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Fork-based user migration in Blockchain Online Social Media

Published: 26 June 2022 Publication History

Abstract

Nowadays, Online Social Media (OSM) are among the most popular web services. Traditional OSM are known to be affected by serious issues including misinformation, fake news, censorship, and privacy violations, to the point that a pressing demand for new paradigms is raised by users all over the world. Among such paradigms, the concepts around the Web 3.0 are fueling a new revolution of online sociality, pushing towards the adoption of innovative and groundbreaking technologies. In particular, the decentralization of social services through the blockchain technology is representing the most valid alternative to current OSM, enabling the development of rewarding strategies for value redistribution, and fake news detection. However, the so-called Blockchain Online Social Media (BOSMs) are far from being mature, with different platforms that continually try to redefine their services in order to attract larger audiences, thus causing blockchain forks and massive user migrations, with the latter dominating the dynamics of the current OSM landscape, too.
In this paper, we deal with the evolution of BOSMs from the perspective of user migration across platforms as a consequence of a fork event. We propose a general user migration model applicable to BOSMs to represent the evolution patterns of fork-based migrations, the multi-interaction structural complexity of BOSMs, and their growth characteristics. Within this framework, we also cope with the task of predicting how users will behave in the case of a fork, i.e. they will remain on the original blockchain or they will migrate to the new one. We apply our framework to the case study of the Steem-Hive fork event, and show the importance of considering both social and economic information, regardless of the learning algorithm considered. To the best of our knowledge, this is the first study on blockchain fork and its related user migration.

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References

[1]
Cheick Tidiane Ba, Matteo Zignani, and Sabrina Gaito. 2021. Social and rewarding microscopical dynamics in blockchain-based online social networks. In Proceedings of the Conference on Information Technology for Social Good. ACM, 127–132.
[2]
Nitesh Chawla, Kevin Bowyer, Lawrence Hall, and W. Kegelmeyer. 2002. SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. (JAIR) 16 (06 2002), 321–357.
[3]
Thomas H Davenport and John C Beck. 2001. The attention economy: Understanding the new currency of business.
[4]
Cai Davies, James R. Ashford, Luis Espinosa-Anke, Alun David Preece, Liam D. Turner, Roger M. Whitaker, Mudhakar Srivatsa, and Diane H Felmlee. 2021. Multi-scale user migration on Reddit. In Workshop on Cyber Social Threats at the 15th International AAAI Conference on Web and Social Media (ICWSM 2021). AAAI.
[5]
Steemit developer documentation. 2021. Broadcast Ops. https://developers.steem.io/apidefinitions/broadcast-ops
[6]
Massimo Di Pierro. 2017. What is the blockchain?Computing in Science & Engineering 19, 5 (2017), 92–95.
[7]
Hive Developer Documentation. 2021. API Docs - API Definitions. https://developers.hive.io/apidefinitions/
[8]
Pierluigi Freni, Enrico Ferro, and G Ceci. 2020. Fixing social media with the blockchain. In Proceedings of the 6th EAI international conference on smart objects and technologies for social good. 175–180.
[9]
Barbara Guidi. 2020. When Blockchain meets Online Social Networks. Pervasive and Mobile Computing 62 (2020), 101131.
[10]
Barbara Guidi, Andrea Michienzi, and Laura Ricci. 2020. Steem Blockchain: Mining the Inner Structure of the Graph. IEEE Access 8(2020), 210251–210266.
[11]
Barbara Guidi, Andrea Michienzi, and Laura Ricci. 2021. A Graph-Based Socioeconomic Analysis of Steemit. IEEE Transactions on Computational Social Systems 8, 2 (2021), 365–376.
[12]
Petter Holme and Jari Saramäki. 2012. Temporal networks. Physics Reports 519, 3 (2012), 97–125. Temporal Networks.
[13]
Le Jiang and Xinglin Zhang. 2019. BCOSN: A blockchain-based decentralized online social network. IEEE Transactions on Computational Social Systems 6, 6 (2019), 1454–1466.
[14]
Alan E Kazdin. 2017. The token economy. In Applications of conditioning theory. 59–80.
[15]
Moon Soo Kim and Jee Yong Chung. 2019. Sustainable growth and token economy design: The case of steemit. Sustainability 11, 1 (2019), 167.
[16]
Mikko Kivelä, Alex Arenas, Marc Barthelemy, James P. Gleeson, Yamir Moreno, and Mason A. Porter. 2014. Multilayer networks. Journal of Complex Networks 2, 3 (07 2014), 203–271.
[17]
Shamanth Kumar, Reza Zafarani, and Huan Liu. 2011. Understanding User Migration Patterns in Social Media. In AAAI.
[18]
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(WebSci19). 145–154.
[19]
Liqun Liu, Weihan Zhang, and Cunqi Han. 2021. A survey for the application of blockchain technology in the media. Peer-to-Peer Networking and Applications(2021), 1–23.
[20]
Seth A Myers, Aneesh Sharma, Pankaj Gupta, and Jimmy Lin. 2014. Information network or social network?: the structure of the twitter follow graph. In Proceedings of the 23rd International Conference on World Wide Web(WWW14). ACM, 493–498.
[21]
Edward Newell, David Jurgens, Haji Mohammad Saleem, Hardik Vala, Jad Sassine, Caitrin Armstrong, and Derek Ruths. 2016. User Migration in Online Social Networks: A Case Study on Reddit During a Period of Community Unrest. In ICWSM.
[22]
T Poongodi, R Sujatha, D Sumathi, P Suresh, and B Balamurugan. 2020. Blockchain in social networking. Cryptocurrencies and Blockchain Technology Applications (2020), 55–76.
[23]
Sarwar Sayeed and Hector Marco-Gisbert. 2019. Assessing blockchain consensus and security mechanisms against the 51% attack. Applied Sciences 9, 9 (2019), 1788.
[24]
Malith Senaweera, Ruwanmalee Dissanayake, Nuwini Chamindi, Anupa Shyamalal, Charitha Elvitigala, Sameera Horawalavithana, Primal Wijesekara, Kasun Gunawardana, Manjusri Ishwara Ellepola Wickramasinghe, and Chamath Keppitiyagama. 2018. A Weighted Network Analysis of User Migrations in a Social Network. 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer) (2018), 357–362.

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            cover image ACM Conferences
            WebSci '22: Proceedings of the 14th ACM Web Science Conference 2022
            June 2022
            479 pages
            ISBN:9781450391917
            DOI:10.1145/3501247
            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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            Publication History

            Published: 26 June 2022

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            Author Tags

            1. Blockchain Online Social Media
            2. Temporal Networks
            3. User Migration

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            WebSci '22: 14th ACM Web Science Conference 2022
            June 26 - 29, 2022
            Barcelona, Spain

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