Journal articles on the topic 'Crowd science.Citizen science. Crowdsourcing'
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Cappa, Francesco. "Big data from customers and non-customers through crowdsourcing, citizen science and crowdfunding." Journal of Knowledge Management 26, no. 11 (August 12, 2022): 308–23. http://dx.doi.org/10.1108/jkm-11-2021-0871.
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Purpose The unprecedented growth in the volume, variety and velocity with which data is generated and collected over the last decade has led to the spread of big data phenomenon. Organizations have become increasingly involved in the collection and analysis of big data to improve their performance. Whereas the focus thus far has mainly been on big data collected from customers, the topic of how to collect data also from those who are not yet customers has been overlooked. A growing means of interacting with non-customers is through crowd-based phenomena, which are therefore examined in this study as a way to further collect big data. Therefore, this study aims to demonstrate the importance of jointly considering these phenomena under the proposed framework. Design/methodology/approach This study seeks to demonstrate that organizations can collect big data from a crowd of customers and non-customers through crowd-based phenomena such as crowdsourcing, citizen science and crowdfunding. The conceptual analysis conducted in this study produced an integrated framework through which companies can improve their performance. Findings Grounded in the resource-based view, this paper argues that non-customers can constitute a valuable resource insofar as they can be an additional source of big data when participating in crowd-based phenomena. Companies can, in this way, further improve their performance. Originality/value This study advances scientific knowledge of big data and crowd-based phenomena by providing an overview of how they can be jointly applied to further benefit organizations. Moreover, the framework posited in this study is an endeavour to stimulate further analyses of these topics and provide initial suggestions on how organizations can jointly leverage crowd-based phenomena and big data.
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Mao, Andrew, Ece Kamar, and Eric Horvitz. "Why Stop Now? Predicting Worker Engagement in Online Crowdsourcing." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 1 (November 3, 2013): 103–11. http://dx.doi.org/10.1609/hcomp.v1i1.13076.
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We present studies of the attention and time, or engagement, invested by crowd workers on tasks. Consideration of worker engagement is especially important in volunteer settings such as online citizen science. Using data from Galaxy Zoo, a prominent citizen science project, we design and construct statistical models that provide predictions about the forthcoming engagement of volunteers. We characterize the accuracy of predictions with respect to different sets of features that describe user behavior and study the sensitivity of predictions to variations in the amount of data and retraining. We design our model for guiding system actions in real-time settings, and discuss the prospect for harnessing predictive models of engagement to enhance user attention and effort on volunteer tasks.
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Noel-Storr, Anna, Gordon Dooley, Robin Featherstone, Susanna Wisniewski, Ian Shemilt, James Thomas, Gerald Gartlehner, Barbara Nußbaumer-Steit, and Christopher Mavergames. "Crowdsourcing and COVID-19: a case study of Cochrane Crowd." Journal of EAHIL 17, no. 2 (June 24, 2021): 27–31. http://dx.doi.org/10.32384/jeahil17467.
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Cochrane has used crowdsourcing effectively to identify health evidence since 2014. To date, over 175,000 trialshave been identified for Cochrane’s Central Register of Controlled Trials via Cochrane Crowd (https://crowd.cochrane.org), Cochrane’s citizen science platform, engaging a Crowd of over 20,000 people from 166 countries. The COVID-19 pandemic presented the evidence synthesis community with the enormous challenge of keeping up with the exponential output of COVID-19 research. This case study will detail the new tasks we developed to aid the production of COVID-19 rapid reviews and supply the Cochrane COVID-19 study register. The pandemic initially looked set to disrupt the Crowd team’s plans for 2020 but has in fact served to further our understanding of the potential role crowdsourcing can play in the health evidence ecosystem.
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Hagen, Niclas. "Scaling up and rolling out through the Web." Nordic Journal of Science and Technology Studies 8, no. 1 (May 16, 2020): 4–15. http://dx.doi.org/10.5324/njsts.v8i1.3320.
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The purpose of this paper is to investigate online public participation and engagement in science through crowdsourcing platforms. In order to fulfil this purpose, this paper will use the crowdsourcing platform Zooniverse as a case study, as it constitutes the most prominent and established citizen science platform today. The point of departure for the analysis is that Zooniverse can be seen as a “platformization” of citizen science and scientific citizenship. The paper suggests that the mobilisation of individuals who participate and engage in science on the Zooniverse platform takes place through an epistemic culture that emphasises both authenticity and prospects of novel discoveries. Yet, in the process of turning “raw” data into useable data, Zooniverse has implemented a framework that structures the crowd, something that limits the sort of participation that is offered on the platform. This limitation means that the platform as a whole hardly be seen as fostering a more radical democratic inclusion, for example in the form of a co-production of scientific knowledge, that dissolves the institutional borders between scientists and non-professional volunteers.
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Dhonju, H. K., W. Xiao, B. Shakya, J. P. Mills, and V. Sarhosis. "DOCUMENTATION OF HERITAGE STRUCTURES THROUGH GEO-CROWDSOURCING AND WEB-MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 17–21. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-17-2017.
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Heritage documentation has become increasingly urgent due to both natural impacts and human influences. The documentation of countless heritage sites around the globe is a massive project that requires significant amounts of financial and labour resources. With the concepts of volunteered geographic information (VGI) and citizen science, heritage data such as digital photographs can be collected through online crowd participation. Whilst photographs are not strictly geographic data, they can be geo-tagged by the participants. They can also be automatically geo-referenced into a global coordinate system if collected via mobile phones which are now ubiquitous. With the assistance of web-mapping, an online geo-crowdsourcing platform has been developed to collect and display heritage structure photographs. Details of platform development are presented in this paper. The prototype is demonstrated with several heritage examples. Potential applications and advancements are discussed.
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Isa, Wan Abdul Rahim Wan Mohd, Indah Mohd Amin, and Norhidayah Saubiran. "Mobile Application on Malay Medicinal Plants based on Information Crowdsourcing." Alinteri Journal of Agriculture Sciences 36, no. 2 (August 16, 2021): 208–29. http://dx.doi.org/10.47059/alinteri/v36i2/ajas21135.
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Mobile Application on Malay Medicinal Plants Based on Information Crowdsourcing is an application that provides information on Malay medicinal plants. The information in this application is obtained from a crowd of people including researchers, Malay villagers, traditional medical practitioners, and the public who are willing to share their knowledge and information on Malay medicinal plants. This project focuses on the use of Malay medicinal plants that contain nutrients which is good for human health. There are a lot of Malay medicinal plants founded by the researcher that can help to treat human illnesses. This project involves crowdsourcing. Crowdsourcing is the best way for people to get information from the researchers and crowd people. This project is related to crowdsourcing information systems. Crowdsourcing information systems are information systems that produce informational products or services for internal or external customers by utilizing the potential of crowd people. This project promotes knowledge sharing and awareness among researchers, Malay villagers, traditional medical practitioners, and local herbs entrepreneurs, and the public towards Malay medicinal plants. This project applies the concept of Wikipedia whereby the information is obtained from a crowd of people. It allows the researchers, Malay villagers, traditional medical practitioners, local herbs entrepreneurs, and the public to share their knowledge and findings on Malay medicinal plants on the internet easily. This project also focuses on motivating the public that there are a lot of Malay medicinal plants that can be used for health care. This project is developed in the Malay language as it provides information on Malay medicinal plants and the target user is Malaysia’s citizens. For future enhancement, this project plan to be developed in English and wider target users from other countries.
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Kamar, Ece, Ashish Kapoor, and Eric Horvitz. "Identifying and Accounting for Task-Dependent Bias in Crowdsourcing." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 (September 23, 2015): 92–101. http://dx.doi.org/10.1609/hcomp.v3i1.13238.
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Models for aggregating contributions by crowd workers have been shown to be challenged by the rise of task-specific biases or errors. Task-dependent errors in assessment may shift the majority opinion of even large numbers of workers to an incorrect answer. We introduce and evaluate probabilistic models that can detect and correct task-dependent bias automatically. First, we show how to build and use probabilistic graphical models for jointly modeling task features, workers' biases, worker contributions and ground truth answers of tasks so that task-dependent bias can be corrected. Second, we show how the approach can perform a type of transfer learning among workers to address the issue of annotation sparsity. We evaluate the models with varying complexity on a large data set collected from a citizen science project and show that the models are effective at correcting the task-dependent worker bias. Finally, we investigate the use of active learning to guide the acquisition of expert assessments to enable automatic detection and correction of worker bias.
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Samulowska, Marta, Szymon Chmielewski, Edwin Raczko, Michał Lupa, Dorota Myszkowska, and Bogdan Zagajewski. "Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping." ISPRS International Journal of Geo-Information 10, no. 2 (January 22, 2021): 46. http://dx.doi.org/10.3390/ijgi10020046.
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Crowdsourcing is one of the spatial data sources, but due to its unstructured form, the quality of noisy crowd judgments is a challenge. In this study, we address the problem of detecting and removing crowdsourced data bias as a prerequisite for better-quality open-data output. This study aims to find the most robust data quality assurance system (QAs). To achieve this goal, we design logic-based QAs variants and test them on the air quality crowdsourcing database. By extending the paradigm of urban air pollution monitoring from particulate matter concentration levels to air-quality-related health symptom load, the study also builds a new perspective for citizen science (CS) air quality monitoring. The method includes the geospatial web (GeoWeb) platform as well as a QAs based on conditional statements. A four-month crowdsourcing campaign resulted in 1823 outdoor reports, with a rejection rate of up to 28%, depending on the applied. The focus of this study was not on digital sensors’ validation but on eliminating logically inconsistent surveys and technologically incorrect objects. As the QAs effectiveness may depend on the location and society structure, that opens up new cross-border opportunities for replication of the research in other geographical conditions.
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Hossain, Mokter, and Ilkka Kauranen. "Crowdsourcing: a comprehensive literature review." Strategic Outsourcing: An International Journal 8, no. 1 (February 16, 2015): 2–22. http://dx.doi.org/10.1108/so-12-2014-0029.
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Purpose – The purpose of this paper is to explore the development of crowdsourcing literature. Design/methodology/approach – This study is a comprehensive review of 346 articles on crowdsourcing. Both statistical and contents analyses were conducted in this paper. Findings – ISI listed journal articles, non-ISI listed journal articles and conference articles have had nearly the same contribution in crowdsourcing literature. Articles published in non-ISI listed journals have had an essential role in the initial theory development related to crowdsourcing. Scholars from the USA have authored approximately the same number of articles as scholars from all the European countries combined. Scholars from developing countries have been more relatively active in authoring conference articles than journal articles. Only very recently, top-tier journals have engaged in publishing on crowdsourcing. Crowdsourcing has proven to be beneficial in many tasks, but the extant literature does not give much help to practitioners in capturing value from crowdsourcing. Despite understanding that the motivations of crowds are crucial when planning crowdsourcing activities, the various motivations in different contexts have not been explored sufficiently. A major concern has been the quality and accuracy of information that has been gathered through crowdsourcing. Crowdsourcing bears a lot of unused potential. For example, it can increase employment opportunities to low-income people in developing countries. On the other hand, more should be known of fair ways to organize crowdsourcing so that solution seekers do not get a chance to exploit individuals committing to provide solutions. Research limitations/implications – The literature included in the study is extensive, but an all-inclusive search for articles was limited to only nine selected publishers. However, in addition to the articles retrieved from the nine selected publishers, 52 highly cited articles were also included from other publishers. Practical implications – Crowdsourcing has much unused potential, and the use of crowdsourcing is increasing rapidly. The study provides a thematic review of various applications of crowdsourcing. Originality/value – The study is the first of its kind to explore the development of crowdsourcing literature, discussing the loci and foci of extant articles and listing applications of crowdsourcing. Successful applications of crowdsourcing include idea generation, microtasking, citizen science, public participation, wikies, open source software and citizen journalism.
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Psaraftis, Konstantinos, Theodoros Anagnostopoulos, and Klimis Ntalianis. "An innovative quality lane change evaluation scheme based on reliable crowd-ratings." Computer Science and Information Systems, no. 00 (2022): 30. http://dx.doi.org/10.2298/csis210830030p.
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Intelligent Transportation Systems (ITSs) and their applications are attracting significant attention in research and industry. ITSs make use of various sensing and communication technologies to assist transportation authorities and vehicle drivers in making informative decisions and provide leisure and safe driving experience. Data collection and dispersion are of utmost importance for the proper operation of ITSs applications. Numerous standards, architectures and communication protocols have been anticipated for ITSs applications. In recent years, crowdsourcing methods have shown to provide important benefits to ITSs, where ubiquitous citizens, acting as mobile human sensors, help respond to signals and providing real-time information. In this paper, the problem of mitigating crowdsourced data bias and malicious activity is addressed, when no auxiliary information is available at the individual level, as a prerequisite for achieving better quality data output. To achieve this goal, an innovative algorithm is designed and tested on a crowdsourcing database of lane change evaluations. A three-month crowdsourcing campaign is performed with 70 participants, resulting in a large number of lane changes evaluations. The proposed algorithm can negate the noisy ground-truth of crowdsourced data and improve the overall quality.
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Skarlatidou, Artemis, and Marcos Moreu. "Maps in Citizen Science: A Preliminary Analysis of Use and User Issues." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-339-2019.
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<p><strong>Abstract.</strong> Citizen Science involves a collaboration or partnership between scientists and amateur volunteers, which may take various forms; from simple data collection to a close collaboration where both parts jointly define their aims, methodologies and analysis approaches in the scientific endeavour. Although citizen science has existed for more than two centuries (Silvertown, 2009), the widespread use of information and communication technology (ICT) now plays a significant role in the way citizen science is currently shaped and utilised. At present, there are hundreds of citizen science applications available which engage thousands of volunteers in the disciplines of astronomy, environmental conservation, biology, marine science, geography and many others. A relatively recent analysis of 388 citizen science projects revealed that they have been used to engage 1.3 million volunteers, contributing up to US$2.5 billion in-kind annually (Theobald et al. 2015).</p><p>Web 2.0 and its associated technologies, which have existed for almost 15 years now, have enabled the development of websites which supported content generation by their end users (aka crowdsourcing; Howe, 2008) and multiple interactions amongst them. Examples include web-based communities, social-networking sites, wikis, mashups, and others (Batty et al., 2010). In this context the term ‘Neogeography’ was coined (Eisnor, 2006) and since then it has been used within the geographic and cartographic circles to describe the multi-directional generation of geospatial contents and interactions, which enables non-GIS professionals to create and share maps and other geographic information online “on their own terms” simply using the “elements of an existing toolset” (Eisnor, 2006). Map mashups started to not only be used for disseminating spatial information to a wider user audience, but applications have been created which enabled the crowdsourcing of geographic information for the production of geospatial knowledge; a trend, which is also known under the term Volunteered Geographic Information (Goodchild, 2007). OpenStreetMap (OSM) is perhaps one of the earliest examples that the literature cites to demonstrate how harnessing the power of the crowds for the collection of geographic information can result in the creation of a free, open source of map of the world (Goodchild, 2007; Haklay et al., 2008; Batty et al., 2010).</p><p>We argue in this paper that the above developments from the geospatial context have massively contributed to the current state of citizen science. While interactive web maps made their appearance as mainly “way-finding” tools (Skarlatidou and Haklay, 2006), they quickly became part of digital interactions in a much broader context and they are currently a basic component of most citizen science projects. The relevance and significance of space has been fully exploited by technological features such as geotagging, GPS-enabled mobile devices fully integrated with other sensors, which has made the collection and sharing of data much easier (Haklay, 2013). Sinton (2018) argues that it is such the power of maps in citizen science that “it would be difficult to pursue a project in biological conservation, for example, without incorporating mapping”. The breadth of citizen science applications is so wide that we observe an extremely wide range of potential users, with very different skill sets, backgrounds, literacy levels and user needs.</p>
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Bu, Qiong, Elena Simperl, Adriane Chapman, and Eddy Maddalena. "Quality assessment in crowdsourced classification tasks." International Journal of Crowd Science 3, no. 3 (September 2, 2019): 222–48. http://dx.doi.org/10.1108/ijcs-06-2019-0017.
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Purpose Ensuring quality is one of the most significant challenges in microtask crowdsourcing tasks. Aggregation of the collected data from the crowd is one of the important steps to infer the correct answer, but the existing study seems to be limited to the single-step task. This study aims to look at multiple-step classification tasks and understand aggregation in such cases; hence, it is useful for assessing the classification quality. Design/methodology/approach The authors present a model to capture the information of the workflow, questions and answers for both single- and multiple-question classification tasks. They propose an adapted approach on top of the classic approach so that the model can handle tasks with several multiple-choice questions in general instead of a specific domain or any specific hierarchical classifications. They evaluate their approach with three representative tasks from existing citizen science projects in which they have the gold standard created by experts. Findings The results show that the approach can provide significant improvements to the overall classification accuracy. The authors’ analysis also demonstrates that all algorithms can achieve higher accuracy for the volunteer- versus paid-generated data sets for the same task. Furthermore, the authors observed interesting patterns in the relationship between the performance of different algorithms and workflow-specific factors including the number of steps and the number of available options in each step. Originality/value Due to the nature of crowdsourcing, aggregating the collected data is an important process to understand the quality of crowdsourcing results. Different inference algorithms have been studied for simple microtasks consisting of single questions with two or more answers. However, as classification tasks typically contain many questions, the proposed method can be applied to a wide range of tasks including both single- and multiple-question classification tasks.
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El, Alaoui, Fazziki El, Fatima Ennaji, and Mohamed Sadgal. "A mobile crowd sensing framework for suspect investigation: An objectivity analysis and de-identification approach." Computer Science and Information Systems 17, no. 1 (2020): 253–69. http://dx.doi.org/10.2298/csis190427039e.
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The ubiquity of mobile devices and their advanced features have increased the use of crowdsourcing in many areas, such as the mobility in the smart cities. With the advent of high-quality sensors on smartphones, online communities can easily collect and share information. These information are of great importance for the institutions, which must analyze the facts by facilitating the data collecting on crimes and criminals, for example. This paper proposes an approach to develop a crowdsensing framework allowing a wider collaboration between the citizens and the authorities. In addition, this framework takes advantage of an objectivity analysis to ensure the participants? credibility and the information reliability, as law enforcement is often affected by unreliable and poor quality data. In addition, the proposed framework ensures the protection of users' private data through a de-identification process. Experimental results show that the proposed framework is an interesting tool to improve the quality of crowdsensing information in a government context.
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Capel, Horacio. "Ciencia ciudadana, ética y política para viejos y nuevos problemas." Locale 1, no. 1 (March 21, 2017): 29–69. http://dx.doi.org/10.14409/rl.v1i1.6264.
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Los retos a los que se enfrentan los países iberoamericanos son numerosos. Algunos son viejos y siguen estando vigentes, pero a ellos se han unido otros nuevos, como resultado de las transformaciones del mundo actual.Para estudiarlos y enfrentarse a ellos, hoy es posible asociar a los ciudadanos a las investigaciones científicas que se realizan. No solo se puede contar con la colaboración de no profesionales en la recolección de datos. lo que ya es muy importante, sino que éstos también pueden hacer preguntas, expresar críticas o dudas, y sugerir nuevas caminos en los trabajos científicos que se realizan. Se alude hoy a ello con el término «ciencia ciudadana», y con calificativos tales como ciencia cívica, ciencia en red, ciencia colaborativa, crowd science, crowd– source science, y otras. Se trata de las investigaciones científicas que se llevan a cabo por científicos con la colaboración de no profesionales, aficionados y gente común, a menudo como forma de colaboración abierta y de micromecenazgo. Lo cual contribuye a extender la práctica científica, en un mundo en el que la población tiene una formación cultural cada vez más elevada.La necesidad de comportamientos éticos se reivindica cada vez más en la crisis actual. En este artículo se defiende que la ética está vinculada a las costumbres y a la política. Aunque las alusiones a la ética son bienvenidas, lo que se necesitan son medidas políticas. En el artículo se abordan los problemas de la ética y su relación con las costumbres y con la política, lo que ya fue teorizado por Aristóteles en su Ética Nicomáquea y por Kant en su Metafísica de las costumbres. La coacción jurídica es fundamental y prioritaria, y los principios religiosos no deben afectar a las leyes que se elaboran y se aprueban democráticamente con validez para todos.Palabras clave: política y ética; problemas en países iberoamericanos; problemas viejos y nuevos; ciencia en colaboración. AbstractCITIZEN SCIENCE, ETHICS AND POLITICAL TO OLD AND NEW PROBLEMSThe challenges facing Ibero American countries are numerous. Some are old and are still in force; but they have been joined by new ones as a result of current world's change.To study and face them, it is possible to associate citizens to scientific research which are being conducted. Not only it is posible to count on the collaboration non–professional in data collection, which is a key issue, but they can also ask questions, express criticism or doubts, and suggest new ways in scientific work done. It is being referred today with the terms citizen science, civic science, networked science, collaborative science, crowd science, and others. It is about the scientific investigations conducted by scientists with the help of non–professionals, amateurs and ordinary people (often as crowdsourcing and crowdfunding), thus contributing to widespread scientific practice, in a world in which the population has a high and increasing cultural training. The need for ethical behaviours are increasingly calling with the current crisis. In this paper it is argued that ethics is linked to the customs and politics.Although allusions to ethics are welcome, what is needed are political measures. This paper addresses the problems of ethics and its relationship with the customs and which politics, which was already theorized by Aristotle in his Nicomachean Ethics and by Kant in his Metaphysics of Morals. Legal coercion is fundamental and a priority, and religious principles should not affect the laws that are developed and approved democratically whith validity for everyone.Keywords: politics and ethics; issues in Ibero American countries; old and new problems; science in collaboration; collaborative science. ResumoCIÊNCIA CIDADÃ, ÉTICA E POLÍTICA PARA PROBLEMAS VELHOS E NOVOSCiência cidadã, ética e política para problemas velhos e novos Os desafíos enfrentados pelos países ibero–americanos são inúmeros. Alguns são antigos, porém ainda vigentes. E a eles se uniram outros novos, como resultado das tranformações do mundo atual.Para estudá–los e enfrentá–los, hoje é possível associar os cidadãos às pesquisas científicas que se realizam. Não somente contar com a colaboração de não–profissionais na coleta de dados, o que já é muito importante, mas os mesmos também podem fazer perguntas, expressar suas críticas e dúvidas, além de sugerir novos caminhos aos trabalhos científicos. O termo «ciência cidadã» hoje é usado para referir–se a este cenário, com denominações tais como ciência cívica, ciência em rede, ciência colaborativa, crowd science, crowd source science, entre outros. Tratam–se de pesquisas científicas realizadas por cientistas com o apoio de não–profissionais, estusiastas e gente comum, muitas vezes através de colaboração aberta e financiamento coletivo. O que acaba contribuindo para estender a prática científica, em um mundo no qual a população tem uma formação cultural cada vez mais elevada.Urge a necessidade de comportamentos éticos na atual crise. Neste artigo defende–se que a ética está vinculada aos costumes e à política, abordando seus problemas e relações, o que já foi teorizado por Aristóteles em Ética a Nicômaco e por Kant em Metafísica dos Costumes. Apesar das alusões à ética serem bem–vindas, o que se necessita na realidade são medidas políticas. A força jurídica é fundamental e prioritária, e os princípios religiosos não devem afetar as leis que são elaboradas e aprovadas democraticamente, válida para todos.Palavras–chave: política e ética; desafios em países ibero americanos; problemas velhos e novos; ciência colaborativa.
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Shang, Rui, YongMei Ma, Farhad Ali, ChuanShuang Hu, Shah Nazir, Huafei Wei, and Abdullah Khan. "Selection of Crowd in Crowdsourcing for Smart Intelligent Applications: A Systematic Mapping Study." Scientific Programming 2021 (July 8, 2021): 1–23. http://dx.doi.org/10.1155/2021/9368128.
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Crowdsourcing is a task-solving model in which human crowd is hired to solve a particular task. During the crowdsourcing process, the crowd selection is performed in order to select appropriate crowd workers for a specific task; without appropriate selection of crowd workers, the process of crowdsourcing is aimless. The main goal of this paper was to identify the features of crowd in crowdsourcing activity, reasons behind crowd participation in the activity of crowdsourcing, and the existing techniques that were utilized for crowd selection in crowdsourcing. Search strings with corresponding keywords were used to capture relevant studies related to crowdsourcing, and crowd selection was classified under conference papers, journal articles, proceedings, and book chapters. 81 relevant studies are selected from 7 digital data repositories using a search strategy. In crowdsourcing practices, crowd selection was considerably addressed. Nonetheless, it has been noticed that the selection is based only on a single crowd worker attribute such as confidence, past success, efficiency, and experience. For the efficiency and effectiveness of the crowdsourcing operation, crowd selection on multicriteria features is essential.
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Wang, Guan, Farhad Ali, Jonghoon Yang, Shah Nazir, Ting Yang, Abdullah Khan, and Muhammad Imtiaz. "Multicriteria-Based Crowd Selection Using Ant Colony Optimization." Complexity 2021 (January 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/6622231.
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Internet-enabled technologies have provided a way for people to communicate and collaborate with each other. The collaboration and communication made crowdsourcing an efficient and effective activity. Crowdsourcing is a modern paradigm that employs cheap labors (crowd) for accomplishing different types of tasks. The task is usually posted online as an open call, and members of the crowd self-select a task to be carried out. Crowdsourcing involves initiators or crowdsourcers (an entity usually a person or an organization who initiate the crowdsourcing process and seek out the ability of crowd for a task), the crowd (online participant who is a having a particular background, qualification, and experience for accomplishing task in crowdsourcing activity), crowdsourcing task (the activity in which the crowd contribute), the process (how the activity is carried out), and the crowdsourcing platform (software or market place) where requesters offer various tasks and crowd workers complete these tasks. As the crowdsourcing is carried out in the online environment, it gives rise to certain challenges. The major problem is the selection of crowd that is becoming a challenging issue with the growth in crowdsourcing popularity. Crowd selection has been significantly investigated in crowdsourcing processes. Nonetheless, it has observed that the selection is based only on a single feature of the crowd worker which was not sufficient for appropriate crowd selection. For addressing the problem of crowd selection, a novel “ant colony optimization-based crowd selection method” (ACO-CS) is presented in this paper that selects a crowd worker based on multicriteria features. By utilizing the proposed model, the efficiency and effectiveness of crowdsourcing activity will be increased.
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Siddharthan, Advaith, Christopher Lambin, Anne-Marie Robinson, Nirwan Sharma, Richard Comont, Elaine O'mahony, Chris Mellish, and René Van Der Wal. "Crowdsourcing Without a Crowd." ACM Transactions on Intelligent Systems and Technology 7, no. 4 (July 14, 2016): 1–20. http://dx.doi.org/10.1145/2776896.
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Huang, Yongjun, Shah Nazir, Jiyu Wu, Fida Hussain Khoso, Farhad Ali, and Habib Ullah Khan. "An Efficient Decision Support System for the Selection of Appropriate Crowd in Crowdsourcing." Complexity 2021 (June 4, 2021): 1–11. http://dx.doi.org/10.1155/2021/5518878.
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Crowdsourcing is a complex task-solving model that utilizes humans for solving organizational specific problems. For assigning a crowdsourced task to an online crowd, crowd selection is carried out to select appropriate crowd for achieving the task. The efficiency and effectiveness of crowdsourcing may fail if irrelevant crowd is selected for performing a task. Early decisions regarding selection of a crowd can ultimately lead to successful completion of tasks. To select most appropriate crowd from crowdsourcing, this paper presents a decision support system (DSS) for appropriate selection of crowd. The system has been implemented in the Superdecision tool by plotting hierarchy of goals, criteria, and alternatives. Various calculations have been done for performing the proposed research. Results of the study reveal that the proposed system is effective and efficient for selection of crowd in crowdsourcing by performing various pairwise computation of the study.
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Del Savio, Lorenzo, Barbara Prainsack, and Alena Buyx. "Crowdsourcing the Human Gut. Is crowdsourcing also 'citizen science'?" Journal of Science Communication 15, no. 03 (April 20, 2016): A03. http://dx.doi.org/10.22323/2.15030203.
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The participation of non-professionally trained people in so-called citizen science (CS) projects is a much discussed topic at the moment. Frequently, however, the contribution of citizens is limited to only a few narrow tasks. Focusing on an initiative dedicated to the study of the human microbiome, this paper describes such a case where citizen participation is limited to the provision of funding, samples, and personal data. Researchers opted for a crowdsourced approaches because other forms of funding and recruitment did not seem feasible. We argue that despite the narrow understanding of participation in the context of some CS projects, they can address some of the democratic concerns related to scientific knowledge creation. For example, CS and crowdsourcing can help to foster dialogue between researchers and publics, and increase the influence of citizens on research agenda setting.
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Becker, Denny. "Citizen Science in Archiven." ABI Technik 40, no. 1 (January 28, 2020): 30–39. http://dx.doi.org/10.1515/abitech-2020-1004.
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ZusammenfassungDie Fotografie hat sich seit ihrer Entstehung zum Massenmedium entwickelt. Die technische Weiterentwicklung führte im 20. und 21. Jahrhundert zur Explosion der fotografischen Überlieferungen. Mit dem Wandel zur Informations- und Mediengesellschaft und der steigenden Bedeutung der Fotografie als wissenschaftliche Quelle, steigen auch die Anforderungen an Archive, diese Quellengattung angemessen zu erschließen und der Forschung zugänglich zu machen. Für die Archive bedeutet die Erschließung von Fotobeständen eine hohe Herausforderung. Einerseits ist die große Masse kaum zu bewältigen, andererseits fehlt es den Mitarbeiterinnen und Mitarbeitern häufig an Wissen über den historischen Kontext. In den Archivwissenschaften werden derzeit neue Erschließungsmethoden diskutiert und erprobt, um durch sogenanntes Crowdsourcing Nutzerinnen und Nutzer an der Erschließung zu beteiligen. Der Artikel beschreibt verschiedene Crowdsourcing-Projekte für die Erschließung genealogischer und fotografischer Archivalien und stellt Chancen und Risiken gegenüber.
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Zhao, Yuxiang (Chris), and Qinghua Zhu. "Conceptualizing task affordance in online crowdsourcing context." Online Information Review 40, no. 7 (November 14, 2016): 938–58. http://dx.doi.org/10.1108/oir-06-2015-0192.
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Purpose The purpose of this paper is to present a new concept – task affordance in crowdsourcing context, and build it as a theoretical lens to help the authors reconfigure the artifacts and process in task-oriented crowdsourcing projects. The paper differs from previous studies by focusing on the relationships between the task artifacts, systems and goal-directed actors in crowdsourcing process rather than on the pure examination of task properties. Design/methodology/approach An operational definition of task affordance was proposed and a pseudo-entity-relationship model based approach was employed to portrait the task affordance in online crowdsourcing context. Furthermore, the authors developed a typology of task affordance and decomposed the concept into five dimensions, namely, design affordance, presentation affordance, assignment affordance, task-platform fit affordance, and task-worker fit affordance. A preliminary analysis of task affordances across various crowdsourcing categories was also conducted to validate the proposed typological framework. Findings The findings show that the task affordances have varying degree and extend among the diverse crowdsourcing categories. For instance, task design affordances seem to be low in the crowd processing and crowd rating cases compared with that in the crowd solving and crowd creation cases. For another example, in terms of the task presentation affordance, crowd rating cases need the lowest affordance while the crowd creation cases need the highest affordance. Therefore, the authors would like to emphasize that the successful adoption, implementation, and design of the task-oriented crowdsourcing owes to the careful examination of the relationships among the actors, artifacts, and environment of the crowdsourcing projects. Originality/value To the authors’ best knowledge, this paper is the first study on conceptualizing the task affordance in online crowdsourcing context. The study contributes to the academic literature on a comprehensive overview of task-related studies in crowdsourcing, which are scattered in several information related fields. Furthermore, this research contributes directly to the area of information science and technology due to a common interest in studying the environments and contexts in which people, information and technology interact and interplay. Practically, this study may yield some implications for the requester and platform operator when designing the relevant tasks or developing the specific crowdsourcing platform.
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Kafka, S., and E. Griffin. "Stellar Variability: from Citizen Science to Citizen Astronomy." Proceedings of the International Astronomical Union 14, S339 (November 2017): 215–19. http://dx.doi.org/10.1017/s1743921318002636.
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AbstractThe contribution of citizens to research is irrefutable. Especially this century with the outburst of all-sky surveys, professional astronomers use citizen-science projects to engage the public in analysing and sorting large quantities of data, often leading to noteworthy discoveries. From crowdsourcing to acquiring data, citizens are leaving a significant mark in the science landscape, assisting professional astronomers with their work. In turn, citizen science is a means of increasing science literacy and public understanding of science. At the same time, the time domain enables a more active engagement of backyard observers in research. Citizen astronomers not only take data, but also reduce and analyse them, and participate in preparing scientific manuscripts.
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Maboudi, Tofigh, and Ghazal P. Nadi. "Crowdsourcing the Egyptian Constitution." Political Research Quarterly 69, no. 4 (July 29, 2016): 716–31. http://dx.doi.org/10.1177/1065912916658550.
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Drawing on empirical evidence from online citizen feedback on the 2012 Egyptian Constitution, we demonstrate that despite normative skepticism about implications of participatory constitution making, citizen participation matters. Using data of more than 650,000 online votes and comments on the constitution, we find that draft provisions with higher public approval are less likely to change and those with lower approval are more likely to change. We also find that Articles related to rights and freedoms are more likely to change based on online public input. Finally, following the boycott of the Constituent Assembly by non-Islamists, changes in draft Articles based on public feedback drop sharply. These findings highlight the conditions under which participatory constitution making becomes more effective. First, consensus among citizens over the most salient issues increases the probability that those issues would be successfully incorporated in the constitution. Second, without ex ante elite agreement over the design of the constitution, it becomes difficult to account for citizen proposals amid political clash between elites.
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Smith, Monica L. "Citizen Science in Archaeology." American Antiquity 79, no. 04 (October 2014): 749–62. http://dx.doi.org/10.7183/0002-7316.79.4.749749.
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Abstract Citizen science, as a process of volunteer participation through crowdsourcing, facilitates the creation of mass data sets needed to address subtle and large-scale patterns in complex phenomena. Citizen science efforts in other field disciplines such as biology, geography, and astronomy indicate how new web-based interfaces can enhance and expand upon archaeologists’ existing platforms of volunteer engagement such as field schools, community archaeology, site stewardship, and professional–avocational partnerships. Archaeological research can benefit from the citizen science paradigm in four ways: fieldwork that makes use of widely available technologies such as mobile applications for photography and data upload; searches of large satellite image collections for site identification and monitoring; crowdfunding; and crowdsourced computer entry of heritage data.
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Uhlmann, Eric Luis, Charles R. Ebersole, Christopher R. Chartier, Timothy M. Errington, Mallory C. Kidwell, Calvin K. Lai, Randy J. McCarthy, Amy Riegelman, Raphael Silberzahn, and Brian A. Nosek. "Scientific Utopia III: Crowdsourcing Science." Perspectives on Psychological Science 14, no. 5 (July 2019): 711–33. http://dx.doi.org/10.1177/1745691619850561.
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Most scientific research is conducted by small teams of investigators who together formulate hypotheses, collect data, conduct analyses, and report novel findings. These teams operate independently as vertically integrated silos. Here we argue that scientific research that is horizontally distributed can provide substantial complementary value, aiming to maximize available resources, promote inclusiveness and transparency, and increase rigor and reliability. This alternative approach enables researchers to tackle ambitious projects that would not be possible under the standard model. Crowdsourced scientific initiatives vary in the degree of communication between project members from largely independent work curated by a coordination team to crowd collaboration on shared activities. The potential benefits and challenges of large-scale collaboration span the entire research process: ideation, study design, data collection, data analysis, reporting, and peer review. Complementing traditional small science with crowdsourced approaches can accelerate the progress of science and improve the quality of scientific research.
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Hansson, Karin, and Thomas Ludwig. "Crowd Dynamics: Conflicts, Contradictions, and Community in Crowdsourcing." Computer Supported Cooperative Work (CSCW) 28, no. 5 (November 29, 2018): 791–94. http://dx.doi.org/10.1007/s10606-018-9343-z.
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Zhang, Xinglin, Longfei Shangguan, and Ye Yuan. "A Crowd Wisdom Management Framework for Crowdsourcing Systems." IEEE Access 4 (2016): 9764–74. http://dx.doi.org/10.1109/access.2016.2581298.
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Lee, Doris Jung-Lin, Joanne Lo, Moonhyok Kim, and Eric Paulos. "Crowdclass: Designing Classification-Based Citizen Science Learning Modules." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (September 21, 2016): 109–18. http://dx.doi.org/10.1609/hcomp.v4i1.13273.
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In this paper, we introduce Crowdclass, a novel framework that integrates the learning of advanced scientific concepts with the crowdsourcing microtask of image classification. In Crowdclass, we design questions to serve as both a learning experience and a scientific classification. This is different from conventional citizen science platforms which decompose high level questions into a series of simple microtasks that require no scientific background knowledge to complete. We facilitate learning within the microtask by providing content that is appropriate for the participant’s level of knowledge through scaffolding learning. We conduct a between-group study of 93 participants on Amazon Mechanical Turk comparing Crowdclass to the popular citizen science project Galaxy Zoo. We find that the scaffolding presentation of content enables learning of more challenging concepts. By understanding the relationship between user motivation, learning, and performance, we draw general design principles for learning-as-an-incentive interventions applicable to other crowdsourcing applications.
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Yang, Keyu, Yunjun Gao, Lei Liang, Song Bian, Lu Chen, and Baihua Zheng. "CrowdTC: Crowd-powered Learning for Text Classification." ACM Transactions on Knowledge Discovery from Data 16, no. 1 (July 3, 2021): 1–23. http://dx.doi.org/10.1145/3457216.
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Text classification is a fundamental task in content analysis. Nowadays, deep learning has demonstrated promising performance in text classification compared with shallow models. However, almost all the existing models do not take advantage of the wisdom of human beings to help text classification. Human beings are more intelligent and capable than machine learning models in terms of understanding and capturing the implicit semantic information from text. In this article, we try to take guidance from human beings to classify text. We propose Crowd-powered learning for Text Classification (CrowdTC for short). We design and post the questions on a crowdsourcing platform to extract keywords in text. Sampling and clustering techniques are utilized to reduce the cost of crowdsourcing. Also, we present an attention-based neural network and a hybrid neural network to incorporate the extracted keywords as human guidance into deep neural networks. Extensive experiments on public datasets confirm that CrowdTC improves the text classification accuracy of neural networks by using the crowd-powered keyword guidance.
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Zakariah, Zazaleena, Norjansalika Janom, and Noor Habibah Arshad. "Low Income Community as Crowd Worker for Crowdsourcing: Issues, Challenges and Future Direction." International Journal of Engineering & Technology 7, no. 4.33 (December 9, 2018): 65. http://dx.doi.org/10.14419/ijet.v7i4.33.23486.
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World are now facing flood of low income community responding to current global issue regarding employment. Considering the advantages of crowdsourcing, it has been identified as one potential solution to the issue. Responding to that, the concept has been implemented in many developing countries that have high rate of low income community such as India, Nepal and Philippines. Through crowdsourcing, business and individual can achieve greater and higher productivity in faster and shorter period. Crowdsourcing offer flexibility in terms of time, locations and durations of doing tasks and the most important is crowdsourcing will give additional income to complement existing income that will help to uplift one’s lifestyle hence reduce the rate of low income community. However, focusing on low income community as crowd worker for crowdsourcing require depth understanding on varies factors such as crowd education levels, skills, attitudes, language and etcetera. Visibly, there are numerous issues and challenges in the implementation process. Thus, this study aims to discuss the issues and challenges when dealing with low income community as crowd worker. This study presents seven vital factors that need to be taken into deliberation when dealing with low income community. The results and discussion from this study can be used as a road map to prepare an action plan for a successful crowdsourcing implementation for low income community.
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Cerquides, Jesus, Mehmet Oğuz Mülâyim, Jerónimo Hernández-González, Amudha Ravi Shankar, and Jose Luis Fernandez-Marquez. "A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data." Mathematics 9, no. 8 (April 15, 2021): 875. http://dx.doi.org/10.3390/math9080875.
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Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. In this work we argue that being able to determine the accuracy of data obtained by crowdsourcing is a fundamental question and we point out that, for many real-life scenarios, mathematical tools and processes for the evaluation of data quality are missing. We propose a probabilistic methodology for the evaluation of the accuracy of labeling data obtained by crowdsourcing in citizen science. The methodology builds on an abstract probabilistic graphical model formalism, which is shown to generalize some already existing label aggregation models. We show how to make practical use of the methodology through a comparison of data obtained from different citizen science communities analyzing the earthquake that took place in Albania in 2019.
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Oesterlund, Carsten S., Gabriel Mugar, Corey Jackson, Katie DeVries Hassman, and Kevin Crowston. "Socializing the Crowd: Learning to Talk in Citizen Science." Academy of Management Proceedings 2014, no. 1 (January 2014): 16799. http://dx.doi.org/10.5465/ambpp.2014.16799abstract.
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Acorn, John H. "Entomological citizen science in Canada." Canadian Entomologist 149, no. 6 (October 6, 2017): 774–85. http://dx.doi.org/10.4039/tce.2017.48.
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AbstractCitizen science involves voluntary participation in the scientific process, typically by gathering data in order to monitor some aspect of the natural world. Entomological citizen science, as an extension of traditional amateur entomology, is an active field in Canada, with online databases such as eButterfly and BugGuide attracting both contributors and database users. As well, traditional amateur entomology continues to be important in Canada, as do short-term insect-themed educational events, the involvement of amateurs in entomological societies, and online crowdsourcing initiatives. Success of citizen science projects can be measured in many ways. In terms of published papers that analyse trends in citizen science data, Canadian projects have only begun to deliver. More valuable are particular records that improve our knowledge of geographic ranges and phenology. In terms of the endurance of particular projects, and the willingness of volunteers to participate, citizen science entomology in Canada is clearly a success. However, quality control of citizen science data remains an issue for some projects. As well, challenges remain with respect to balancing the goals of researchers, participants, and supporting institutions.
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Leung, Gabriel Shing-Koon, Vincent Cho, and C. H. Wu. "Crowd Workers' Continued Participation Intention in Crowdsourcing Platforms." Journal of Global Information Management 29, no. 6 (November 2021): 1–28. http://dx.doi.org/10.4018/jgim.20211101.oa13.
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The micro-task crowdsourcing marketplace, as a novel platform, has provided firms with a new way to recruit employees at a reasonable cost and with a fast turnaround. This research explores how different types of motivations affect individuals’ continued participation intention in compensation-based micro-task crowdsourcing platforms. Our theoretical model builds on expectancy theory, self-determination theory, organizational justice theory and self-efficacy theory. To validate the theoretical model, over 1,000 crowd workers participating in Amazon’s Mechanical Turk completed an online questionnaire. Distributive justice and self-efficacy were applied to moderate the relationship between different types of motivations and continued participation intention. The confirmed three-way interaction effects indicated that external regulation and intrinsic motivation on continued participation intention are contingent on distributive justice and the level of self-efficacy. The findings enrich the understanding of MCS communities and provide important guidelines for motivating crowd workers.
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Wexler, Mark N. "Reconfiguring the sociology of the crowd: exploring crowdsourcing." International Journal of Sociology and Social Policy 31, no. 1/2 (March 2011): 6–20. http://dx.doi.org/10.1108/01443331111104779.
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Hettiachchi, Danula, Vassilis Kostakos, and Jorge Goncalves. "A Survey on Task Assignment in Crowdsourcing." ACM Computing Surveys 55, no. 3 (April 30, 2023): 1–35. http://dx.doi.org/10.1145/3494522.
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Quality improvement methods are essential to gathering high-quality crowdsourced data, both for research and industry applications. A popular and broadly applicable method is task assignment that dynamically adjusts crowd workflow parameters. In this survey, we review task assignment methods that address: heterogeneous task assignment, question assignment, and plurality problems in crowdsourcing. We discuss and contrast how these methods estimate worker performance, and highlight potential challenges in their implementation. Finally, we discuss future research directions for task assignment methods, and how crowdsourcing platforms and other stakeholders can benefit from them.
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Chen, Liang, Pei Xu, and De Liu. "Effect of Crowd Voting on Participation in Crowdsourcing Contests." Journal of Management Information Systems 37, no. 2 (April 2, 2020): 510–35. http://dx.doi.org/10.1080/07421222.2020.1759342.
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Mueller, Johannes, Hangxin Lu, Artem Chirkin, Bernhard Klein, and Gerhard Schmitt. "Citizen Design Science: A strategy for crowd-creative urban design." Cities 72 (February 2018): 181–88. http://dx.doi.org/10.1016/j.cities.2017.08.018.
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SKOPIK, FLORIAN, DANIEL SCHALL, and SCHAHRAM DUSTDAR. "DISCOVERING AND MANAGING SOCIAL COMPOSITIONS IN COLLABORATIVE ENTERPRISE CROWDSOURCING SYSTEMS." International Journal of Cooperative Information Systems 21, no. 04 (December 2012): 297–341. http://dx.doi.org/10.1142/s0218843012500050.
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Crowdsourcing is an increasingly used model to outsource certain tasks to be carried out by external experts on the Web. Especially when lacking experience or expertise with certain task types, crowdsourcing offers a convenient way to receive instant support. In this paper, we introduce an in-house enterprise crowdsourcing model, which leverages the crowdsourcing concept and transfers it to traditional organizations. Here, a company's staff is considered a crowd that — besides its regularly assigned tasks — can also receive tasks from colleagues from other departments and across hierarchical structures. The aim is to offer instant support and utilize free capacities throughout a large organization more efficiently. In our work, we describe this concept and supporting mechanisms in context of an agile software development use case. However, in contrast to usually crowdsourced microtasks, complex software architectures usually consist of tens and hundreds of connected modules that can be potentially crowdsourced. These technical dependencies between modules require active coordination and interactions between crowd members that process the single artifacts. Hence, technical dependencies of artifacts result in social dependencies of collaborating crowd members that create them. In order to efficiently discover member compositions based on artifact dependencies, we introduce an indexing and discovery approach based on subgraph matching. Typically, assigning tasks to well-rehearsed teams results in more reliable task processing, faster results, and higher quality of work. We evaluate our approach in terms of system scalability and overall applicability by mining and analyzing the popular SourceForge community. We show that our approach of member composition discovery is feasibly in terms of scalability and quality of discovery results. Our findings deliver important input for the design and implementation of supporting information systems for future large-scale collaboration platforms.
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English, P. B., M. J. Richardson, and C. Garzón-Galvis. "From Crowdsourcing to Extreme Citizen Science: Participatory Research for Environmental Health." Annual Review of Public Health 39, no. 1 (April 2018): 335–50. http://dx.doi.org/10.1146/annurev-publhealth-040617-013702.
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Balcom, Brittany. "Improving Crowdsourcing and Citizen Science as a Policy Mechanism for NASA." New Space 3, no. 2 (June 2015): 98–116. http://dx.doi.org/10.1089/space.2015.0017.
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Younis, Eman M. G., Eiman Kanjo, and Alan Chamberlain. "Designing and evaluating mobile self-reporting techniques: crowdsourcing for citizen science." Personal and Ubiquitous Computing 23, no. 2 (March 29, 2019): 329–38. http://dx.doi.org/10.1007/s00779-019-01207-2.
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Liang, Wen-Tzong, Jian-Cheng Lee, Kate Huihsuan Chen, and Nai-Chi Hsiao. "Citizen Earthquake Science in Taiwan: From Science to Hazard Mitigation." Journal of Disaster Research 12, no. 6 (November 29, 2017): 1174–81. http://dx.doi.org/10.20965/jdr.2017.p1174.
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Taiwan is located at the convergent plate boundary between the Eurasian and the Philippine Sea plates. As a result, intense earthquake activity and associated surface deformation are manifesting in this region. To implement and promote citizen earthquake science in Taiwan, we have developed several web-based platforms with multi-purpose themes, such as earthquake science information, popular science education, and crowdsourcing systems. First, with the rapid earthquake report issued from the Taiwan Central Weather Bureau (CWB), the available near real-time scientific results obtained from the Taiwanese seismology community are collected and published to a platform, the Taiwan Earthquake Science Information System (TESIS). The scientific information archived at TESIS includes CWB earthquake reports, focal mechanisms, shake maps, and finite source models (for strong earthquakes). All real-time results are integrated into a GIS system with background geospatial information, such as geological maps, traces of active faults, background seismicity, and inter-seismic GPS velocity fields. Second, by collaborating with Stanford University to maintain a regional Quake-Catcher Network (QCN) server in Taiwan, we have promoted citizen seismology in Asia by bringing earthquake information and scientific knowledge to the public. More than 200 school teachers have already installed the QCN sensors in Internet-enabled computers. Through two web-based educational platforms, users are able to access the guidelines and further interact with the recorded waveforms. Third, we also developed an earthquake damage reporting system – the Taiwan Scientific Earthquake Reporting (TSER) system – to encourage the citizen to collect field observation for significant earthquake-induced ground damages such as surface fault rupture, landslide, rock fall, liquefaction, and landslide-triggered dam or lake. The TSER system is constructed under the Ushahidi mapping platform, which has been widely used in crowdsourcing for the geospatial archiving of events. Trained high school teachers and public volunteers can send their ground damage observations, including photographs, through the TSER system. Most of these products and online systems are now being operated by the Taiwan Earthquake Research Center (TEC). With these newly developed platforms and materials, we aim to not only raise earthquake awareness and preparedness, but also encourage public participation in earthquake science in Taiwan.
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Skevakis, Giannis, Chrisa Tsinaraki, Ioanna Trochatou, and Stavros Christodoulakis. "A crowdsourcing framework for the management of mobile multimedia nature observations." International Journal of Pervasive Computing and Communications 10, no. 3 (August 26, 2014): 216–38. http://dx.doi.org/10.1108/ijpcc-06-2014-0038.
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Purpose – This paper aims to describe MoM-NOCS, a Framework and a System that support communities with common interests in nature to capture and share multimedia observations of nature objects or events using mobile devices. Design/methodology/approach – The observations are automatically associated with contextual metadata that allow them to be visualized on top of 2D or 3D maps. The observations are managed by a multimedia management system, and annotated by the same and/or other users with common interests. Annotations made by the crowd support the knowledge distillation of the data and data provenance processes in the system. Findings – MoM-NOCS is complementary and interoperable with systems that are managed by natural history museums like MMAT (Makris et al., 2013) and biodiversity metadata management systems like BIOCASE (BioCASE) and GBIF (GBIF) so that they can link to interesting observations in the system, and the statistics of the observations that they manage can be visualized by the software. Originality/value – The Framework offers rich functionality for visualizing the observations made by the crowd as function of time.
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Dortheimer, Jonathan. "Collective Intelligence in Design Crowdsourcing." Mathematics 10, no. 4 (February 9, 2022): 539. http://dx.doi.org/10.3390/math10040539.
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This study investigates how collective intelligence emerges in crowdsourcing for architectural design. Previous studies have revealed that collective intelligence emerges from collaboration and can outperform individual intelligence. As design is a highly collaborative practice, collective intelligence plays a vital role in the design process. In this study, we compare the structure of two architectural design crowdsourcing systems using several methods. The results of the analysis suggest that design crowdsourcing systems can give rise to the following three types of collective intelligence: (1) discussive, which emerges from an conversation between designers and clients; (2) synthetic, which emerges from a parallel and sequential design development; and (3) evaluative, which is based on the wisdom of the crowd in evaluating and selecting designs. The article concludes with recommendations for collaborative design method.
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Nishal, Sachita, and Nicholas Diakopoulos. "From Crowd Ratings to Predictive Models of Newsworthiness to Support Science Journalism." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (November 7, 2022): 1–28. http://dx.doi.org/10.1145/3555542.
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The scale of scientific publishing continues to grow, creating overload on science journalists who are inundated with choices for what would be most interesting, important, and newsworthy to cover in their reporting. Our work addresses this problem by considering the viability of creating a predictive model of newsworthiness of scientific articles that is trained using crowdsourced evaluations of newsworthiness. We proceed by first evaluating the potential of crowd-sourced evaluations of newsworthiness by assessing their alignment with expert ratings of newsworthiness, analyzing both quantitative correlations and qualitative rating rationale to understand limitations. We then demonstrate and evaluate a predictive model trained on these crowd ratings together with arXiv article metadata, text, and other computed features. Based on the crowdsourcing protocol we developed, we find that while crowdsourced ratings of newsworthiness often align moderately with expert ratings, there are also notable differences and divergences which limit the approach. Yet despite these limitations we also find that the predictive model we built provides a reasonably precise set of rankings when validated against expert evaluations (P@10 = 0.8, P@15 = 0.67), suggesting that a viable signal can be learned from crowdsourced evaluations of newsworthiness. Based on these findings we discuss opportunities for future work to leverage crowdsourcing and predictive approaches to support journalistic work in discovering and filtering newsworthy information.
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Tsoukalas, Gerry, and Brett Hemenway Falk. "Token-Weighted Crowdsourcing." Management Science 66, no. 9 (September 2020): 3843–59. http://dx.doi.org/10.1287/mnsc.2019.3515.
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Blockchain-based platforms often rely on token-weighted voting (“τ-weighting”) to efficiently crowdsource information from their users for a wide range of applications, including content curation and on-chain governance. We examine the effectiveness of such decentralized platforms for harnessing the wisdom and effort of the crowd. We find that τ-weighting generally discourages truthful voting and erodes the platform’s predictive power unless users are “strategic enough” to unravel the underlying aggregation mechanism. Platform accuracy decreases with the number of truthful users and the dispersion in their token holdings, and in many cases, platforms would be better off with a “flat” 1/n mechanism. When, prior to voting, strategic users can exert effort to endogenously improve their signals, users with more tokens generally exert more effort—a feature often touted in marketing materials as a core advantage of τ-weighting—however, this feature is not attributable to the mechanism itself, and more importantly, the ensuing equilibrium fails to achieve the first-best accuracy of a centralized platform. The optimality gap decreases as the distribution of tokens across users approaches a theoretical optimum, which we derive, but tends to increase with the dispersion in users’ token holdings. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.
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Jiang, Weijin, Xiaoliang Liu, Dejia Shi, Junpeng Chen, Yongxia Sun, and Liang Guo. "Research on Crowdsourcing Price Game Model in Crowd Sensing." Computers, Materials & Continua 68, no. 2 (2021): 1769–84. http://dx.doi.org/10.32604/cmc.2021.016609.
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Zhu, Xuan, Jian An, Maishun Yang, Lele Xiang, Qiangwei Yang, and Xiaolin Gui. "A Fair Incentive Mechanism for Crowdsourcing in Crowd Sensing." IEEE Internet of Things Journal 3, no. 6 (December 2016): 1364–72. http://dx.doi.org/10.1109/jiot.2016.2600634.
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Greenhill, Anita, Kate Holmes, Jamie Woodcock, Chris Lintott, Brooke D. Simmons, Gary Graham, Joe Cox, Eun Young Oh, and Karen Masters. "Playing with science." Aslib Journal of Information Management 68, no. 3 (May 16, 2016): 306–25. http://dx.doi.org/10.1108/ajim-11-2015-0182.
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Purpose – The purpose of this paper is to examine intrinsic forms of motivation and particular incidents of play, socialisation, fun and amusement on an online crowdsourced citizen science platform. The paper also investigates gamised activity (Greenhill et al., 2014) as a form of intrinsic motivation adding a sense of play to work and tasks (Xu et al., 2012). These concepts are explored through close scrutiny of the online citizen science platform Zooniverse.org. Design/methodology/approach – Qualitative techniques with an interpretivist approach are used to analyse online content found within citizen science platforms, related forums and social media by examining incidents of play, socialisation, fun and amusement to investigate how these aspects are applied as a form of user motivation. Findings – The authors find that when users classify crowdsourced tasks voluntarily it does not matter how users are classifying as long as it is accurately. However, what does matter is why they are doing it particularly because of the complex processes that builds relationships between users and the platform. The authors present a conceptual model to enable deeper understandings of how forms of social interaction and play are motivating users contributing to citizen science project to participate in the online processes. Practical implications – The findings of this paper provide practical implications for how citizen science, and also other crowdsourcing platforms, can engage with notions of play and gamification to motivate participation. Originality/value – Using detailed examples of online content, the authors reveal how participants of the Zooniverse.org demonstrate aspects of “gamised” behaviour. The authors argue that the exploration of gaming as well as play provides evidence that contributing to citizen science projects can be both utilitarian and hedonic.