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Social and rewarding microscopical dynamics in blockchain-based online social networks

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Published:09 September 2021Publication History

ABSTRACT

The rising of online social platforms makes large volumes of data about social relationships and interactions available to the research community. In the varied ecosystem of techno-social platforms, blockchain-based online social networks - BOSNs - are gaining momentum since the underlying blockchain offers data validation, data storage, and data decentralization. As data sources, BOSNs provide high-resolution temporal data about the evolution of the social network and on the interactions of users with the platform services. In this study, we focus on a few temporal characteristics, by analyzing the dynamics of the link creation process and the claiming of rewards in the BOSN Steemit. We model blockchain data as a temporal directed network from which we extract the time series characterizing link creation and reward claims. Adopting a user-centric approach, we evaluate the heterogeneity of the time series through the inter-event time distribution, the burstiness, the bursty train size distribution, and the fitting of inter-event times by power law models. The outcomes of the analysis highlight that the above processes show bursty traits typical of human dynamics. However, the two aspects present a few differences concerning the types of models describing their behavior and the time scale of their bursty nature. To sum up, the creation of new relationships and the reward claim dynamics ask for specific models able to reproduce their general bursty traits but taking into account their specificities and relations with other services and mechanisms offered by BOSN platforms.

References

  1. Jeff Alstott, Ed Bullmore, and Dietmar Plenz. 2014. powerlaw: a Python package for analysis of heavy-tailed distributions. PloS one 9, 1 (2014), e85777.Google ScholarGoogle ScholarCross RefCross Ref
  2. Cheick Tidiane Ba, Matteo Zignani, Sabrina Gaito, and Gian Paolo Rossi. 2021. The Effect of Cryptocurrency Price on a Blockchain-Based Social Network. In Complex Networks & Their Applications IX. Springer International Publishing, Cham, 581--592.Google ScholarGoogle Scholar
  3. Usman W Chohan. 2018. The concept and criticisms of Steemit. CBRI Working Papers: Notes on the 21st Century, Available at SSRN: http://dx.doi.org/10.2139/ssrn.3129410.Google ScholarGoogle Scholar
  4. Raffaele Ciriello, Roman Beck, and Jason Thatcher. 2018. The Paradoxical Effects of Blockchain Technology on Social Networking Practices. In Proceedings of the Thirty Ninth International Conference on Information Systems. AIS.Google ScholarGoogle ScholarCross RefCross Ref
  5. Aaron Clauset, Cosma Rohilla Shalizi, and Mark EJ Newman. 2009. Power-law distributions in empirical data. SIAM review 51, 4 (2009), 661--703.Google ScholarGoogle Scholar
  6. Sabrina Gaito, Matteo Zignani, Gian Paolo Rossi, Alessandra Sala, Xiaohan Zhao, Haitao Zheng, and Ben Y Zhao. 2012. On the bursty evolution of online social networks. In Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research. ACM, New York, NY, 1--8.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. K-I Goh and A-L Barabási. 2008. Burstiness and memory in complex systems. EPL (Europhysics Letters) 81, 4 (2008), 48002.Google ScholarGoogle ScholarCross RefCross Ref
  8. Barbara Guidi. 2020. When Blockchain meets Online Social Networks. Pervasive and Mobile Computing 62 (2020), 101131.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Barbara Guidi, Andrea Michienzi, and Laura Ricci. 2020. A Graph-Based Socioeconomic Analysis of Steemit. IEEE Transactions on Computational Social Systems PP (12 2020), 1--12. https://doi.org/10.1109/TCSS.2020.3042745Google ScholarGoogle Scholar
  10. Barbara Guidi, Andrea Michienzi, and Laura Ricci. 2020. Steem Blockchain: Mining the Inner Structure of the Graph. IEEE Access 8 (11 2020). https://doi.org/10.1109/ACCESS.2020.3038550Google ScholarGoogle Scholar
  11. Barbara Guidi, Andrea Michienzi, and Laura Ricci. 2021. Analysis of Witnesses in the Steem Blockchain. Mobile Networks and Applications (2021), 1--12.Google ScholarGoogle Scholar
  12. Hive. 2020. Hive WhitePaper. Online. https://hive.io/whitepaper.pdfGoogle ScholarGoogle Scholar
  13. Petter Holme. 2015. Modern temporal network theory: a colloquium. The European Physical Journal B 88, 9 (2015), 1--30.Google ScholarGoogle ScholarCross RefCross Ref
  14. Petter Holme and Jari Saramäki. 2019. Temporal network theory. Vol. 2. Springer, New York City, NY.Google ScholarGoogle Scholar
  15. P. Jia and C. Yin. 2019. Research on the Characteristics of Community Network Information Transmission in Blockchain Environment. In IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Vol. 1. IEEE, New York, NY, 2296--2300.Google ScholarGoogle Scholar
  16. K. Kapanova, Barbara Guidi, Andrea Michienzi, and K. Koidl. 2020. Evaluating Posts on the Steemit Blockchain: Analysis on Topics Based on Textual Cues. In Proceedings of the 6th EAI International Conference on Smart Objects and Technologies for Social Good. EAI.Google ScholarGoogle Scholar
  17. Márton Karsai, Hang-Hyun Jo, Kimmo Kaski, et al. 2018. Bursty human dynamics. Springer, New York, NY.Google ScholarGoogle Scholar
  18. A. Kiayias, B. Livshits, Andrés Monteoliva Mosteiro, and O. Litos. 2019. A Puff of Steem: Security Analysis of Decentralized Content Curation. ArXiv abs/1810.01719 (2019).Google ScholarGoogle Scholar
  19. Eun-Kyeong Kim and Hang-Hyun Jo. 2016. Measuring burstiness for finite event sequences. Physical Review E 94, 3 (2016), 032311.Google ScholarGoogle ScholarCross RefCross Ref
  20. Moon Soo Kim and Jee Yong Chung. 2019. Sustainable growth and token economy design: The case of Steemit. Sustainability 11, 1 (2019), 167.Google ScholarGoogle ScholarCross RefCross Ref
  21. Tae-Hyun Kim, Hyo min Shin, H. Hwang, and Seungwon Jeong. 2020. Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques. ArXiv abs/2008.12471 (2020).Google ScholarGoogle Scholar
  22. Chao Li and Balaji Palanisamy. 2019. Incentivized blockchain-based social media platforms: A case study of steemit. In Proceedings of the 10th ACM Conference on Web Science. 145--154.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. C. Li, B. Palanisamy, Runhua Xu, Jinlai Xu, and Jingzhe Wang. 2021. SteemOps: Extracting and Analyzing Key Operations in Steemit Blockchain-based Social Media Platform. Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy (2021).Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. M. Thelwall. 2018. Can social news websites pay for content and curation? The SteemIt cryptocurrency model. Journal of Information Science 44 (2018), 736--751.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Rongen Zhang, Junyoung Park, and Raffaele Ciriello. 2019. The Differential Effects of Cryptocurrency Incentives in Blockchain Social Networks.Google ScholarGoogle Scholar

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        cover image ACM Conferences
        GoodIT '21: Proceedings of the Conference on Information Technology for Social Good
        September 2021
        345 pages
        ISBN:9781450384780
        DOI:10.1145/3462203

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        • Published: 9 September 2021

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