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Using machine learning to map the field of collective intelligence research

As part of our new research programme we have used machine learning and literature search to map key trends in collective intelligence research. This helps us build on the existing body of knowledge on collective intelligence, as well as identify some of the gaps in research that can be addressed to advance the field.

At Nesta’s Centre for Collective Intelligence Design we are interested in the potential for collective intelligence (CI) to be amplified by new methods and emerging technologies. And in the spirit of collective intelligence, we want to ensure that our work complements and builds on the existing academic canon, addresses current gaps and challenges and takes full advantage of new research methods.

We think it’s important to open up the conversation beyond some of the narrower academic definitions of collective intelligence to include all forms of group intelligences (human and machine) acting at scale to achieve an objective. This is why we also consider crowdsourcing, citizen science and deliberative democracy in our working definition of collective intelligence.

By codifying this body of knowledge to build an evidence base for what works, we aim to bring rigour to the field and contribute resources for designing collective intelligence that can be used by ourselves and others to address some of the most complex issues in health, international development, environment and many more!

Using topic modelling to understand the field

Cluster enhance.png

Prominent topic clusters within collective intelligence research

Much of the existing knowledge on collective intelligence is discoverable through online research and innovation databases which is where we’ve starting our mapping endeavour. In collaboration with the Innovation Mapping Team at Nesta, we’re using a machine learning enabled approach to understand how the collective intelligence research community describe their work and the kind of subjects that feature most often in the literature and project descriptions. By applying topic modelling to our dataset of 7000 items (open access articles, funding abstracts, company descriptions) we’ve identified prominent thematic clusters and their relatedness to each other.

The cluster diagram above shows the dominance of each topic within the corpus relative to the others (size of text) and how closely related the topics are by meaning (position and distance between text headings). We can see that the largest topic heading is crowdsourcing, indicating that this is most commonly cited type of collective intelligence. Other well represented topics include citizen science, sensor data, data quality and multi-agent systems.

Four key trends in the academic literature

Disciplines represented in CI

Disciplinary division of research on "collective intelligence"

By delving deeper into the research using traditional academic literature search we have identified four key trends that are currently shaping the field:

  1. Research on collective intelligence is divided between many different academic disciplines. It is dominated by publications in Computer Sciences but academics from Social Sciences, Engineering, Mathematics, Business & Management, Medicine & Health also contribute a large number of articles on the topic. This multidisciplinarity can be both a strength and a weakness, as the benefits brought by diversity of expertise are sometimes diluted by the lack of dedicated forums for convening the different fields and publishing their work.

  2. Over the last 10 years, articles featuring the terms “crowdsourcing” and “citizen science” have seen a massive boom compared to “collective intelligence”. In 2008, publications featuring the keyword “collective intelligence” outnumbered citizen science and crowdsourcing by an order of 5 but while the former term has plateaued at ~200 publications annually in the years since, crowdsourcing and citizen science publications have increased at pace, with ~1400 and ~550 articles published in 2018 respectively.

  3. Of the search terms we chose, citizen science research is most closely aligned with the social issues focus at the heart of CCID. Keywords closely associated with citizen science include urbanization, climate change and biodiversity, while keywords linked to collective intelligence include web 2.0, swarm and artificial intelligence, knowledge management and open innovation. These distinct priorities of focus and complementary strengths suggest that different academic communities working on collective intelligence have a lot to learn from one another.

  4. Mapping the co-citation (how often authors cite each other) and relatedness of the bibliographies between the crowdsourcing, citizen science and collective intelligence articles revealed distinct and mostly non-overlapping clusters. This suggests to us that academics working in these fields are not currently reading each other’s work and learning from one another.

  5. Collective intelligence research has a global reach. As with many academic fields, most articles originate in the US and UK, but academics from China, Japan, South Korea and Australia also author a substantial proportion of publications using the term “collective intelligence”. We see similar geographical trends for funding of research ( for the ~15% of our dataset that specified funding). Our data reveal that the National Natural Science Foundation of China (NSFC) is an emerging funder for collective intelligence.

These early insights suggest that academics and practitioners working within and adjacent to collective intelligence have a lot to learn from each other across disciplinary boundaries. Whether you call it citizen science, crowdsourcing, deliberative democracy or collaborative learning… these are all great examples of collective intelligence in action.

Shaping the research agenda for collective intelligence design

This work complements our ongoing efforts to build a repository of practical projects and tools demonstrating collective intelligence. Together, these mapping studies are helping to shape the research agenda for the Centre. Some of the questions we hope to address in the next 12 months include:

  1. How can we best design CI systems that optimise for a particular objective?
  2. What are the skills necessary to design, orchestrate and participate in CI projects/approaches?
  3. What are the best models for governance of networks, organisations, assets and knowledge commons?
  4. In what ways are machine learning and AI being used to enhance collective intelligence?

In the coming months, we’ll be refining our research questions and supporting our Small Grants winners to answer others about designing collective intelligence. And of course we’d welcome your feedback, get in touch at [email protected]

Author

Aleks Berditchevskaia

Aleks Berditchevskaia

Aleks Berditchevskaia

Senior Researcher, Centre for Collective Intelligence Design

Aleks is a Senior Researcher and Project Manager for the Centre for Collective Intelligence Design.

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Konstantinos Stathoulopoulos

Konstantinos Stathoulopoulos

Konstantinos Stathoulopoulos

Data Science Researcher, Innovation Mapping

Konstantinos is working as a Data Science Researcher on Nesta's Research Analysis and Policy team.

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