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Sosyal Medyada Kripto Para: Coin Piyasasına Yönelik Dijital Söylemin Analizi

Year 2024, , 202 - 223, 29.02.2024
https://doi.org/10.25204/iktisad.1419066

Abstract

Bu çalışma, kapsamlı bir sosyal medya söylem analizi yoluyla kripto para birimini çevreleyen kamu duyarlılığının dinamik manzarasını araştırmaktadır. Python Selenium Kütüphanesi kullanılarak büyük platformlardaki (X, Facebook, Instagram ve LinkedIn) 1000 genel profilden veriler sistematik olarak toplanmıştır. R Studio’da gelişmiş metin madenciliği teknikleri kullanılarak ‘Syuzhet’ paketi ile duygu analizi ve ‘tm’ paketi ile kelime sıklığı analizi yapılmıştır. Sonuçlar, başta öfke ve kayıp olmak üzere olumsuzluk ifadeleriyle iç içe geçmiş, baskın beklenti ve pozitiflik duygularıyla karakterize edilen incelikli bir duygusal manzarayı ortaya çıkarmıştır. Kelime sıklığı analizi, yerleşik kripto para birimleri (örn. Bitcoin, Ethereum), blok zinciri teknolojisi ve kripto para kullanımının pratik ve finansal yönleri gibi temel temaları vurgulamıştır. Çalışmada, teknik ilgi, finansal spekülasyon ve düzenleyici ve ekonomik gelişmelere verilen tepkileri aydınlatılmaktadır. Yatırımcılar ve politika yapıcılar da dahil olmak üzere paydaşlar için çok önemli bilgiler sunan bu araştırma, kripto para piyasalarının değişken doğasını ve blok zinciri teknolojisinin dönüştürücü potansiyelini vurgulayarak kamu duyarlılığının akademik olarak anlaşılmasına katkıda bulunmakta ve sürekli gelişen kripto para ekosisteminde politika, yatırım ve teknolojik yenilikleri bilgilendirmek için kamu duyarlılığının sürekli izlenmesi çağrısında bulunmaktadır.

References

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  • Abubakar, M., Hassan, M. K., and Haruna, M. A. (2019). Cryptocurrency tide and Islamic Finance development: Any issue? In J. Jay Choi and B. Ozkan (Eds.), Disruptive Innovation in Business and Finance in the Digital World (Vol. 20, pp. 189–200). Emerald Publishing Limited. https://doi.org/10.1108/S1569-376720190000020019
  • Albrecht, S., Lutz, B., and Neumann, D. (2019). How sentiment impacts the success of blockchain startups – an analysis of social media data and initial coin offerings. Hawaii International Conference on System Sciences 2019 (HICSS-52). https://aisel.aisnet.org/hicss-52/in/blockchain/3
  • Allen, F., Gu, X., and Jagtiani, J. (2022). Fintech, cryptocurrencies, and CBDC: Financial structural transformation in China. Journal of International Money and Finance, 124, 102625. https://doi.org/10.1016/j.jimonfin.2022.102625
  • Almeida, J., and Gonçalves, T. C. (2022). A systematic literature review of volatility and risk management on cryptocurrency investment: A methodological point of view. Risks, 10(5), Article 5. https://doi.org/10.3390/risks10050107
  • Amsyar, I., Christopher, E., Dithi, A., Khan, A. N., and Maulana, S. (2020). The challenge of cryptocurrency in the era of the digital revolution: A review of systematic literature. Aptisi Transactions on Technopreneurship (ATT), 2(2), 153–159.
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  • Biryukov, A., and Tikhomirov, S. (2019). Security and privacy of mobile wallet users in Bitcoin, Dash, Monero, and Zcash. Pervasive and Mobile Computing, 59, 101030. https://doi.org/10.1016/j.pmcj.2019.101030
  • Chervinski, J. O. M., Kreutz, D., and Yu, J. (2019). FloodXMR: Low-cost transaction flooding attack with Monero’s bulletproof protocol (2019/455). Cryptology ePrint Archive. https://eprint.iacr.org/2019/455
  • D’Amato, V., Levantesi, S., and Piscopo, G. (2022). Deep learning in predicting cryptocurrency volatility. Physica A: Statistical Mechanics and Its Applications, 596, 127158. https://doi.org/10.1016/j.physa.2022.127158
  • De Vries, A. (2023). Cryptocurrencies on the road to sustainability: Ethereum paving the way for Bitcoin. Patterns, 4(1), 100633. https://doi.org/10.1016/j.patter.2022.100633
  • Elaiyaraja, A., (2023). A Revolutionary Impact on Cryptocurrency. Emerging Insights on the Relationship Between Cryptocurrencies and Decentralized Economic Models (pp. 183–197). IGI Global. https://doi.org/10.4018/978-1-6684-5691-0.ch012
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  • Hamayel, M. J., and Owda, A. Y. (2021). A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms. AI, 2(4), Article 4. https://doi.org/10.3390/ai2040030
  • Haritha, G. B., and Sahana, N. B. (2023). Cryptocurrency Price Prediction using Twitter Sentiment Analysis. CS & IT Conference Proceedings, 13(3).
  • Hassan, M. K., Hudaefi, F. A., and Caraka, R. E. (2021). Mining netizen’s opinion on cryptocurrency: Sentiment analysis of Twitter data. Studies in Economics and Finance, 39(3), 365–385. https://doi.org/10.1108/SEF-06-2021-0237
  • Helmi, M. H., Çatık, A. N., and Akdeniz, C. (2023). The impact of central bank digital currency news on the stock and cryptocurrency markets: Evidence from the TVP-VAR model. Research in International Business and Finance, 65, 101968. https://doi.org/10.1016/j.ribaf.2023.101968
  • Huang, X., Zhang, W., Tang, X., Zhang, M., Surbiryala, J., Iosifidis, V., Liu, Z., and Zhang, J. (2021). LSTM Based Sentiment Analysis for Cryptocurrency Prediction. In C. S. Jensen, E.-P. Lim, D.-N. Yang, W.-C. Lee, V. S. Tseng, V. Kalogeraki, J.-W. Huang, & C.-Y. Shen (Eds.), Database Systems for Advanced Applications (pp. 617–621). Springer International Publishing. https://doi.org/10.1007/978-3-030-73200-4_47
  • Ibrahim, R., Harby, A. A., Nashwan, M. S., and Elhakeem, A. (2022). Financial Contract Administration in Construction via Cryptocurrency Blockchain and Smart Contract: A Proof of Concept. Buildings, 12(8), Article 8. https://doi.org/10.3390/buildings12081072
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  • Jain, S., Johari, S., and Delhibabu, R. (2023). Analyzing Cryptocurrency trends using Tweet Sentiment Data and User Meta-Data. https://doi.org/10.48550/arXiv.2307.15956
  • Kausar, M. A., Soosaimanickam, A., and Nasar, M. (2021). Public sentiment analysis on Twitter data during COVID-19 outbreak. International Journal of Advanced Computer Science and Applications, 12(2).
  • Khan, N., Ahmad, T., and State, R. (2019). Feasibility of stellar as a blockchain-based Micropayment System. In M. Qiu (Ed.), Smart Blockchain (pp. 53–65). Springer International Publishing. https://doi.org/10.1007/978-3-030-34083-4_6
  • Kim, S. R. (2022). How the Cryptocurrency Market is Connected to the Financial Market (SSRN Scholarly Paper 4106815). https://doi.org/10.2139/ssrn.4106815
  • Lansiaux, E., Tchagaspanian, N., and Forget, J. (2022). Community Impact on a Cryptocurrency: Twitter Comparison Example Between Dogecoin and Litecoin. Frontiers in Blockchain, 5. https://www.frontiersin.org/articles/10.3389/fbloc.2022.829865
  • Lee, J. Y. (2019). A decentralized token economy: How blockchain and cryptocurrency can revolutionize business. Business Horizons, 62(6), 773–784. https://doi.org/10.1016/j.bushor.2019.08.003
  • Li, Z., Wang, Y., and Huang, Z. (2020). Risk Connectedness Heterogeneity in the Cryptocurrency Markets. Frontiers in Physics, 8. https://www.frontiersin.org/articles/10.3389/fphy.2020.00243
  • Mallick, S. (2020). Causal relationship between Crypto currencies: An analytical study between bitcoin and binance Coin. Journal of Contemporary Issues in Business and Government, 26, 2171–2181. https://doi.org/10.47750/cibg.2020.26.02.265
  • Mondal, L., Raj, U., S, A., S, B. G., P, S., and Chandra, A. (2023). Causality between sentiment and cryptocurrency prices. https://doi.org/10.48550/arXiv.2306.05803
  • Morton, D. T. (2020). The future of cryptocurrency: An unregulated instrument in an increasingly regulated global economy. Loyola University Chicago International Law Review, 16, 129.
  • Nerurkar, P., Patel, D., Busnel, Y., Ludinard, R., Kumari, S., and Khan, M. K. (2021). Dissecting bitcoin blockchain: Empirical analysis of bitcoin network (2009–2020). Journal of Network and Computer Applications, 177, 102940. https://doi.org/10.1016/j.jnca.2020.102940
  • Nyamathulla, S., Ratnababu, D. P., Shaik, N. S., and N, B. L. (2021). A Review on Selenium Web Driver with Python. Annals of the Romanian Society for Cell Biology, 16760–16768.
  • Pierro, G. A., and Tonelli, R. (2022). Can solana be the solution to the blockchain scalability problem? 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 1219–1226. https://doi.org/10.1109/SANER53432.2022.00144
  • Raheman, A., Kolonin, A., Fridkins, I., Ansari, I., and Vishwas, M. (2022). Social media sentiment analysis for cryptocurrency market prediction. https://doi.org/10.48550/arXiv.2204.10185
  • Şaşmaz, E., and Tek, F. B. (2021). Tweet sentiment analysis for cryptocurrencies. 2021 6th International Conference on Computer Science and Engineering (UBMK), 613–618. https://doi.org/10.1109/UBMK52708.2021.9558914
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Cryptocurrency on Social Media: Analyzing the Digital Discourse Towards the Coin Market

Year 2024, , 202 - 223, 29.02.2024
https://doi.org/10.25204/iktisad.1419066

Abstract

This study delves into the dynamic landscape of public sentiment surrounding cryptocurrency through a comprehensive social media discourse analysis. Employing the Python Selenium library, data from 1000 public profiles across major platforms—X, Facebook, Instagram, and LinkedIn—were systematically collected. Using advanced text-mining techniques in R Studio, sentiment analysis was conducted with the ‘Syuzhet’ package and word frequency analysis via the ‘tm’ package. The results unveiled a nuanced emotional landscape characterized by dominant sentiments of anticipation and positivity, interwoven with expressions of negativity, notably anger, and loss. Word frequency analysis highlighted vital themes such as established cryptocurrencies (e.g., Bitcoin, Ethereum), blockchain technology, and practical and financial aspects of cryptocurrency usage. The study illuminated technical interest, financial speculation, and reactions to regulatory and economic developments. Offering insights crucial for stakeholders, including investors and policymakers, this research contributes to the academic understanding of public sentiment, emphasizing the volatile nature of crypto-currency markets and the transformative potential of blockchain technology and calls for ongoing monitoring of public sentiment to inform policy, investment, and technological innovation in the ever-evolving cryptocurrency ecosystem.

References

  • Abraham, J., Higdon, D., Nelson, J., and Ibarra, J. (2018). Cryptocurrency price prediction using tweet volumes and sentiment analysis. SMU Data Science Review, 1(3). https://scholar.smu.edu/datasciencereview/vol1/iss3/1/
  • Abubakar, M., Hassan, M. K., and Haruna, M. A. (2019). Cryptocurrency tide and Islamic Finance development: Any issue? In J. Jay Choi and B. Ozkan (Eds.), Disruptive Innovation in Business and Finance in the Digital World (Vol. 20, pp. 189–200). Emerald Publishing Limited. https://doi.org/10.1108/S1569-376720190000020019
  • Albrecht, S., Lutz, B., and Neumann, D. (2019). How sentiment impacts the success of blockchain startups – an analysis of social media data and initial coin offerings. Hawaii International Conference on System Sciences 2019 (HICSS-52). https://aisel.aisnet.org/hicss-52/in/blockchain/3
  • Allen, F., Gu, X., and Jagtiani, J. (2022). Fintech, cryptocurrencies, and CBDC: Financial structural transformation in China. Journal of International Money and Finance, 124, 102625. https://doi.org/10.1016/j.jimonfin.2022.102625
  • Almeida, J., and Gonçalves, T. C. (2022). A systematic literature review of volatility and risk management on cryptocurrency investment: A methodological point of view. Risks, 10(5), Article 5. https://doi.org/10.3390/risks10050107
  • Amsyar, I., Christopher, E., Dithi, A., Khan, A. N., and Maulana, S. (2020). The challenge of cryptocurrency in the era of the digital revolution: A review of systematic literature. Aptisi Transactions on Technopreneurship (ATT), 2(2), 153–159.
  • Ao, Z., Cong, L. W., Horvath, G., and Zhang, L. (2023). Is decentralized finance actually decentralized? A social network analysis of the Aave protocol on the Ethereum blockchain (arXiv:2206.08401).
  • Appel, G., Grewal, L., Hadi, R., and Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95. https://doi.org/10.1007/s11747-019-00695-1
  • Barua, S., and Varma, J. (2023). Tezos: Governance in the cryptocurrency world. https://doi.org/10.4135/9781529619638
  • Biryukov, A., and Tikhomirov, S. (2019). Security and privacy of mobile wallet users in Bitcoin, Dash, Monero, and Zcash. Pervasive and Mobile Computing, 59, 101030. https://doi.org/10.1016/j.pmcj.2019.101030
  • Chervinski, J. O. M., Kreutz, D., and Yu, J. (2019). FloodXMR: Low-cost transaction flooding attack with Monero’s bulletproof protocol (2019/455). Cryptology ePrint Archive. https://eprint.iacr.org/2019/455
  • D’Amato, V., Levantesi, S., and Piscopo, G. (2022). Deep learning in predicting cryptocurrency volatility. Physica A: Statistical Mechanics and Its Applications, 596, 127158. https://doi.org/10.1016/j.physa.2022.127158
  • De Vries, A. (2023). Cryptocurrencies on the road to sustainability: Ethereum paving the way for Bitcoin. Patterns, 4(1), 100633. https://doi.org/10.1016/j.patter.2022.100633
  • Elaiyaraja, A., (2023). A Revolutionary Impact on Cryptocurrency. Emerging Insights on the Relationship Between Cryptocurrencies and Decentralized Economic Models (pp. 183–197). IGI Global. https://doi.org/10.4018/978-1-6684-5691-0.ch012
  • Fang, F., Ventre, C., Basios, M., Kanthan, L., Martinez-Rego, D., Wu, F., and Li, L. (2022). Cryptocurrency trading: A comprehensive survey. Financial Innovation, 8(1), 13. https://doi.org/10.1186/s40854-021-00321-6
  • Fauzi, M. A., Paiman, N., and Othman, Z. (2020). Bitcoin and cryptocurrency: Challenges, opportunities and future works. The Journal of Asian Finance, Economics and Business (JAFEB), 7(8), 695–704.
  • Goodkind, A. L., Jones, B. A., and Berrens, R. P. (2020). Cryptodamages: Monetary value estimates of the air pollution and human health impacts of cryptocurrency mining. Energy Research & Social Science, 59, 101281. https://doi.org/10.1016/j.erss.2019.101281
  • Hamayel, M. J., and Owda, A. Y. (2021). A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms. AI, 2(4), Article 4. https://doi.org/10.3390/ai2040030
  • Haritha, G. B., and Sahana, N. B. (2023). Cryptocurrency Price Prediction using Twitter Sentiment Analysis. CS & IT Conference Proceedings, 13(3).
  • Hassan, M. K., Hudaefi, F. A., and Caraka, R. E. (2021). Mining netizen’s opinion on cryptocurrency: Sentiment analysis of Twitter data. Studies in Economics and Finance, 39(3), 365–385. https://doi.org/10.1108/SEF-06-2021-0237
  • Helmi, M. H., Çatık, A. N., and Akdeniz, C. (2023). The impact of central bank digital currency news on the stock and cryptocurrency markets: Evidence from the TVP-VAR model. Research in International Business and Finance, 65, 101968. https://doi.org/10.1016/j.ribaf.2023.101968
  • Huang, X., Zhang, W., Tang, X., Zhang, M., Surbiryala, J., Iosifidis, V., Liu, Z., and Zhang, J. (2021). LSTM Based Sentiment Analysis for Cryptocurrency Prediction. In C. S. Jensen, E.-P. Lim, D.-N. Yang, W.-C. Lee, V. S. Tseng, V. Kalogeraki, J.-W. Huang, & C.-Y. Shen (Eds.), Database Systems for Advanced Applications (pp. 617–621). Springer International Publishing. https://doi.org/10.1007/978-3-030-73200-4_47
  • Ibrahim, R., Harby, A. A., Nashwan, M. S., and Elhakeem, A. (2022). Financial Contract Administration in Construction via Cryptocurrency Blockchain and Smart Contract: A Proof of Concept. Buildings, 12(8), Article 8. https://doi.org/10.3390/buildings12081072
  • Inder, S. (2022). Linkages Among Cryptocurrencies: A Network Analysis Approach. Handbook of Research on Stock Market Investment Practices and Portfolio Management (pp. 392–408). IGI Global. https://doi.org/10.4018/978-1-6684-5528-9.ch021
  • Jain, S., Johari, S., and Delhibabu, R. (2023). Analyzing Cryptocurrency trends using Tweet Sentiment Data and User Meta-Data. https://doi.org/10.48550/arXiv.2307.15956
  • Kausar, M. A., Soosaimanickam, A., and Nasar, M. (2021). Public sentiment analysis on Twitter data during COVID-19 outbreak. International Journal of Advanced Computer Science and Applications, 12(2).
  • Khan, N., Ahmad, T., and State, R. (2019). Feasibility of stellar as a blockchain-based Micropayment System. In M. Qiu (Ed.), Smart Blockchain (pp. 53–65). Springer International Publishing. https://doi.org/10.1007/978-3-030-34083-4_6
  • Kim, S. R. (2022). How the Cryptocurrency Market is Connected to the Financial Market (SSRN Scholarly Paper 4106815). https://doi.org/10.2139/ssrn.4106815
  • Lansiaux, E., Tchagaspanian, N., and Forget, J. (2022). Community Impact on a Cryptocurrency: Twitter Comparison Example Between Dogecoin and Litecoin. Frontiers in Blockchain, 5. https://www.frontiersin.org/articles/10.3389/fbloc.2022.829865
  • Lee, J. Y. (2019). A decentralized token economy: How blockchain and cryptocurrency can revolutionize business. Business Horizons, 62(6), 773–784. https://doi.org/10.1016/j.bushor.2019.08.003
  • Li, Z., Wang, Y., and Huang, Z. (2020). Risk Connectedness Heterogeneity in the Cryptocurrency Markets. Frontiers in Physics, 8. https://www.frontiersin.org/articles/10.3389/fphy.2020.00243
  • Mallick, S. (2020). Causal relationship between Crypto currencies: An analytical study between bitcoin and binance Coin. Journal of Contemporary Issues in Business and Government, 26, 2171–2181. https://doi.org/10.47750/cibg.2020.26.02.265
  • Mondal, L., Raj, U., S, A., S, B. G., P, S., and Chandra, A. (2023). Causality between sentiment and cryptocurrency prices. https://doi.org/10.48550/arXiv.2306.05803
  • Morton, D. T. (2020). The future of cryptocurrency: An unregulated instrument in an increasingly regulated global economy. Loyola University Chicago International Law Review, 16, 129.
  • Nerurkar, P., Patel, D., Busnel, Y., Ludinard, R., Kumari, S., and Khan, M. K. (2021). Dissecting bitcoin blockchain: Empirical analysis of bitcoin network (2009–2020). Journal of Network and Computer Applications, 177, 102940. https://doi.org/10.1016/j.jnca.2020.102940
  • Nyamathulla, S., Ratnababu, D. P., Shaik, N. S., and N, B. L. (2021). A Review on Selenium Web Driver with Python. Annals of the Romanian Society for Cell Biology, 16760–16768.
  • Pierro, G. A., and Tonelli, R. (2022). Can solana be the solution to the blockchain scalability problem? 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 1219–1226. https://doi.org/10.1109/SANER53432.2022.00144
  • Raheman, A., Kolonin, A., Fridkins, I., Ansari, I., and Vishwas, M. (2022). Social media sentiment analysis for cryptocurrency market prediction. https://doi.org/10.48550/arXiv.2204.10185
  • Şaşmaz, E., and Tek, F. B. (2021). Tweet sentiment analysis for cryptocurrencies. 2021 6th International Conference on Computer Science and Engineering (UBMK), 613–618. https://doi.org/10.1109/UBMK52708.2021.9558914
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There are 57 citations in total.

Details

Primary Language English
Subjects Capital Market, Business Administration
Journal Section Research Papers
Authors

Hafize Nurgül Durmuş Şenyapar 0000-0003-0927-1643

Early Pub Date February 29, 2024
Publication Date February 29, 2024
Submission Date January 13, 2024
Acceptance Date February 27, 2024
Published in Issue Year 2024

Cite

APA Durmuş Şenyapar, H. N. (2024). Cryptocurrency on Social Media: Analyzing the Digital Discourse Towards the Coin Market. İktisadi İdari Ve Siyasal Araştırmalar Dergisi, 9(23), 202-223. https://doi.org/10.25204/iktisad.1419066
AMA Durmuş Şenyapar HN. Cryptocurrency on Social Media: Analyzing the Digital Discourse Towards the Coin Market. İKTİSAD. February 2024;9(23):202-223. doi:10.25204/iktisad.1419066
Chicago Durmuş Şenyapar, Hafize Nurgül. “Cryptocurrency on Social Media: Analyzing the Digital Discourse Towards the Coin Market”. İktisadi İdari Ve Siyasal Araştırmalar Dergisi 9, no. 23 (February 2024): 202-23. https://doi.org/10.25204/iktisad.1419066.
EndNote Durmuş Şenyapar HN (February 1, 2024) Cryptocurrency on Social Media: Analyzing the Digital Discourse Towards the Coin Market. İktisadi İdari ve Siyasal Araştırmalar Dergisi 9 23 202–223.
IEEE H. N. Durmuş Şenyapar, “Cryptocurrency on Social Media: Analyzing the Digital Discourse Towards the Coin Market”, İKTİSAD, vol. 9, no. 23, pp. 202–223, 2024, doi: 10.25204/iktisad.1419066.
ISNAD Durmuş Şenyapar, Hafize Nurgül. “Cryptocurrency on Social Media: Analyzing the Digital Discourse Towards the Coin Market”. İktisadi İdari ve Siyasal Araştırmalar Dergisi 9/23 (February 2024), 202-223. https://doi.org/10.25204/iktisad.1419066.
JAMA Durmuş Şenyapar HN. Cryptocurrency on Social Media: Analyzing the Digital Discourse Towards the Coin Market. İKTİSAD. 2024;9:202–223.
MLA Durmuş Şenyapar, Hafize Nurgül. “Cryptocurrency on Social Media: Analyzing the Digital Discourse Towards the Coin Market”. İktisadi İdari Ve Siyasal Araştırmalar Dergisi, vol. 9, no. 23, 2024, pp. 202-23, doi:10.25204/iktisad.1419066.
Vancouver Durmuş Şenyapar HN. Cryptocurrency on Social Media: Analyzing the Digital Discourse Towards the Coin Market. İKTİSAD. 2024;9(23):202-23.


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