Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labelling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labelling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work introduces the technical aspects of the platform and explores its future use cases

CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations

This paper introduces a novel activity dataset which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions. The dataset presents a set of videos of actors performing everyday activities in a natural and unscripted manner. The dataset was recorded using a static Kinect 2 sensor which is commonly used on many robotic platforms. The dataset comprises of RGB-D images, point cloud data, automatically generated skeleton tracks in addition to crowdsourced annotations. Furthermore, we also describe the methodology used to acquire annotations through crowdsourcing. Finally some activity recognition benchmarks are presented using current state-of-the-art techniques. We believe that this dataset is particularly suitable as a testbed for activity recognition research but it can also be applicable for other common tasks in robotics/computer vision research such as object detection and human skeleton tracking

A Framework for Techniques for Information Technology Enabled Innovation

Australia is seen as lagging in the innovation that is needed for corporate success and national productivity gains. There is an apparent lack of consistent and integrated advice to managers on how to undertake innovation. Thus, this study aims to develop and investigate a framework that relates innovation practices to the type of innovation outcome, in the context of Information Technology (IT) enabled innovations. An Innovation Practice Framework was developed based on the Knowledge-Innovation Matrix (KIM) proposed by Gregor and Hevner (2015). Eleven commonly used innovation techniques (practices) were identified and placed in one or more of the quadrants: invention, advancement, exaptation and exploitation. Interviews were conducted with key informants in nine organisations in the Australian Capital Territory. Results showed that the least used techniques were skunk works and crowdsourcing. The most used techniques were traditional market research, brainstorming and design thinking. The Innovation Practice Framework was given some support, with genius grants being related to invention outcomes, design thinking with exaptation, traditional R&D with advancement and managerial scanning with exploitation. The study contributes theoretically with the new Innovation Practice Framework and has the potential to be useful to managers in showing how benefits can be gained from a range of innovation practices. Further work is in progress

A Framework for Techniques for Information Technology Enabled Innovation

Australia is seen as lagging in the innovation that is needed for corporate success and ational productivity gains. There is an apparent lack of consistent and integrated advice to managers on how to undertake innovation. Thus, this study aims to develop and investigate a framework that relates innovation practices to the type of innovation outcome , in the context of Information Technology (IT) enabled innovations. An Innovation Practice Framework was developed based on the Knowledge - Innovation Matrix (KIM) proposed by Gregor and Hevner 2015). Eleven commonly used innovation techniques (practices) were identified and laced in one or more of the quadrants: invention, advancement, exaptation and exploitation. Interviews were conducted with key informants in nine organisations in the Australian Capital Territory. Results showed that the least used techniques were skunk works and crowdsourcing. The most used techniques were traditional market research, brainstorming and design thinking. The Innovation Practice framework was given some support, with genius grants being related to invention outcomes, design thinking with exaptation, traditional R&D with advancement and managerial scanning with exploitation. The study contributes theoretically with the new Innovation Practice Framework and has the potential to be useful to managers in showing how benefits can be gained from a range of innovation practices. Further work is in progres

Identifying new product ideas : waiting for the wisdom of the crowd or screening ideas in real time

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Beautiful and damned. Combined effect of content quality and social ties on user engagement

User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on Knowledge and Data Engineering (Volume: PP, Issue: 99

Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort