Changing the pattern of unrest: Social media and social networks in the UK riots


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Sarabi, Yasaman, Tubaro, Paola and Antonio A. Casilli "Changing the pattern of unrest: The role of social media and social networks in the UK riots", présentation at the 9th UKSNA (UK Social Networks Analysis) Conference, London 28 June 2013. For more on the ICCU project:

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Changing the pattern of unrest: Social media and social networks in the UK riots

  1. 1. Changing the pattern of unrest: The role of social media and social networks in the UK riots Antonio A. Casilli, Yasaman Sarabi and Paola Tubaro 9th UK Social Networks Conference London 28 June 2013
  2. 2. Outline ● Introduction ● Method ● Epstein's civil violence model ● Results ● Future developments
  3. 3. Introduction The political role of online social networks ● Did use of online social networks (Twitter, Facebook, BBM) fuel the riots? ● Ambiguity in public discourse: networks as instrument of democracy (Arab spring) or criminality (London)? ● Cameron: shall we shut the Web to stop the violence? ⇒ Would censorship work? (assuming it is technically, legally and economically possible).
  4. 4. Method Agent-based computer simulation ● Generate socially consistent scenarios on a computer; ● Compare their outcomes; ● Detect and assess variables coming into play within the social process under study; ● Identify sufficient conditions for a macro phenomenon to emerge from interaction of micro behaviours. ● An aid to perform a thought experiment
  5. 5. Method The logic of an agent-based model ● Generating an artificial population of agents in an environment; ● Endowing them with basic rules of behaviour; ● Letting them interact for a certain time and step aside; ● Observing outcomes at the system level at the end.
  6. 6. Epstein's civil violence model ● An environment (a city?); ● One type of agent (circle); ● Agent have different levels of “grievance” towards government (shades of green); ● Some decide to go active (red circles); ● Depends on level of grievance + presence of other actives vs. cops (blue triangles) around them; ● Cops pick up randomly an active agent in their surroundings and arrest it (black circles). Source: Wilensky 2004.
  7. 7. Epstein's civil violence model ● Model also takes into account: ○ Government legitimacy; ○ Individual perceived risk of arrest. ● Agents can move on the social grid. ● “Vision” variable: agent’s ability to scan his/her neighbourhood for signs of cops and/or actives. ⇒ The higher the vision, the wider the agent’s range.
  8. 8. Epstein's civil violence model Epstein’s main result Civil violence is not a linear process: ⇒ Punctuated Equilibrium: periods of stability followed by short violent outbursts (red curve), while political tension builds up (blue curve). Source: Epstein 2002, Fig. 8.
  9. 9. Epstein's civil violence model Revisiting Epstein’s model ● Epstein’s simulation: agents move randomly to an empty place within their neighbourhood (vision range); ● Our idea: allow agents to scan their neighbourhoods more effectively and move to areas where there are more actives; ● Idea that online social networks give a competitive advantage against the police (awareness of the field). ⇒ With agents endowed with greater moving power, what are the effects of censorship?
  10. 10. Epstein's civil violence model Revisiting Epstein’s model Censorship represented through different values of “vision” parameter: Low vision = high censorship; High vision = low censorship. ⇒ How do model outcomes vary with different levels of vision?
  11. 11. Results Tests ● Run the simulation for different values of parameter vision (1 to 10); ● Do so over a significant period of time (1000 time steps) for each parameter value; ● Observe results at the end.
  12. 12. Results Red patterns represent number of active protesters over time with different levels of censorship: from 0 vision (total censorship, upper left) to 10 vision (no censorship, lower right).
  13. 13. Results Time in peace Time without riots, in percentage, corresponding to different levels of vision
  14. 14. Results How to interpret these results ● The pattern of violence changes with censorship; ● Absence of censorship does not totally eliminate violence; ● But it allows for periods of social peace to appear, which is never the case with censorship: ⇒ It may not pay to trade civil freedoms for security!
  15. 15. Future developments Work in progress ● Implement the model on a network rather than a grid topology. ● Taking out the cops ○ The estimated arrest probability of each agent depends on the number of active (A) and jailed (J) agents in its range of vision - instead of the number of A and cops (C).
  16. 16. Future developments Work in progress ● The notion of "distance" and how it is related to "vision". ? Computationally heavy? ● How the network structure changes over time. ● Testing the effects of different values on different network structures to see what happens.
  17. 17. Questions? Comments?
  18. 18. Thank you! Yasaman Sarabi