Engineering social media driven intelligent systems through crowdsourcing: Insights from a financial news summarisation system

Purpose The purpose of this paper is to explore implicit crowdsourcing, leveraging social media in real-time scenarios for intelligent systems. Design/methodology/approach A case study using an illustrative example system, which systematically employed a custom social media platform for automated financial news analysis and summarisation was developed, evaluated and discussed. Literature review related to crowdsourcing and collective intelligence in intelligent systems was also conducted to provide context and to further explore the case study. Findings It was shown how, and that useful intelligent systems can be constructed from appropriately engineered custom social media platforms which are integrated with intelligent automated processes. A recent inter-rater agreement measure for evaluating quality of implicit crowd contributions was also explored and found to be of value. Practical implications This paper argues that when social media platforms are closely integrated with other automated processes into a single system, this may provide a highly worthwhile online and real-time approach to intelligent systems through implicit crowdsourcing. Key practical issues, such as achieving high quality crowd contributions, challenges of efficient workflows and real-time crowd integration into intelligent systems were discussed. Important ethical and related considerations were also covered. Originality/value A contribution to existing theory was made by proposing how social media web platforms may benefit crowdsourcing. As opposed to traditional crowdsourcing platforms, the presented approach and example system has a set of social elements that encourages implicit crowdsourcing. Instances of crowdsourcing with existing social media, such as Twitter, often also called crowd piggybacking have been used in the past; however, employing an entirely custom-built social media system for implicit crowdsourcing is relatively novel and has several advantages. Some of the discussion in context of intelligent systems construction are novel and contribute to the existing body of literature in this field.