Crowdsourcing Effort Research Articles

Exploring incentive mechanisms for mobile crowdsourcing: sense of safety in a Thai city

ABSTRACTThe rapid adoption of mobile devices enables capture and transmission of a variety of sensor and user-contributed data, creating a new data collection paradigm and a wide range of services, often termed as mobile crowdsourcing. However, we are still investigating factors contributing towards the effectiveness of such systems. One key factor to succeed in mobile crowdsourcing applications is the incentive mechanism, which motivates people to contribute to a crowdsourcing effort. In this research, we conducted field experiments that compared the effectiveness of non-monetary and monetary incentive mechanisms, using both quantitative and qualitative methods. The focus was on an exploration of how these mechanisms motivate users’ performance in a mobile crowdsourcing environment. In the experiment, we developed a smart zoning application that allows users to share areas of the cities they perceived as safe or unsafe. The results from this study contribute to research in mobile crowdsourcing for urban understanding. Taken together, these results suggest that payment-related incentives can not only engage people’s interest in project participation but also help improve their work performance in terms of productivity and quality. Notably, a fixed-price financial reward mechanism is best suited for short period data collection and achieving data quality. Also, referral incentive mechanisms, if properly designed, have the potential to extend user coverage, both spatially and temporally. These results can be helpful with regard to the formulation of guidelines on how to create and organize effective payment-based incentives for crowd involvement in cities.

Engaging customer care employees in internal collaborative crowdsourcing: Managing the inherent tensions and associated challenges

Customer care employees (CCEs) are an excellent source of ideas for new and enhanced services for customers. By serving many customers, CCEs have the ability to see patterns in unserved and underserved needs. By being inside rather than external to the firm, CCEs have the ability to offer suggestions that build on existing capabilities, which result in ideas that are more easily implementable. There is a long history of research and practice for soliciting suggestions from employees, but little of this work has described how CCEs can be organized into a temporary online crowd to cocreate innovative ideas. With the advent of crowdsourcing software platforms, online collaboration among geographically distributed CCEs is increasingly possible. We describe an action research study of Optum, a healthcare company that implemented a collaborative internal crowdsourcing initiative over a period of 18 months. The action research study led to the identification of three tensions and associated challenges. We outline eight HRM actions that were undertaken to mitigate the challenges of engaging CCEs in internal crowdsourcing efforts. Based on our findings, we lay out a general framework, implications, and future research directions for research and managerial actions when engaging CCEs in internal collaborative crowdsourcing.

Improving Health Promotion Through the Integration of Technology, Crowdsourcing, and Social Media.

As Internet accessibility and technological innovations continue to increase communication, new opportunities have emerged to leverage these tools to improve health promotion practice. Advances and utilization of collaborative Internet communication, or social media, have provided global connectivity on an unprecedented scale. Using these innovations to leverage the collective intellect of online communities for specific goals, crowdsourcing is an approach that has the potential to solve complex public health problems. Due to the novelty of crowdsourcing implementations and the relative infancy of its application within public health, it is necessary to examine examples to facilitate practitioner conceptualization and application. This article details the development and application of a crowdsourced effort leveraging social media and technology to assist in relief efforts during Hurricane Harvey. Furthermore, the article presents examples corresponding to a typology of crowdsourcing for public health, including Knowledge Discovery and Management, Distributed Human Intelligence Tasking, Broadcast Search, and Peer-Vetted Creative Production problems. Leveraging these innovative applications has positive implications for health promotion practice, including improved intervention development and evaluation, increased multidisciplinary collaboration, and enhanced facilitation of communication, information exchange, and support.

Development of an interstitial cystitis risk score for bladder permeability.

BackgroundInterstitial cystitis/bladder pain syndrome (IC) is a multifactorial syndrome of severe pelvic and genitalia pain and compromised urinary function; a subset of IC patients present with Hunner’s lesions or ulcers on their bladder walls (UIC). UIC is diagnosed by cystoscopy, which may be quite painful. The objective of this study was to determine if a calculated Bladder Permeability Defect Risk Score (BP-RS) based on non-invasive urinary cytokines could discriminate UIC patients from controls and IC patients without Hunner’s ulcers.MethodsA national crowdsourcing effort targeted IC patients and age-matched controls to provide urine samples. Urinary cytokine levels for GRO, IL-6, and IL-8 were determined using a Luminex assay.ResultsWe collected 448 urine samples from 46 states consisting of 153 IC patients (147 female, 6 male), of which 54 UIC patients (50 females, 4 male), 159 female controls, and 136 male controls. A defined BP-RS was calculated to classify UIC, or a bladder permeability defect etiology, with 89% validity.ConclusionsThe BP-RS Score quantifies UIC risk, indicative of a bladder permeability defect etiology in a subset of IC patients. The Bladder Permeability Defect Risk Score is the first validated urine biomarker assay for interstitial cystitis/bladder pain syndrome.

Future directions in international financial integration research - A crowdsourced perspective

This paper is the result of a crowdsourced effort to surface perspectives on the present and future direction of international finance. The authors are researchers in financial economics who attended the INFINITI 2017 conference in the University of Valencia in June 2017 and who participated in the crowdsourcing via the Overleaf platform. This paper highlights the actual state of scientific knowledge in a multitude of fields in finance and proposes different directions for future research.

MP29-07 DEVELOPMENT OF THE ULCERATIVE INTERSTITIAL CYSTITIS RISK SCORE (ICUS): A URINE-BASED MULTIPLE PROTEIN ASSAY TO PREDICT ULCERATIVE INTERSTITIAL CYSTITIS

You have accessJournal of UrologyInfections/Inflammation/Cystic Disease of the Genitourinary Tract: Interstitial Cystitis II1 Apr 2017MP29-07 DEVELOPMENT OF THE ULCERATIVE INTERSTITIAL CYSTITIS RISK SCORE (ICUS): A URINE-BASED MULTIPLE PROTEIN ASSAY TO PREDICT ULCERATIVE INTERSTITIAL CYSTITIS Laura Lamb, Joseph Janicki, Sarah Bartolone, Interstitial Cystitis Association, Bernadette Zwaans, Kenneth Peters, and Michael Chancellor Laura LambLaura Lamb More articles by this author , Joseph JanickiJoseph Janicki More articles by this author , Sarah BartoloneSarah Bartolone More articles by this author , Interstitial Cystitis AssociationInterstitial Cystitis Association More articles by this author , Bernadette ZwaansBernadette Zwaans More articles by this author , Kenneth PetersKenneth Peters More articles by this author , and Michael ChancellorMichael Chancellor More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.919AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Interstitial cystitis/bladder pain syndrome (IC/BPS) is a multifactorial syndrome of severe pelvic and genitalia pain and compromised urinary function. A fraction of IC patients who are the most severe present with Hunner's ulcers or patches on their bladder walls, termed ulcerative IC (UIC). UIC is diagnosed by cystoscopy, however this is a painful and expensive procedure. The objective of this study was to determine if a calculated Interstitial Cystitis Ulcerative Risk Score (ICUS) based on non-invasive urinary cytokines could discriminate UIC patients from controls and non-ulcerative IC patients. METHODS A national crowdsourcing effort targeting IC/BPS patients resulted in 442 urine samples consisting of 153 IC patients (146 female, 7 male), 155 female controls, and 134 male controls were collected. Controls were age-matched. This consisted of 52 UIC patients (48 females, 4 male). Urinary cytokine levels were determined using Luminex assay. A predictive classification model was generated from this data using the scikit-learn machine learning library and the Python programming language. It provides a probability of ulcerative IC when the algorithm is supplied with the levels of three different proteins found in the urine. RESULTS A defined ICUS Score of 0 to 1 was calculated to predict UIC, or a bladder permeability defect etiology (Figure 1). If the ICUS is ≥ 0.5, then there is an 87% chance that the patient has ulcerative IC. If the ICUS is <0.5, there is an 87% chance that the patient does not have ulcerative IC. The three protein levels combined provide a much better prediction model versus any of the individual protein levels alone. CONCLUSIONS The ICUS Score quantifies UIC risk, indicative of a bladder permeability defect etiology, in a subset of IC patients. This provides a new clinical tool to improve diagnosing patients with suspected IC symptoms. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e382 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Laura Lamb More articles by this author Joseph Janicki More articles by this author Sarah Bartolone More articles by this author Interstitial Cystitis Association More articles by this author Bernadette Zwaans More articles by this author Kenneth Peters More articles by this author Michael Chancellor More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

A Personalized Multi-Path and Multi-User Traffic Analysis and Visualization Tool

Traffic optimization is a subject that has become vital for the world we live in. People these days need to get from a starting point to a destination point as fast and as safe as possible. Traffic congestion plays a key role in the frustration of people and it results in lost time, reduced productivity and wasted resources. In our study we seek to address these issues by proposing a real-time road traffic planning system based on mobile context and crowd sourcing efforts. The first step toward this goal is real-time traffic characterization using data collected from mobile sensors of drivers, pedestrians, cyclists, passengers, etc.. We started developing a data collection and analysis system composed of a mobile application in order to collect user context data and a Web application to view and analyze the data. This new system will eventually give the users an automatically optimized route to the destination and predict the users’ traveling route based on live traffic conditions and historical data.

Fragments of Medieval Manuscripts in Printed Books: Crowdsourcing and Cataloging Medieval Manuscript Waste in the Book Collection of the Harry Ransom Center

This catalog describes 144 medieval manuscript fragments used as binding material in the printed book collection of the Harry Ransom Center at the University of Texas, Austin. It is the result of “The Medieval Fragments Project,” a crowdsourcing effort using Flickr to identify medieval manuscripts used as binding waste. It illustrates the ways in which the fragments have been utilized as binder’s waste and the use of image hosting websites and social networking platforms to crowdsource the identification of manuscript fragments. It presents a method for describing fragments in situ to facilitate the study of manuscript waste within the historical context of bookbinding.

On the relationship between number of votes and sentiment in crowdsourcing ideas and comments for innovation: A case study of Canada's digital compass

Recently, PWC facilitated an innovation crowdsourcing effort entitled “Canada's Digital Compass” aimed at generating creative directions for Canada to pursue in its future. As part of this effort, over 70 ideas were submitted and over five hundred votes on those ideas were generated. This paper analyzes data developed as part of that effort, investigating a number of relationships between different variables elicited from the analysis and the number of votes for those ideas. The analysis confirms some previous results, provides a structure for interpreting those results and generates some new results. In particular, this paper generates sentiment measures for both the innovation ideas and for the comments. Using a decision tree approach we find that the number of comments and the extent to which the sentiment in those comments is positive are statistically significantly related to the number of votes. In addition, using regression analysis, this paper finds that the number of votes is statistically significantly related to the interaction between sentiment measures for ideas and comments. Finally, a rationale for these results, based on game theory is proposed and investigated.

Crowdsourcing metrics of digital collections

In the National Library of Finland (NLF) there are millions of digitized newspaper and journal pages, which are openly available via the public website http://digi.kansalliskirjasto.fi. To serve users better, last year the front end was completely overhauled with its main aim in crowdsourcing features, e.g., by giving end-users the opportunity to create digital clippings and a personal scrapbook from the digital collections. But how can you know whether crowdsourcing has had an impact? How much crowdsourcing functionalities have been used so far? Did crowdsourcing work? In this paper the statistics and metrics of a recent crowdsourcing effort are analysed across the different digitized material types (newspapers, journals, ephemera). The subjects, categories and keywords given by the users are analysed to see which topics are the most appealing. Some notable public uses of the crowdsourced article clippings are highlighted. These metrics give us indications on how the end-users, based on their own interests, are investigating and using the digital collections. Therefore, the suggested metrics illustrate the versatility of the information needs of the users, varying from citizen science to research purposes. By analysing the user patterns, we can respond to the new needs of the users by making minor changes to accommodate the most active participants, while still making the service more approachable for those who are trying out the functionalities for the first time. Participation in the clippings and annotations can enrich the materials in unexpected ways and can possibly pave the way for opportunities of using crowdsourcing more also in research contexts. This creates more opportunities for the goals of open science since source data becomes ­available, making it possible for researchers to reach out to the general public for help. In the long term, utilizing, for example, text mining methods can allow these different end-user segments to achieve more. Based on our current initial experiences, we feel that crowdsourcing gives an opportunity for a library context to get closer to the user base and to obtain insight into the numerous opportunities, which the digitized content provides for them and for the library. Gathering the first prototype qualitative and quantitative metrics for this particular crowdsourcing case gives information on how to further improve both the service and the metrics so that they can give valid information for decision-making.

Experiments in Crowd-funding Community Archaeology

This article reviews existing case studies in the ‘crowd-funding’ of community archaeology, as well as offering preliminary results from a small-scale experiment conducted alongside the wider crowd-sourcing efforts of the MicroPasts project (http://micropasts.org). In so-doing, it also considers the possible role of a hybrid reward- and donation-based model for micro-financing collaborative archaeological research. The article concludes with a summary of the key lessons drawn from experiences of crowd-funding archaeology so far, and highlights their particular implications for community archaeology.

Street-Scale Modeling of Storm Surge Inundation along the New Jersey Hudson River Waterfront

AbstractA new, high-resolution, hydrodynamic model that encompasses the urban coastal waters of New Jersey along the Hudson River Waterfront opposite New York City, New York, has been developed and validated for simulating inundation during Hurricane Sandy. A 3.1-m-resolution square model grid combined with a high-resolution lidar elevation dataset permits a street-by-street focus to inundation modeling. The waterfront inundation model is a triple-nested Stevens Institute Estuarine and Coastal Ocean Hydrodynamic Model (sECOM) application; sECOM is a successor model to the Princeton Ocean Model family of models. Robust flooding and drying of land in the model physics provides for the dynamic prediction of flood elevations and velocities across land features during inundation events. The inundation model was forced by water levels from the extensively validated New York Harbor Observing and Prediction System (NYHOPS) hindcast of that hurricane.Validation against 56 watermarks and 16 edgemarks provided via the USGS and through an extensive crowdsourcing effort consisting of photographs, videos, and personal stories shows that the model is capable of computing overland water elevations quite accurately throughout the entire surge event. The correlation coefficient (R2) between the watermark observations and the model results is 0.92. The standard deviation of the residual error is 0.07 m. Comparisons to the 16 flood edgemarks suggest that the model was able to reproduce flood extent to within 20 m. Because the model was able to capture the spatial and temporal variation of water levels in the region observed during Hurricane Sandy, it was used to identify the flood pathways and suggest where flood-preventing interventions could be built.

Allele Frequency Crowdsourcing Effort Enjoys Brisk Start

Allele Frequency Crowdsourcing Effort Enjoys Brisk Start

DBpedia – A large-scale, multilingual knowledge base extracted from Wikipedia

The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a world-wide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.

Enhancing Data Quality using Human Computation and Crowd Sourcing

This paper is aimed at addressing the issues that are present in the data dumps available at DBpedia by using the concept of associations i.e. concept hierarchy to enhance the quality of those data dumps. These data dumps are extracted from Wikipedia and the issues that prevail in these data dumps is because of either the data extraction frameworks or the human error during crowd-sourcing efforts made on Wikipedia. By using Human Computation techniques and employing Crowd sourcing together with query morphing, diving deeper into this subject would become easier in a better way. One of the key issues with the datasets is the presence of multiple values in a single attribute and vice versa especially in the “Place of Birth” field of important personalities. This paper highlights the implementation process in order to solve these issues and adds a survey conducted on Crowd Sourcing to highlight its impact.

A Framework for Using Crowdsourcing in Government

Crowdsourcing is a concept in which the crowd is used as a source of labor, idea generation, or problem identification. Crowdsourcing originated in the private sector; though with any good private sector practice it is increasingly being utilized in government. This paper provides an overview of the concept of crowdsourcing, gives examples of its use in the private and public sectors, and develops a framework for how governments can begin to strategize and think about crowdsourcing to solve problems when engaging with citizens. The authors' framework is illustrated with a number of cases from current or past uses of crowdsourcing in government. They conclude with important considerations about how governments should strategize their crowdsourcing efforts.

Crowd-sourcing delivery system innovation: A public–private solution

We propose the establishment of a public–private approach which creates and maintains a “delivery systems innovations knowledge management system” to define, describe, and assess novel delivery approaches. The public sector could provide the foundational technology, resources and convening power for this innovations database. The private sector would contribute practical innovations that could guide annual strategic planning and implementation.A crowd-sourced effort would jump start delivery system reform. We believe that providing a comprehensive knowledge resource will not stifle competition or private sector opportunities but rather augment and speed the application of effective innovation.

Durham Zoo: Powering a Search-&amp;-Innovation Engine with Collective Intelligence

Purpose – Durham Zoo (hereinafter – DZ) is a project to design and operate a concept search engine for science and technology. In DZ, a concept includes a solution to a problem in a particular context. Design – Concept searching is rendered complex by the fuzzy nature of a concept, the many possible implementations of the same concept, and the many more ways that the many implementations can be expressed in natural language. An additional complexity is the diversity of languages and formats, in which the concepts can be disclosed. Humans understand language, inference, implication and abstraction and, hence, concepts much better than computers, that in turn are much better at storing and processing vast amounts of data. We are 7 billion on the planet and we have the Internet as the backbone for Collective Intelligence. So, our concept search engine uses humans to store concepts via a shorthand that can be stored, processed and searched by computers: so, humans IN and computers OUT. The shorthand is classification: metadata in a structure that can define the content of a disclosure. The classification is designed to be powerful in terms of defining and searching concepts, whilst suited to a crowdsourcing effort. It is simple and intuitive to use. Most importantly, it is adapted to restrict ambiguity, which is the poison of classification, without imposing a restrictive centralised management. In the classification scheme, each entity is shown together in a graphical representation with related entities. The entities are arranged on a sliding scale of similarity. This sliding scale is effectively fuzzy classification. Findings – The authors of the paper have been developing a first classification scheme for the technology of traffic cones, this in preparation for a trial of a working system. The process has enabled the authors to further explore the practicalities of concept classification. The CmapTools knowledge modelling kit to develop the graphical representations has been used. Practical implications – Concept searching is seen as having two categories: prior art searching, which is searching for what already exists, and solution searching: a search for a novel solution to an existing problem. Prior art searching is not as efficient a process, as all encompassing in scope, or as accurate in result, as it could and probably should be. The prior art includes library collections, journals, conference proceedings and everything else that has been written, drawn, spoken or made public in any way. Much technical information is only published in patents. There is a good reason to improve prior art searching: research, industry, and indeed humanity faces the spectre of patent thickets: an impenetrable legal space that effectively hinders innovation rather than promotes it. Improved prior-art searching would help with the gardening and result in fewer and higher-quality patents. Poor-quality patents can reward patenting activity per se, which is not what the system was designed for. Improved prior-art searching could also result in less duplication in research, and/or lead to improved collaboration. As regards solution search, the authors of the paper believe that much better use could be made of the existing literature to find solutions from non-obvious areas of science and technology. The so-called cross industry innovation could be joined by biomimetics, the inspiration of solutions from nature. Crowdsourcing the concept shorthand could produce a system ‘by the people, for the people’, to quote Abraham Lincoln out of context. A Citizen Science and Technology initiative that developed a working search engine could generate revenue for academia. Any monies accruing could be invested in research for the common good, such as the development of climate change mitigation technologies, or the discovery of new antibiotics. Originality – The authors know of no similar systems in development

Crowdsourcing to fight corruption in Macedonia: interview with Metodi Zajkov (Transparency International – Macedonia)

The General Secretary at Transparency International – Macedonia (TI – Macedonia) discusses challenges facing Macedonia resulting from corruption. The interview explores a new crowdsourcing effort, Transparency Watch, which empowers citizens to report corruption through digital means. Specific success stories for Transparency Watch and TI – Macedonia’s legal assistance center are also highlighted.

Community engagement

Crowdsourcing efforts, such as those involved in the Escherichia coli O104 outbreak and, most recently, ash dieback disease, reflect a new era of community engagement in genomic analyses.