Applications -- ICCSA 2015 pp 16–31Cite as

Mining Climate Change Awareness on Twitter: A PageRank Network Analysis Method

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9155))

Abstract

Climate change is one of this century’s greatest unbalancing forces that affect our planet. Mining the public awareness is an essential step towards the assessment of current climate policies, dedication of sufficient resources, and construction of new policies for business planning. In this paper, we present an exploratory data mining method that compares two types of networks. The first type is constructed from a set of words collected from a Climate Change corpus, which we consider as ground-truth (i.e., base of comparison). The other type of network is constructed from a reasonably large data set of 72 million tweets; it is used to analyze the public awareness of climate change on Twitter.

The results show that the social-language used on Twitter is more complex than just single word expressions. While the term climate and the hashtag (#climate) scored a lower rank, complex terms such as (“Climate Change”) and (“Climate Engineering”) were more dominant using hashtags. More interestingly, we found the (#ClimateChange) hashtag is the top ranked term, among all other features, used on Twitter to signal climate familiarity expressions. This is indeed striking evidence that demonstrates a great deal of awareness and provides hope for a better future dealing with Climate Change issues.

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Author information

Authors and Affiliations

  1. EPSCoR, University or Vermont, Burlington, VT, USA

    Ahmed Abdeen Hamed & Asim Zia

Authors
  1. Ahmed Abdeen Hamed
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  2. Asim Zia
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Corresponding author

Correspondence to Ahmed Abdeen Hamed .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Canaanland, Nigeria

    Sanjay Misra

  4. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  5. University of Minho, Braga, Portugal

    Ana Maria Alves Coutinho Rocha

  6. Polytechnic University, Bari, Italy

    Carmelo Torre

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

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Hamed, A.A., Zia, A. (2015). Mining Climate Change Awareness on Twitter: A PageRank Network Analysis Method. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9155. Springer, Cham. https://doi.org/10.1007/978-3-319-21404-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-21404-7_2

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-21404-7

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