Embedded Human Computation for Knowledge Extraction and Evaluation
Summary
The rapid growth and fragmented character of social media and publicly available structured data challenges established approaches to knowledge extraction. It advances the field of Web Science by developing a scalable and generic HC framework for knowledge extraction and evaluation, delegating the most challenging tasks to large communities of users and continuously learning from their feedback to optimise automated methods as part of an iterative process. Accuracy and scalability of EHC to acquire factual and affective knowledge were assessed in an open evaluation campagin and two crowdsourcing applications based on the uComp human computation engine: While the generic uComp methods were evaluated across different domains, climate change was chosen as the main use case for its challenging nature, subject to fluctuating and often conflicting interpretations. The collaboration with international organisations such as the Climate Program Office of the National Oceanic and Atmospheric Administration (NOAA) and the United Nations Environment Programme have increased impact, provided a rich stream of input data, attracted a critical mass of users, and promoted EHC among a wide range of stakeholders. The achieved advances in affective knowledge extraction will improve the computation of a communication success metric and help assess the impact of online coverage on brand reputation and the perceptions of social issues.
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