skip to main content
10.1145/3599696.3612901acmconferencesArticle/Chapter ViewAbstractPublication PageshtConference Proceedingsconference-collections
research-article

A First Look at User-Controlled Moderation on Web3 Social Media: The Case of Memo.cash

Published: 04 September 2023 Publication History

Abstract

Web3 social media strives to eliminate the need for centralized management by building upon on technologies such public ledgers and smart contracts. This has generated significant hype, but creates many notable challenges, many of which remain unaddressed. One such challenge is content moderation. Specifically, the immutable nature of blockchain means that it becomes impossible to retrospectively delete posts. This means that illegal content posts cannot be moderated or removed. In an attempt to overcome this, Web3 platforms, such as memo.cash, allow users to filter out posts from their personal timelines. Taking memo.cash as a use case, the goal of this work is to study the efficacy of the approach, and identify associated challenges. A particularly unique feature of memo.cash is that users must pay money (satoshi) for each social action (e.g., posting and blocking other users). We conjecture that this may impact the nature of moderation, particularly among poorer users. To explore this, we gather data from memo.cash covering 24K users, 317K posts, and 2.57M user actions. We investigate how the need to pay may impact the moderation system and propose potential solutions to address the challenges that arise.

References

[1]
Ahmed Abbasi, Abdul Rehman Javed, Farkhund Iqbal, Natalia Kryvinska, and Zunera Jalil. 2022. Deep learning for religious and continent-based toxic content detection and classification. Scientific Reports 12, 1 (2022), 17478.
[2]
Yong-Yeol Ahn, Seungyeop Han, Haewoon Kwak, Sue Moon, and Hawoong Jeong. 2007. Analysis of topological characteristics of huge online social networking services. In Proceedings of the 16th international conference on World Wide Web. 835–844.
[3]
Shiza Ali, Mohammad Hammas Saeed, Esraa Aldreabi, Jeremy Blackburn, Emiliano De Cristofaro, Savvas Zannettou, and Gianluca Stringhini. 2021. Understanding the effect of deplatforming on social networks. In 13th acm web science conference 2021. 187–195.
[4]
Ishaku Hassan Anaobi, Aravindh Raman, Ignacio Castro, Haris Bin Zia, Damilola Ibosiola, and Gareth Tyson. 2023. Will Admins Cope? Decentralized Moderation in the Fediverse. In Proceedings of the ACM Web Conference 2023. 3109–3120.
[5]
M. Arquam, A. Singh, and R. Sharma. 2018. A blockchain based Secure and Trusted framework for Information Propagation on Online Social Networks. (2018).
[6]
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. 127–132.
[7]
Cheick Tidiane Ba, Matteo Zignani, and Sabrina Gaito. 2022. The role of cryptocurrency in the dynamics of blockchain-based social networks: The case of Steemit. PloS one 17, 6 (2022), e0267612.
[8]
Z. H. Bin, I. Castro, A. I. Hassan, A. Raman, C. E. De, N. Sastry, and G. Tyson. 2022. Toxicity in the Decentralized Web and the Potential for Model Sharing. Performance Evaluation Review1 (2022), 50.
[9]
Haris Bin Zia, Aravindh Raman, Ignacio Castro, Ishaku Hassan Anaobi, Emiliano De Cristofaro, Nishanth Sastry, and Gareth Tyson. 2022. Toxicity in the decentralized web and the potential for model sharing. Proceedings of the ACM on Measurement and Analysis of Computing Systems 6, 2 (2022), 1–25.
[10]
Hongbo Bo, Ryan McConville, Jun Hong, and Weiru Liu. 2022. Ego-graph replay based continual learning for misinformation engagement prediction. In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 01–08.
[11]
Derek Caelin. 2022. Decentralized networks vs the trolls. In Fundamental Challenges to Global Peace and Security: The Future of Humanity. Springer, 143–168.
[12]
Dongmei Cao, Maureen Meadows, Donna Wong, and Senmao Xia. 2021. Understanding consumers’ social media engagement behaviour: An examination of the moderation effect of social media context. Journal of Business Research 122 (2021), 835–846.
[13]
Samuel Carton, Qiaozhu Mei, and Paul Resnick. 2020. Feature-based explanations don’t help people detect misclassifications of online toxicity. In Proceedings of the international AAAI conference on web and social media, Vol. 14. 95–106.
[14]
C. Cerisara, S. Jafaritazehjani, A. Oluokun, and H. Le. 2018. Multi-task dialog act and sentiment recognition on Mastodon.
[15]
Abdelberi Chaabane, Yuan Ding, Ratan Dey, Mohamed Ali Kaafar, and Keith W Ross. 2014. A closer look at third-party OSN applications: are they leaking your personal information?. In Passive and Active Measurement: 15th International Conference, PAM 2014, Los Angeles, CA, USA, March 10-11, 2014, Proceedings 15. Springer, 235–246.
[16]
Xu Cheng, Cameron Dale, and Jiangchuan Liu. 2008. Statistics and social network of youtube videos. In 2008 16th Interntional Workshop on Quality of Service. IEEE, 229–238.
[17]
Wallace Chipidza. 2021. The effect of toxicity on COVID-19 news network formation in political subcommunities on Reddit: An affiliation network approach. International Journal of Information Management 61 (2021), 102397.
[18]
Giovanni De Gregorio. 2020. Democratising online content moderation: A constitutional framework. Computer Law & Security Review 36 (2020), 105374.
[19]
Terry Flew, Fiona Martin, and Nicolas Suzor. 2019. Internet regulation as media policy: Rethinking the question of digital communication platform governance. Journal of Digital Media & Policy 10, 1 (2019), 33–50.
[20]
Barbara Guidi. 2020. When blockchain meets online social networks. Pervasive and Mobile Computing 62 (2020), 101131.
[21]
Barbara Guidi, Andrea Michienzi, and Laura Ricci. 2022. Assessment of Wealth Distribution in Blockchain Online Social Media. IEEE Transactions on Computational Social Systems (2022).
[22]
Adil Hassan. 2017. Replication and availability in decentralised online social networks. (2017).
[23]
Anaobi Ishaku Hassan, Aravindh Raman, Ignacio Castro, Haris Bin Zia, Emiliano De Cristofaro, Nishanth Sastry, and Gareth Tyson. 2021. Exploring content moderation in the decentralised web: The pleroma case. In Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies. 328–335.
[24]
Shagun Jhaver, Sucheta Ghoshal, Amy Bruckman, and Eric Gilbert. 2018. Online harassment and content moderation: The case of blocklists. ACM Transactions on Computer-Human Interaction (TOCHI) 25, 2 (2018), 1–33.
[25]
L. Jiang and X. Zhang. 2019. BCOSN: A Blockchain-Based Decentralized Online Social Network. IEEE Transactions on Computational Social Systems PP, 99 (2019), 1–13.
[26]
Kristina Kapanova, Barbara Guidi, Andrea Michienzi, and Kevin 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. 163–168.
[27]
Do Yeon Kim, Xiaohang Li, Sheng Wang, Yunying Zhuo, and Roy Ka-Wei Lee. 2019. Topic enhanced word embedding for toxic content detection in Q&A sites. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 1064–1071.
[28]
David Koll, Jun Li, Joshua Stein, and Xiaoming Fu. 2014. On the state of OSN-based Sybil defenses. In 2014 IFIP Networking Conference. IEEE, 1–9.
[29]
Keita Kurita, Anna Belova, and Antonios Anastasopoulos. 2019. Towards robust toxic content classification. arXiv preprint arXiv:1912.06872 (2019).
[30]
M. Lata and A. Gupta. 2020. Role of Social Media in Environmental Democracy. Examining the Roles of IT and Social Media in Democratic Development and Social Change.
[31]
Yi Liu and Tuba Bakici. 2019. Enterprise social media usage: The motives and the moderating role of public social media experience. Computers in Human Behavior 101 (2019), 163–172.
[32]
Zhiyong Liu, Yueping Li, Qingfei Min, and Mengting Chang. 2022. User incentive mechanism in blockchain-based online community: An empirical study of steemit. Information & Management 59, 7 (2022), 103596.
[33]
Garrett Morrow, Briony Swire-Thompson, Jessica Montgomery Polny, Matthew Kopec, and John P Wihbey. 2022. The emerging science of content labeling: Contextualizing social media content moderation. Journal of the Association for Information Science and Technology 73, 10 (2022), 1365–1386.
[34]
Sarah Myers West. 2018. Censored, suspended, shadowbanned: User interpretations of content moderation on social media platforms. New Media & Society 20, 11 (2018), 4366–4383.
[35]
Radu Prodan, Nishant Saurabh, Zhiming Zhao, Kate Orton-Johnson, Antorweep Chakravorty, Aleksandar Karadimce, and Alexandre Ulisses. 2019. ARTICONF: towards a smart social media ecosystem in a blockchain federated environment. In European Conference on Parallel Processing. Springer, 417–428.
[36]
Julian Risch and Ralf Krestel. 2020. Toxic comment detection in online discussions. Deep learning-based approaches for sentiment analysis (2020), 85–109.
[37]
Martin Saveski, Brandon Roy, and Deb Roy. 2021. The structure of toxic conversations on twitter. In Proceedings of the Web Conference 2021. 1086–1097.
[38]
Aaron Shaw. 2012. Centralized and decentralized gatekeeping in an open online collective. Politics & Society 40, 3 (2012), 349–388.
[39]
Z. Sujon, H. T. Dyer, D. Zulli, M. Liu, and R. Gehl. 2020. Rethinking the "social" in "social media": Insights into topology, abstraction, and scale on the Mastodon social network:. New Media & Society 22, 7 (2020), 1188–1205.
[40]
J. Trienes, Andrés Torres Cano, and D. Hiemstra. 2018. Recommending Users: Whom to Follow on Federated Social Networks. arXiv e-prints (2018).
[41]
Joshua Uyheng and Kathleen M Carley. 2020. Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines. Journal of computational social science 3 (2020), 445–468.
[42]
José Van Dijck, Thomas Poell, and Martijn De Waal. 2018. The platform society: Public values in a connective world. Oxford University Press.
[43]
Juan Wang, Chao Li, and Chengyi Xia. 2018. Improved centrality indicators to characterize the nodal spreading capability in complex networks. Appl. Math. Comput. 334 (2018), 388–400.
[44]
Erjia Yan and Ying Ding. 2009. Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology 60, 10 (2009), 2107–2118.
[45]
M. Zignani, C. Quadri, S. Gaito, H. Cherifi, and G. P. Rossi. 2019. The Footprints of a "Mastodon": How a Decentralized Architecture Influences Online Social Relationships. In IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
[46]
Wenrui Zuo, Aravindh Raman, Raul J Mondragón, and Gareth Tyson. 2023. Set in Stone: Analysis of an Immutable Web3 Social Media Platform. In Proceedings of the ACM Web Conference 2023. 1865–1874.

Cited By

View all
  • (2024)Guardians of the galaxyProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3698985(1507-1524)Online publication date: 14-Aug-2024
  • (2024)The lure of decentralized social media: Extending the UTAUT model for understanding users’ adoption of blockchain-based social mediaPLOS ONE10.1371/journal.pone.030845819:8(e0308458)Online publication date: 7-Aug-2024

Index Terms

  1. A First Look at User-Controlled Moderation on Web3 Social Media: The Case of Memo.cash

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      OASIS '23: Proceedings of the 3rd International Workshop on Open Challenges in Online Social Networks
      September 2023
      53 pages
      ISBN:9798400702259
      DOI:10.1145/3599696
      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 the author(s) 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].

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 04 September 2023

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Web3
      2. decentralization
      3. memo.cash network measurement
      4. moderation system

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Funding Sources

      • EPSRC
      • EPSRC

      Conference

      HT '23
      Sponsor:

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)93
      • Downloads (Last 6 weeks)8
      Reflects downloads up to 08 Mar 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Guardians of the galaxyProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3698985(1507-1524)Online publication date: 14-Aug-2024
      • (2024)The lure of decentralized social media: Extending the UTAUT model for understanding users’ adoption of blockchain-based social mediaPLOS ONE10.1371/journal.pone.030845819:8(e0308458)Online publication date: 7-Aug-2024

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media