Call for Papers
This workshop invites researchers and practitioners from different disciplines to explore the challenges and opportunities of applying social media to designing novel applications of collective intelligence, with a special focus on crowdsourcing and human computation. We are particularly interested in contributions that consider crowdsourcing and human computation in the broader context: as specific instantiations of collective intelligence and social computing on the web. How can the experience gained from the design of crowdsourcing applications inform the development of new approaches to collective intelligence? And vice versa: what lessons from the broader domain of collective intelligence can inform the design of new kinds of systems for crowdsourcing and human computation?
Both crowdsourcing and human computation consider humans as distributed task-solvers, leveraging human reasoning to solve complex tasks that are easy for individuals but difficult for purely computational approaches (human computation) or for traditional organizational work arrangements (crowdsourcing). Effective realizations of these paradigms typically require participation of a large number of distributed users over the Internet, a careful design of task structures, participation incentives and mechanisms for coordinating and aggregating results of individual participants into collective solutions.
Though rarely explicitly addressed as such, social media and related technologies often provide the enabling methods and technologies for the realization of such models. Examples include crowdsourcing marketplaces (e.g. Amazon Mechanical Turk), crowdsourcing service providers (e.g. Microtask, CrowdFlower) or games with a purpose. While centralized platforms are also at the core of “traditional” approaches to collective intelligence (e.g. Wikipedia), attention is increasingly turning to harnessing existing social platforms (e.g. Facebook, Twitter) that already gather huge numbers of users into webs of social relationships. .
Such Social Clouds pose both chances and challenges for new kinds of approaches to crowdsourcing and human computation in particular and to collective intelligence in general. On one hand, the intricate social relationships allow the development of new kinds of task routing mechanisms (e.g. identifying the best or most trusted participants for a specific task). Incentive structures are intrinsically social and tend to reflect community-like phenomena (e.g. the reputation economy), thus differing strongly from single-user approaches in classical crowdsourcing. This is already leading to early experiments such as expert-based crowdsourcing or solutions for task-injection across distributed social platforms. On the other hand, the design of such socially distributed computing structures relates the fields of crowdsourcing and human computation to the lessons from a broader class of collective intelligence platforms and applications.
The need to interrelate these fields is reflected in questions such as:
- How can we design effective incentive systems for large-scale participation of human users in structured collective intelligence systems?
- How do we design tasks at different levels of complexity that can be solved reliably through a composition of individual contributions?
- How can we use intricate webs of social relationships of existing social platforms for new models of coordination in distributed task-solving?
- How can distributed social media enable the design of new classes of crowdsourcing applications (e.g. using social network analysis for new ways of task-routing)?
- How can the comparison of lessons from distributed problem-solving in human computation and community-based approaches lead to novel classes of collective intelligence applications?
We are especially interested in applications and investigations in a range of domains such as collective action and social deliberation, multimedia search and exploration, enterprise and medical applications, cultural heritage, social data analysis or citizen science.
Topics include (but are not limited to):
- Social media in collective intelligence systems
- Use cases and applications of social media to crowdsourcing and human computation
- Social incentive models for crowdsourcing and human computation
- Social-network analysis for crowdsourcing and human computation
- Applications of social media visualization to collective intelligence applications
- Social coordination in crowdsourcing and human computation
- Social search and human computation
- Trust models for collective intelligence and crowdsourcing
- Semantic modeling in crowdsourcing and human computation
- Expert-based crowdsourcing
- Influence metering and social trust models
- Expertise-inference techniques and their application to task routing
- Reputation systems for human computation
- Quality assurance in distributed human intelligence tasks
- Social sensing in crowdsourcing and human computation
- Domain-specific challenges in crowdsourcing and human computation
The workshop will accept:
- Regular research papers (6-8 pages)
- Applications / Demonstrators (4 pages)
- Position papers (2-4 pages)
Paper Selection Process
All submissions will be reviewed in a peer-review process by at least two members of the program committee. At least one author of each paper will need to register for and attend the workshop to present the paper.
Results of the workshop (papers, findings from the discussion panel) will be published as separate workshop proceedings (either as Springer Lecture Notes in Computer Science or as CEUR-WS Proceedings). Depending on the quality of the submissions there will also be an opportunity to publish extended versions of the papers as a special issue in a major journal.