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Detecting disruptive events within digital communities: Visualizing Data from Cryptocurrency and Dark Web Communities


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Dr Alexia Maddox, Lecturer in Communications, School of Communications and Creative Arts, Deakin University.

From a community studies perspective the adaptation of digital communities to dynamic environments has meant that they are more difficult to detect and characterise. However, I argue that the built environment of a digital community can be understood through its digital and material construction articulated within social practices of engagement (ie where people are actively connecting with each other). In my previous work characterising the community of people with reptile interests, I offered a conceptual model of social ecology. This model was built with the key agenda to support the animation of social form through dimensional understandings of socio-technical connections from physical place to code. Here I offered connected research that illustrated ways to identify the built environment, the social layer and the mediating culture of a community. This model aimed to support the production of layered data that created social surfaces where there are densities of interaction. Moving to my present research into the community surrounding cryptocurrencies, I am progressing through three case studies that seek to provide data surfaces that can model community processes, particularly those that are disruptive. In this paper, I discuss these three case studies and their progressive illustrations of both internal and external disruption within community structures. The first study broaches a social media analysis of public discourse surrounding cryptocurrencies. It will focus on identifying and characterising contentious engagements and community disputes over the last five years. The second case study will move to working with cyber-libertarian discourse within the contentious environments of the dark web. The method for this study is yet to be determined and is likely to combine digital trace data analysis with community engagement. This study will seek to generate big data-small data relationships that provide both the digital imprint and lived experience of internal and external disruption within a radical community context. The final case study will work to identify ways to visualize the information collected and curated in case study one and two into 3D data visualization formats. The aim here will be to generate data recognition practices to search for signatures of socio-technical disruption, where disruptive events characterise tensions and tipping points for community cohesion.

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Detecting disruptive events within digital communities: Visualizing Data from Cryptocurrency and Dark Web Communities

  1. 1. Dr Alexia Maddox detecting disruption Visualizing data from digital communities surrounding cryptocurrencies and the dark web Twitter: @alexiamadd
  2. 2. Overview ❖ Research agenda ❖ Visualising social form ❖ Characterising digital communities ❖ Social surfaces ❖ Data alignments for visualisation ❖ Socio-technical disruption ❖ Case study 1: cryptocurrency community(ies) ❖ Case study 2: Dark web community(ies).
  3. 3. Research context ❖ Social Scientist specialising in the study of digital communities, digital social frontiers, and digital research methods including ❖ the study of collective behaviours online ❖ social media use ❖ digital cultures ❖ research methods to observe, monitor and engage online cohorts. ❖ Themes of social change, social cohesion, social activism and social inclusion.
  4. 4. Research vision ❖ Generate a data recognition practice (AI/machine learning) ❖ model social intelligence in 3D immersive environment. ❖ search for signatures of social disruption within digital trace data. ❖ Interactive and predictive analysis (historical and real time). ❖ Identify non-human actors within social datasets that influence social sentiment.
  5. 5. Research agenda ❖ Investigating available data, techniques and community processes suitable for modelling and visualisation. ❖ Building collaborations through an interdisciplinary network of researchers across the social sciences, humanities and computational sciences (but not limited to!). ❖ Developing an interactive and immersive platform for researchers and research stakeholders to model social processes.
  6. 6. Visualising social form
  7. 7. Modelling social form Maddox, circa 2014 (vintage graphics!)
  8. 8. Maddox, A 2016, Research Methods and Global Online Communities: A Case Study, Routledge, London, UK. Figure: Methods for collecting data on digital community
  9. 9. Figure: Conceptualizing the link between egocentric data imprints to digital community Modelling individual practices Maddox, A 2017, 'Beyond digital dualism: Modeling digital community', in T Cottom, J Daniels & K Gregory (eds), Digital Sociologies, Policy Press, Bristol, UK, pp. 9-26.
  10. 10.;590 Serendipity & Social Surfaces Maddox, circa 2014 (yes, Chromatophores!) Deravi, LF, Magyar, AP, Sheehy, SP, Bell, GR, Mäthger, LM, Senft, SL, Wardill, TJ, Lane, WS, Kuzirian, AM & Hanlon, RT 2014, 'The structure– function relationships of a natural nanoscale photonic device in cuttlefish chromatophores', Journal of The Royal Society Interface, vol. 11, no. 93, p. 20130942.
  11. 11. Socio-technical disruption
  12. 12. Socio-technical disruption ❖ Hypotheses directing investigation: ❖ Digital communities generate and appropriate emerging technologies to create alternative possibilities. ❖ Socio-technical disruption is manifested through digital communities. ❖ Socio-technical disruption may be identifiable through digital signatures. ❖ Forms of identifiable disruption may be ambivalent, malicious or resistance/refusal acts against structural inequalities. ❖ Within a community this can be characterised through both internal events and externalised activities.
  13. 13. Case study 1: Cryptocurrencies ❖ To study public discourse surrounding cryptocurrencies via social media analysis. ❖ Cryptocurrencies are: ❖ Digital payments systems ❖ based upon decentralised peer-to-peer exchange practices ❖ Use of encryption technologies for user privacy and anonymity. ❖ Contentious developments widely discussed within social media surrounding cryptocurrencies over the last five years (2012-2017).
  14. 14. Data gathered through TrISMA archive and processed through Tableau (unpublished) Figure: Cryptocurrency discussion in the Australian Twittersphere 2012 to early 2017
  15. 15. Case study 1: Cryptocurrencies ❖ Community profile ❖ Origins within the Cyber-libertarians of the 1990s (Cypherpunks email list formed in 1992). ❖ Value field includes: privacy, anonymity, personal sovereignty & autonomy, freedom (of information and by contractual relationships), disruption of state, decentralisation, peer-to-peer socio-technical architectures, code-as-law. ❖ Goldbugs, hippies, cyberlibertarians and so on … (Maurer 2013). ❖ Cypherpunks and Crypto-anarchists (Swartz 2018). ❖ Broader base includes fintech enthusiasts and speculators, start ups and entrepreneurs, privacy advocates. Maddox, A, Singh, S, Horst, H & Adamson, G 2016, 'An ethnography of Bitcoin: Towards a future research agenda', Australian Journal of Telecommunications and the Digital Economy, vol. 4, no. 1, pp. 65-78.
  16. 16. Case study 1: Cryptocurrencies ❖ External disruption ❖ Community agenda towards the disruption of centralised banking system ❖ Frictionless transfers across national borders (ie stateless transfer). ❖ Blockchain developments (cf. Allen 2018, Zhao 2016)
  17. 17. Case study 1: Cryptocurrencies ❖ Internal disruption ❖ There have been several contentious events within the community (ie forking in the blockchain, hacks and scams). ❖ These are disputes surrounding what the technologies can or should do linked to disputes between values.
  18. 18. Case study 1: Cryptocurrencies ❖ Trends & tensions over 2012-2017 ❖ Incorporation of these technologies within banks ❖ Scams and hacks through exchanges and ICOs ❖ Centralising forces & middle men ❖ Investment/speculation bubble ❖ Digital metallism vs infrastructural mutualism (Swartz 2018).
  19. 19. Case study 2: Cyberlibertarians ❖ To study cyber-libertarian discourse within the contentious environments of the dark web. ❖ To investigate the relationship between passive data monitoring (big data analysis) and data gathered through active engagement (small data analysis) ❖ Consider how this combination of data can inform and disrupt assumptions within data visualisation approaches.
  20. 20. Case study 2: Cyberlibertarians ❖ Dark web environments ❖ Overlapping values towards the sovereign self, personal privacy and anonymity. ❖ Nodal governance of online spaces ❖ Expression of radical values (information freedom, radical transparency, radical anti-statism). ❖ Cyber-libertarianism 2.0: anarcho-capitalism of the sharing economy & DIY prosumers freed from external constraint (Dahlberg 2010). ❖ Technology-enabled solutions to social problems ❖ Self-organising practices of resistance (cf. Fuchs, Coleman)
  21. 21. Case study 2: Cyberlibertarians ❖ Cryptomarkets ❖ Choice-driven drug purchasing online ❖ Constructive activism ❖ Contentious visibility ❖ ‘Selfcare’ vs ‘Healthcare’ Maddox, A, Barratt, MJ, Allen, M & Lenton, S 2015, 'Constructive activism in the dark web: cryptomarkets and illicit drugs in the digital ‘demimonde’', Information, Communication & Society, pp. 1-16. Barratt, MJ, Lenton, S, Maddox, A & Allen, M 2016, '‘What if you live on top of a bakery and you like cakes?’—Drug use and harm trajectories before, during and after the emergence of Silk Road', International Journal of Drug Policy, vol. 35, pp. 50-7. Barratt, MJ & Maddox, A 2016, 'Active engagement with stigmatised communities through digital ethnography', Qualitative Research, vol. 16, no. 6, pp. 701-19.
  22. 22. Case study 2: Cyberlibertarians ❖ External disruption ❖ Constructive activism: building a new world in the shell of the old. ❖ Online privacy and digital rights movement ❖ Internal disruption ❖ Hacks and Scams, DDOS attacks, Doxxing , site seizure by law enforcement, digital diasporas in digital spaces
  23. 23. Methodological challenges abound ❖ Defining and locating the community to study. ❖ Gaining consent and engagement and addressing the many ethical quandaries. ❖ Decisions on which methods and data collection tools. ❖ Identification of appropriate data sources ❖ Data capture: data on the dark web tends towards the ephemeral. ❖ How to render social intelligence into 3D formats (multi-factor visualisation). ❖ How to incorporate machine learning and AI approaches to process social intelligence (archival and real time information).
  24. 24. Next steps ❖ Keep plugging away at case study 1 ❖ Identify appropriate subject of study for case study 2 ❖ Identify and map available data sources ❖ Investigate 3D visualisation tools and Machine learning/AI tools. ❖ Determine funding sources to support the development of this project ❖ Identify and engage collaborators and build a research network/ engage and interdisciplinary field ❖ Publish, publish, publish ❖ Stay employed long enough to develop critical mass around this project!