Collective Intelligence and Community Resilience on Social Networks – Part One

Friday, 29 May, 2020 - 11:28

The popularity and ubiquity of social networks has enabled a new form of decentralised online collaboration: groups of users gathering around a central theme and working together to solve problems, complete tasks and develop social connections. Groups that display such ‘organic collaboration’ have been shown to solve tasks quicker and more accurately than other methods of crowdsourcing. They can also enable community action and resilience in response to different events, from casual requests to emergency response and crisis management. However, engaging such groups through formal agencies risks disconnect and disengagement by destabilising motivational structures.
 
Well-developed communities are capable of organising and solving their own needs; however, such activities are often undirected and lack resources. On a day-to-day basis individuals connect with each other in the community and gain personal satisfaction from involvement. It is in response to critical events such as flooding, fire, crime etc. that we can see resilient communities reacting and supporting each other quickly and effectively without coordination by a centralised government agent.
 
Crowdsourcing and citizen science projects have shown the willingness of the public to participate in projects they feel are worthwhile; however, interfacing digital communities with physical communities invokes a complex array of motivations, not only why people do things, but why they do not. Engaging such groups through formal agencies may risk destabilising motivational structures so careful consideration of the design of systems should be made.
 
In the Adaptive Resilience Model, widespread community engagement is a tool for building resilience. All relevant stakeholders and community members should be engaged in the process of recovery. At that point, effective communication and engagement can create and reinforce community resilience. Enabling such collaboration and engagement requires large-scale communication networks, such as those afforded by social media platforms, to coordinate and organise people and tasks.
 
Social networks building resilience
 
We know that people are self-organising similar tasks using social media platforms with great success and high engagement. This is driven by the familiarity and reach of the systems used, as well as the ability of each user to manage their subscriptions, news feed and privacy, and provides them with fine-grained control over what they present of themselves and what information they receive. It has also been observed that users on micro-task systems such as Mechanical Turk use social network groups to communicate task information and help complete tasks.
 
Another way communities might choose to collaborate is via existing social network groups to distribute tasks to members with the relevant expertise or experience. Similar to the concept of crowdsourcing (where the work traditionally done by a single person is replaced by the collective action of a group of people via the Internet), groupsourcing is defined as completing a task using a group of intrinsically-motivated people of varying expertise connected through a social network.
 
A group in this context is a feature of a social network platform that allows a small subset of users to communicate through a shared message system. Groups (for example, on Facebook) are initially set up in response to the needs of a few people and the community evolves as news from the group is proliferated around the network in feeds and user activity.
 
Social network groupsourcing is distinguished by several features:

  • tasks are created by the users;
  • communication is unconstrained and multi-threaded;
  • users are inherently motivated, socially trained and work collaboratively;
  • the output is immediately accessible and (potentially) beneficial to all, with users receiving recognition for their efforts.

One of the advantages of this approach is that the participants learn from each other, not only how to contribute to the system, but also the knowledge to participate. This interaction is led by more experienced and knowledgeable members of the community in an open and transparent way, meaning that when a user receives an answer from an expert, many more may be passively learning from it.
 
Social learning, in which users on a platform teach and support each other in an ad-hoc manner, encourages users to engage in the learning process to an extent that suits their interests and time constraints.
 
The organic collaboration within the online community already exists and manifests in numerous forms, from coordinating hyper-local action such as searching for a missing dog, through to wider responses to take action on local issues. Unlike other methods of crowdsourcing and citizen science, the people volunteering their time are doing so in undirected, but inherently motivated way.
 
SOURCES:
 
JON CHAMBERLAIN, UNIVERSITY OF ESSEX
BENJAMIN TURPIN, UNIVERSITY OF ESSEX
MAGED ALI, UNIVERSITY OF ESSEX
KAKIA CHATSIOU, UNIVERSITY OF ESSEX
KIRSTY O’CALLAGHAN, ESSEX COUNTY COUNCIL
 
PUBLISHED BY: J. Chamberlain et al. / Human Computation (in-press 2020)

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