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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Global Landscapes Forum. (2018) Forest and Landscape Restoration in Africa: Prospects and Opportunities Forest and Landscape Restoration in Africa: Prospects and Opportunities A Global Landscapes Forum event Forest and Landscape Restoration in Africa: Prospects and Opportunities A Globa.
Gabizon, S. (2016). Women’ s movements’ engagement in the SDGs : lessons learned from the Women’ s Major Group from the Women’ s Major Group. Gender & Development,2074(24), 99–110.
Carant, J. B. (2017). Unheard voices : A critical discourse analysis of the Millennium Development Goals’ evolution into the Sustainable Development Goals Development Goals. Third World Q.,38(1), 16–41.
Atzmueller, M., & Lemmerich, F. (2018). Homophily at academic conferences. In The Web Conference Companion (pp. 3–4).
Lovei, M. Desertification is not fate. [Online]. https://blogs.worldbank.org/nasikiliza/desertification-is-not-fate. Accessed 6 Jun 2019.
Niang, I. et al. (2014). Africa. In Climate change 2014: Impacts, adaptation, and vulnerability (pp. 1199–1265). Cambridge: Cambridge University Press.
TARGET 15—Technical Rationale extended. (2012). [Online]. https://www.cbd.int/sp/targets/rationale/target-15/. Accessed 6 Jun 2019.
Bonn Challenge. [Online]. http://www.bonnchallenge.org/. Accessed 6 Jun 2019.
Yahya, M. Africa’s defining challenge | UNDP in Africa. [Online]. http://www.africa.undp.org/content/rba/en/home/blog/2017/8/7/africa_defining_challenge.html. Accessed 18 Jun 2019.
GMSA. The Mobile Economy—Africa 2016. [Online]. https://www.gsma.com/mobileeconomy/africa/. Accessed 18 Jun 2019.
Africa Internet Users. 2019 Population and Facebook Statistics. [Online]. https://www.internetworldstats.com/stats1.htm. Accessed 18 Jun 2019.
Mourdoukoutas, E. The hashtag revolution gaining ground. [Online]. https://www.un.org/africarenewal/magazine/april-2018-july-2018/hashtag-revolution-gaining-ground. Accessed 18 Jun 2019.
Nkomo, S., Wafula, A. Strong public support for ‘watchdog’ role backs African news media under attack|Afrobarometer. [Online]. https://afrobarometer.org/publications/ad85-media_in_africa_world_press_freedom_day_2016. Accessed 18 Jun 2019.
Ramaswamy, A. The big picture: Technology to meet the challenges of media fragmentation. [Online]. https://www.nielsen.com/us/en/insights/reports/2017/the-big-picture-technology-to-meet-the-challenges-of-media-fragmentation.html. Accessed 18 Jun 2019.
Heine, B., & Derek, N. (2000). African languages: An introduction. Cambridge: Cambridge University Press.
Wolff, E. (2000). Language and society. In African languages—An introduction (p. 317). Cambridge: CUP.
Outcome Statement of the 2016 Global Landscapes Forum: Climate Action for Sustainable Development—Global Landscapes Forum. [Online]. https://www.globallandscapesforum.org/publication/outcome-statement-2016-global-landscapes-forum-climate-action-sustainable-development/. Accessed 6 Jun 2019.
Youth in Landscapes Initiative—Nairobi Leadership Program—Global Landscapes Forum Events. [Online]. https://events.globallandscapesforum.org/nairobi-2018/youth-leaders-at-glf-nairobi-2018/. Accessed 6 Jun 2019.
Kursuncu, U., Gaur, M., Lokala, U., Thirunarayan, K., Sheth, A., & Arpinar, I. B. (2019). Predictive analysis on Twitter: Techniques and applications (pp. 67–104)., Lecture notes in social networks Cham: Springer.
Balasuriya, L., Wijeratne, S., Doran, D., Sheth, A. (2016). Finding street gang members on Twitter. In 2016 IEEE/ACM International Conferences on Advances in Social Network Analysis and Mining (pp. 685–692).
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science,2(1), 1–8.
Tumasjan, A., Sprenger, T.O., Sandner, P.G., & Welpe, I.M. (2010) Predicting elections with Twitter: What 140 characters reveal about political sentiment. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (pp. 178–185).
An, X., Ganguly, A. R., Fang, Y., Scyphers, S. B., Hunter, A. M. & Dy, J. G. (2014) Tracking climate change opinions from Twitter Data. In KDD (pp. 1–5).
Cody, E. M., Reagan, A. J., Mitchell, L., Dodds, P. S., & Danforth, C. M. (2015). Climate change sentiment on Twitter: An unsolicited public opinion poll. PLoS One,10(8), 1–18.
Blei, D. M., Ng, A. Y., & Jordan, M. (2003). Latent dirichlet allocation. Journal of Machine Learning Research,3, 993–1022.
Yan, X., Guo, J., Lan, Y. & Cheng, X. (2013) A biterm topic model for short texts. In Proceedings of the International World Wide Web Conference (pp. 1445–1455).
Mikolov, T., Chen, K., Corrado, G.S. & Dean, J. (2013) Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems (pp. 1–9).
Pennington, J., Socher, R. & Manning, C. (2014) GloVe: Global vectors for word representation.
Fang, A., Macdonald, C., Ounis, I. & Habel, P. (2016). Using word embedding to evaluate the coherence of topics from Twitter Data. In Special Interest Group on Information Retrieval.
Cer, D. et al. (2018). Universal sentence encoder. arXiv preprint arXiv:1803.11175.
Li, H., Caragea, D., Li, X. & Caragea, C. (2018) Comparison of word embeddings and sentence encodings as generalized representations for crisis Tweet classification tasks. In Proceedings of the ISCRAM Asian Pacific 2018 Conference (pp. 1–13).
Sood, G., & Laohaprapanon, S. (2018). Predicting race and ethnicity from the sequence of characters in a name. arXiv preprint arXiv:1805.02109.
Rothe, R., Timofte, R., & Van Gool, L. (2016) DEX: Deep expectation of apparent age from a single image. In 2015 IEEE International Conference on Computer Vision Workshop. Santiago, Chile.
Dhomne, A., Kumar, R., & Bhan, V. (2018). Gender recognition through face using deep learning. International Conference on Computational Intelligence and Data Science,132, 2–10.
Cesare, N., Grant, C., Hawkins, J. B., Brownstein, J. S. and Nsoesie, E. O. (2017). Demographics in social media data for public health research: Does it matter? In Bloomberg Data for Good Exchange Conference.
Cesare, N., Grant, C., Nguyen, Q., Lee, H. & Nsoesie, E.O. (2017) How well can machine learning predict demographics of social media users?.
Murthy, D., Gross, A., & Pensavalle, A. (2016). Urban Social Media demographics: An exploration of Twitter use in major American cities. Journal of Computer-Mediated Communication,21(1), 33–49.
Nijbroek, R., & Wangui, E. (2018). What women and men want: Considering gender for successful, sustainable land management programs. Colombo, Sri Lanka: CGIAR Research Program on Water, Land and Ecosystems (WLE).
Conover, M.D., Gonçalves, B., Ratkiewicz, J., Flammini, A. & Menczer, F. (2011) Predicting the political alignment of Twitter users. In 2011 IEEE Third Int’l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int’l Conference on Social Computing, pp. 192–199.
Compton, R., Jurgens, D., & Allen D. (2014). Geotagging one hundred million Twitter accounts with total variation minimization. In 2014 IEEE International Conference on Big Data, Washington DC, USA.
Preoţiuc-Pietro, D., Volkova, S., Lampos, V., Bachrach, Y., & Aletras, N. (2015). Studying user income through language, behaviour and affect in social media. PLoS One,10(9), e0138717.
Volkova, S. & Yarowsky, D. (2014). Improving gender prediction of social media users via weighted annotator rationales. In NIPS 2014 Workshop on Personalization, Montreal, Canada.
GLF. GLF Nairobi: Social Media Toolkit [Online]. https://events.globallandscapesforum.org/nairobi-2018/social-media-toolkit/. Accessed 6 June 2019.
Rahimi, A., Cohn, T., & Baldwin, T. (2016). Pigeo: A python geotagging tool. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. (pp. 127–132), Berlin, Germany.
Zagoruyko, S. and Komodakis, N. (2016). Wide residual networks.
Mullen, L., Blevins, C. & Schmidt, B. (2018) gender: Predict Gender from Names Using Historical Data. R package version 0.5.2.
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of tricks for efficient text classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics,2(1), 427–431. Valencia, Spain.
Roberts, M.E., Stewart, B.M., Tingley, D., & Airoldi, E.M. (2013). The structural topic model and applied social science. In NIPS 2013 Workshop on Topic Models: Computation, Application, and Evaluation. Lake Tahoe, USA.
Vosoughi, S., Vijayaraghavan, P., & Roy, D. (2016). Tweet2Vec: Learning tweet embeddings using character-level CNN-LSTM encoder-decoder. In Special Interest Group on Information Retrieval. Pisa, Italy.
Conneau, A., Lample, G., Ranzato, M.A., Denoyer, L. & Jégou, H. (2018) Word translation without parallel data. In: International Conference on Learning Representations (pp. 1–14).
Vaswani, A. et al. (2017). Attention is all you need. In 31st Conference on Neural Information Processing Systems. Long Beach, CA, USA.
Core Team, R. (2013). R: A language and environment for statistical computing. Vienna: R Core Team.
Abadi, M. et al. (2016) TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation.
IUCN. Pakistan’s Billion Tree Tsunami restores 350,000 hectares of forests and degraded land to surpass Bonn Challenge commitment, 2017. [Online]. https://www.iucn.org/news/forests/201708/pakistan’s-billion-tree-tsunami-restores-350000-hectares-forests-and-degraded-land-surpass-bonn-challenge-commitment.
Jansen, B. J., Moore, K., & Carman, S. (2013). Evaluating the performance of demographic targeting using gender in sponsored search. Information Processing & Management,49, 286–302.
Global Landscapes Forum Nairobi 2018: The highlights. [Online]. https://events.globallandscapesforum.org/nairobi-2018/. Accessed: 18 Jun 2019.
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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
- Text mining
- Social media analysis
- Demographic analysis