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Identifying social media user demographics and topic diversity with computational social science: a case study of a major international policy forum

Abstract

When the world’s countries agreed on the 2030 Agenda for Sustainable Development, they recognized that equity and inclusion should be at the center of implementing the 17 Sustainable Development Goals (SDGs). SDG 15, which calls for protecting, restoring, and promoting the sustainable use of terrestrial ecosystems, has spurred commitments to restore 350 million hectares of land by 2030. These commitments, primarily made in a top-down manner at the international scale, must be implemented by actively engaging individual landholders and local communities. Ensuring that diverse and marginalized audiences are engaged in the land restoration movement is critical to equitably distributing the economic benefits of restoration. This publication uses social network analysis and machine learning to understand how important the voices of Africans, women, and young people are in governing restoration in Africa. We analyze location- and machine learning-identified demographics from Twitter data collected during the Global Landscapes Forum (GLF), which is the world’s largest platform for promoting sustainable land use practices. Our results suggest that convening the GLF in Nairobi, Kenya elevated the voices of African leaders in comparison to the previous GLF in Bonn, Germany. We also found significant demographic differences in topic-level engagement between different ages, races, and genders. The primary contributions of this paper are a novel methodology for quantifying demographic differences in social media engagement and the application of social media and social network analysis to provide critical insights into the inclusivity of a large political conference aimed at engaging youth and African voices.

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Correspondence to John Brandt.

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Appendix

Appendix

See Tables 4, 5, 6 and 7.

Table 4 GLF Nairobi 2018 URLs Tracked on Facebook
Table 5 Highest gains in followers
Table 6 2017 GLF Bonn Communities
Table 7 2018 GLF Nairobi Communities

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Brandt, J., Buckingham, K., Buntain, C. et al. Identifying social media user demographics and topic diversity with computational social science: a case study of a major international policy forum. J Comput Soc Sc (2020). https://doi.org/10.1007/s42001-019-00061-9

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Keywords

  • Text mining
  • Social media analysis
  • Demographic analysis
  • Network