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

Crowdsourcing for Reminiscence Chatbot Design

In this work-in-progress paper we discuss the challenges in identifying effective and scalable crowd-based strategies for designing content, conversation logic, and meaningful metrics for a reminiscence chatbot targeted at older adults. We formalize the problem and outline the main research questions that drive the research agenda in chatbot design for reminiscence and for relational agents for older adults in general

Privacy in crowdsourcing:a systematic review

The advent of crowdsourcing has brought with it multiple privacy challenges. For example, essential monitoring activities, while necessary and unavoidable, also potentially compromise contributor privacy. We conducted an extensive literature review of the research related to the privacy aspects of crowdsourcing. Our investigation revealed interesting gender differences and also differences in terms of individual perceptions. We conclude by suggesting a number of future research directions.</p

CommuniSense: Crowdsourcing Road Hazards in Nairobi

Nairobi is one of the fastest growing metropolitan cities and a major business and technology powerhouse in Africa. However, Nairobi currently lacks monitoring technologies to obtain reliable data on traffic and road infrastructure conditions. In this paper, we investigate the use of mobile crowdsourcing as means to gather and document Nairobi's road quality information. We first present the key findings of a city-wide road quality survey about the perception of existing road quality conditions in Nairobi. Based on the survey's findings, we then developed a mobile crowdsourcing application, called CommuniSense, to collect road quality data. The application serves as a tool for users to locate, describe, and photograph road hazards. We tested our application through a two-week field study amongst 30 participants to document various forms of road hazards from different areas in Nairobi. To verify the authenticity of user-contributed reports from our field study, we proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to verify whether submitted reports indeed depict road hazards. We found 92% of user-submitted reports to match the MTurkers judgements. While our prototype was designed and tested on a specific city, our methodology is applicable to other developing cities.Comment: In Proceedings of 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2015