Renewable Energy Discourse on Social Media: A Cross-Platform Sentiment Analysis

Yıl 2023, , 466 - 480, 30.11.2023

Hafize Nurgül Durmuş Şenyapar

https://doi.org/10.29249/selcuksbmyd.1376922

Öz

The global shift towards renewable energy has garnered significant attention in digital spaces, with platforms teeming with discourse about its implications, challenges, and potential. This study undertook a comprehensive exploration of this digital discourse across prominent social media platforms, including Quora, Facebook, Instagram, Reddit, and the platform formerly known as Twitter, now “X.” Leveraging the API-based tool from BrandMentions, a dataset focused on the keyword “Renewable Energy” was extracted and conducted in-depth sentiment and textual analyses. Findings revealed a predominant positive sentiment, accounting for 71.44% of the mentions. English emerged as the dominant language, comprising 97.48% of the dataset. Platform-specific insights showcased diverse aspects of the renewable energy conversation, from career-related inquiries on Quora to industry trends on Reddit. Co-occurrence analysis further underscored the multifaceted nature of discussions, highlighting areas of technological innovation, sustainability concerns, and commercial implications. This research sheds light on the complex web of online discourse on renewable energy, providing significant implications for stakeholders, policymakers, and researchers. Future studies might delve deeper into regional sentiments, and temporal shifts in discourse and employ more advanced analytical tools for granular insights.

Anahtar Kelimeler

Renewable energy, Social media, Sentiment analysis, Sustainable marketing, Marketing communication

Sosyal Medyada Yenilenebilir Enerji Söylemi: Platformlar Arası Duygu Analizi

Öz

Küresel olarak yenilenebilir enerjiye doğru olan geçiş, etkileri, zorlukları ve potansiyeli hakkındaki tartışmalar ile dijital mecralarda önemli bir ilgi çekmiştir. Bu çalışma, Quora, Facebook, Instagram, Reddit ve X. (eski adıyla Twitter) dahil olmak üzere önde gelen sosyal medya platformlarında bu dijital tartışmanın kapsamlı bir keşfini gerçekleştirmiştir. BrandMentions’dan API tabanlı bir araç kullanılarak “yenilenebilir enerji” anahtar kelimesine odaklanan bir veri kümesi çıkarılıp derinlemesine duygu ve metin analizleri gerçekleştirilmiştir. Bulgular, gönderilerin %71,44’ünün baskın bir pozitif duygu içerdiğini ortaya çıkarmıştır. İngilizce, veri kümesinin %97,48’ini oluşturarak baskın dil olarak ortaya çıkmıştır. Platforma özel bilgiler, Quora’da kariyerle ilgili sorularından Reddit’te endüstri trendlerine kadar yenilenebilir enerji tartışmalarının farklı yönlerini ortaya çıkarmıştır. Eş bulunuş analizi, tartışmaların çok yönlü doğasını vurgulayarak teknolojik yenilik, sürdürülebilirlik endişeleri ve ticari sonuçlar alanlarını öne çıkarmıştır. Bu araştırma, yenilenebilir enerji hakkındaki çevrimiçi tartışmanın karmaşık ağına ışık tutarak paydaşlar, politika yapıcılar ve araştırmacılar için önemli sonuçlar sunmaktadır. Gelecekteki çalışmalar, bölgesel görüşlere, söylemdeki zaman içindeki değişikliklere daha derinlemesine inebilir ve daha gelişmiş analitik araçlar kullanarak ayrıntılı içgörüler elde edebilir.

Anahtar Kelimeler

Yenilenebilir enerji, Sosyal medya, Duygu analizi, Sürdürülebilir pazarlama, Pazarlama iletişimi.

Kaynakça

• Abdul, D., Wenqi, J., & Tanveer, A. (2022). Environmental stewardship: Analyzing the dynamic impact of renewable energy, foreign remittances, and globalization index on China’s CO2 emissions. Renewable Energy, 201, 418–425. https://doi.org/10.1016/j.renene.2022.10.113
• Arnold, M. V., Dodds, P. S., & Danforth, C. M. (2023). Curating corpora with classifiers: A case study of clean energy sentiment online (arXiv:2305.03092). arXiv. https://doi.org/10.48550/arXiv.2305.03092
• Boughton, M., & Halliday, L. (2008). A challenge to the menopause stereotype: Young Australian women’s reflections of ‘being diagnosed’ as menopausal. Health & Social Care in the Community, 16(6), 565–572. https://doi.org/10.1111/j.1365-2524.2008.00777.x
• Boulianne, S., Koc-Michalska, K., & Bimber, B. (2020). Mobilizing media: Comparing TV and social media effects on protest mobilization. Information, Communication & Society, 23(5), 642–664. https://doi.org/10.1080/1369118X.2020.1713847
• Boulianne, S., Lalancette, M., & Ilkiw, D. (2020). “School Strike 4 Climate”: Social media and the ınternational youth protest on climate change. Media and Communication, 8(2), 208–218. https://doi.org/10.17645/mac.v8i2.2768
• Ching, J., & Kajino, M. (2020). Rethinking air quality and climate change after COVID-19. International Journal of Environmental Research and Public Health, 17(14), Article 14. https://doi.org/10.3390/ijerph17145167
• Church, J. A., & White, N. J. (2011). Sea-level rise from the late 19th to the early 21st century. Surveys in Geophysics, 32(4–5), 585–602. https://doi.org/10.1007/s10712-011-9119-1
• Clark, D. (2015, March 25). How much of the world’s fossil fuel can we burn? The Guardian. https://www.theguardian.com/environment/keep-it-in-the-ground-blog/2015/mar/25/what-numbers-tell-about-how-much-fossil-fuel-reserves-cant-burn
• Destek, M. A., & Sinha, A. (2020). Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from organisation for economic Co-operation and development countries. Journal of Cleaner Production, 242, 118537. https://doi.org/10.1016/j.jclepro.2019.118537
• Gilardi, F., Gessler, T., Kubli, M., & Müller, S. (2022). Social media and political agenda setting. Political Communication, 39(1), 39–60. https://doi.org/10.1080/10584609.2021.1910390
• Gunasekaran, K. P. (2023). Exploring sentiment analysis techniques in natural language processing: A Comprehensive Review. https://doi.org/10.17148/IJARCCE.2019.8126
• Isoaho, K., Gritsenko, D., & Mäkelä, E. (2021). Topic modeling and text analysis for qualitative policy research. Policy Studies Journal, 49(1), 300–324. https://doi.org/10.1111/psj.12343
• Jain, P. K., Pamula, R., & Srivastava, G. (2021). A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Computer Science Review, 41, 100413. https://doi.org/10.1016/j.cosrev.2021.100413
• Karn, M., & Sharma, M. (2021). Climate change, natural calamities and the triple burden of disease. Nature Climate Change, 11(10), Article 10. https://doi.org/10.1038/s41558-021-01164-w
• Kaur, P., & Edalati, M. (2022). Sentiment analysis on electricity Twitter posts (arXiv:2206.05042). arXiv. https://doi.org/10.48550/arXiv.2206.05042
• Kim, S. Y., Ganesan, K., Dickens, P., & Panda, S. (2020). Public sentiment toward solar energy: Opinion mining of Twitter using a transformer-based language model (arXiv:2007.13306). arXiv. https://doi.org/10.48550/arXiv.2007.13306
• Klinkhammer, D. (2022). Sentiment analysis with R: Natural language processing for semi-automated assessments of qualitative data (arXiv:2206.12649). arXiv. https://doi.org/10.48550/arXiv.2206.12649
• Kreps, B. H. (2020). The rising costs of fossil-fuel extraction: An energy crisis that will not go away. The American Journal of Economics and Sociology, 79(3), 695–717. https://doi.org/10.1111/ajes.12336
• Lee, J.-H. (2019). The willingness of ınformation exchange on social media environment. 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), 1–2. https://doi.org/10.1109/ICCE-TW46550.2019.8991774
• Li, R., Wang, X., & Wang, Q. (2022). Does renewable energy reduce ecological footprint at the expense of economic growth? An empirical analysis of 120 countries. Journal of Cleaner Production, 346, 131207. https://doi.org/10.1016/j.jclepro.2022.131207
• Macanovic, A. (2022). Text mining for social science – The state and the future of computational text analysis in sociology. Social Science Research, 108, 102784. https://doi.org/10.1016/j.ssresearch.2022.102784
• Mateo-Tomás, P., & López-Bao, J. V. (2022). A nuclear future for biodiversity conservation? Biological Conservation, 270, 109559. https://doi.org/10.1016/j.biocon.2022.109559
• Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(1), 81. https://doi.org/10.1007/s13278-021-00776-6
• Omri, A., & Belaïd, F. (2021). Does renewable energy modulate the negative effect of environmental issues on the socio-economic welfare? Journal of Environmental Management, 278, 111483. https://doi.org/10.1016/j.jenvman.2020.111483
• Ovchinnikova, S., Borovkov, A., Kukinova, G., & Markina, N. (2021). Environmental substantiation for the use of alternative energy sources. E3S Web of Conferences, 244, 01007. https://doi.org/10.1051/e3sconf/202124401007
• Qazi, A., Hussain, F., Rahim, N. Abd., Hardaker, G., Alghazzawi, D., Shaban, K., & Haruna, K. (2019). Towards sustainable energy: A systematic review of renewable energy sources, technologies, and public opinions. IEEE Access, 7, 63837–63851. https://doi.org/10.1109/ACCESS.2019.2906402
• Ragosa, G., & Warren, P. (2019). Unpacking the determinants of cross-border private investment in renewable energy in developing countries. Journal of Cleaner Production, 235, 854–865. https://doi.org/10.1016/j.jclepro.2019.06.166
• Raheman, A., Kolonin, A., Fridkins, I., Ansari, I., & Vishwas, M. (2022). Social media sentiment analysis for cryptocurrency market prediction (arXiv:2204.10185). arXiv. https://doi.org/10.48550/arXiv.2204.10185
• Rana, M. M. P., & Ilina, I. N. (2021). Climate change and migration impacts on cities: Lessons from Bangladesh. Environmental Challenges, 5, 100242. https://doi.org/10.1016/j.envc.2021.100242
• Ravindra, K., Rattan, P., Mor, S., & Aggarwal, A. N. (2019). Generalized additive models: Building evidence of air pollution, climate change and human health. Environment International, 132, 104987. https://doi.org/10.1016/j.envint.2019.104987
• Razi, F., & Dincer, I. (2022). Renewable energy development and hydrogen economy in MENA region: A review. Renewable and Sustainable Energy Reviews, 168, 112763. https://doi.org/10.1016/j.rser.2022.112763
• Sarjou, A. (2021). The power of language: Understanding sentiment towards the climate emergency using Twitter data (arXiv:2101.10376). arXiv. https://doi.org/10.48550/arXiv.2101.10376
• Sasankan, A., Jones, K., Sweeney, E., & Knight, L. (2019, September 5). Information exchange in supply chain management: Social media as an enabler. Logistics Research Network Annual Conference, LRN 2019. https://research.utwente.nl/en/publications/information-exchange-in-supply-chain-management-social-media-as-a
• Saud, M., Mashud, M., & Ida, R. (2020). Usage of social media during the pandemic: Seeking support and awareness about COVID-19 through social media platforms. Journal of Public Affairs, 20(4), e2417. https://doi.org/10.1002/pa.2417
• Shahbaz, M., Raghutla, C., Chittedi, K. R., Jiao, Z., & Vo, X. V. (2020). The effect of renewable energy consumption on economic growth: Evidence from the renewable energy country attractive index. Energy, 207, 118162. https://doi.org/10.1016/j.energy.2020.118162
• Vågerö, O., Bråte, A., Wittemann, A., Robinson, J. Y., Sirotko-Sibirskaya, N., & Zeyringer, M. (2023). Machine learning of public sentiments toward wind energy in Norway (arXiv:2304.02388). arXiv. https://doi.org/10.48550/arXiv.2304.02388
• Vakulchuk, R., Overland, I., & Scholten, D. (2020). Renewable energy and geopolitics: A review. Renewable and Sustainable Energy Reviews, 122, 109547. https://doi.org/10.1016/j.rser.2019.109547
• Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1