Related papers: Health Care Crowds: Collective Intelligence in Pub…
New techniques leveraging IT-mediated crowds such as Crowdsensing, Situated Crowdsourcing, Spatial Crowdsourcing, and Wearables Crowdsourcing have now materially emerged. These techniques, here termed next generation Crowdsourcing, serve to…
The increasing practice of engaging crowds, where organizations use IT to connect with dispersed individuals for explicit resource creation purposes, has precipitated the need to measure the precise processes and benefits of these…
Traditionally, the term crowd was used almost exclusively in the context of people who self-organized around a common purpose, emotion or experience. Today, however, firms often refer to crowds in discussions of how collections of…
Motivation: Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base construction and protein structure determination all benefit from human input. In…
With the increasing pervasiveness of algorithms across industry and government, a growing body of work has grappled with how to understand their societal impact and ethical implications. Various methods have been used at different stages of…
Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity, and uncertainty, which lead the knowledge learning from them full of challenges. With the emergence of crowdsourcing, versatile information can…
Research in population and public health focuses on the mechanisms between different cultural, social, and environmental factors and their effect on the health, of not just individuals, but communities as a whole. We present here a very…
Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed,…
Crowdsourcing is beginning to be used for policymaking. The wisdom of crowds [Surowiecki 2005], and crowdsourcing [Brabham 2008], are seen as new avenues that can shape all kinds of policy, from transportation policy [Nash 2009] to urban…
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…
To begin to understand the implications of the implementation of IT-mediated Crowds for Politics and Policy purposes, this research builds the first-known dataset of IT-mediated Crowd applications currently in use in the governance context.…
What is the state of the research on crowdsourcing for policy making? This article begins to answer this question by collecting, categorizing, and situating an extensive body of the extant research investigating policy crowdsourcing, within…
A clinical study is often necessary for exploring important research questions; however, this approach is sometimes time and money consuming. Another extreme approach, which is to collect and aggregate opinions from crowds, provides a…
In this work, we present a high-level computational model of IT-mediated crowds for collective intelligence. We introduce the Crowd Capital perspective as an organizational-level model of collective intelligence generation from IT-mediated…
Crowd behaviour analysis is essential to numerous real-world applications, such as public safety and urban planning, and therefore has been studied for decades. In the last decade or so, the development of deep learning has significantly…
Crowdsourcing is a process of accumulating the ideas, thoughts or information from many independent participants, with aim to find the best solution for a given challenge. Modern information technologies allow for massive number of subjects…
Many data mining tasks cannot be completely addressed by auto- mated processes, such as sentiment analysis and image classification. Crowdsourcing is an effective way to harness the human cognitive ability to process these machine-hard…
The advent of the digital pathology has introduced new avenues of diagnostic medicine. Among them, crowdsourcing has attracted researchers' attention in the recent years, allowing them to engage thousands of untrained individuals in…
Crowdsourcing is the primary means to generate training data at scale, and when combined with sophisticated machine learning algorithms, crowdsourcing is an enabler for a variety of emergent automated applications impacting all spheres of…
Crowdsourcing is rapidly evolving and applied in situations where ideas, labour, opinion or expertise of large groups of people are used. Crowdsourcing is now used in various policy-making initiatives; however, this use has usually focused…