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Crowd Knowledge Creation Acceleration. / Yang, Jie.

2017. 293 p.

Research output: ThesisDissertation (TU Delft)

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@phdthesis{ed22a51a34694699836d19322b9537c9,
title = "Crowd Knowledge Creation Acceleration",
abstract = "Crowd knowledge creation plays a central role in many types of Web based information systems, ranging from community question-answering (CQA) systems (e.g. StackOverflow and Quora) to micro-task crowdsourcing systems (e.g. Amazon mTurk and CrowdFlower). In these systems, knowledge demands are generally fulfilled by means of tasks (e.g. questions in CQA systems, micro-tasks in crowdsourcing systems) executed by group of individuals (e.g. contributors in CQA systems, workers in crowdsourcing systems). Despite of the success in some platforms, knowledge creation tasks so far are assumed to be of low cognitive complexity and are generally solved as a bottom-up process; as a consequence, outcomes are heavily dependent on the spontaneous and autonomous contribution of crowds. This limits our ability to control the volume, speed, and quality of knowledge creation. By unlocking the value of features related to human knowledge, e.g. expertise and motivation, we envision that crowd knowledge creation can reach its full potential where complex, cognitively intensive tasks are solved and thus high-quality knowledge is efficiently generated...",
keywords = "Knowledge Creation, Acceleration, Human Computation, Crowd-sourcing, Recommender Systems, User Modeling",
author = "Jie Yang",
note = "SIKS Dissertation Series No. 2017-47",
year = "2017",
doi = "10.4233/uuid:ed22a51a-3469-4699-836d-19322b9537c9",
language = "English",
isbn = "978-94-6186-865-7",
school = "Delft University of Technology",

}

RIS

TY - THES

T1 - Crowd Knowledge Creation Acceleration

AU - Yang, Jie

N1 - SIKS Dissertation Series No. 2017-47

PY - 2017

Y1 - 2017

N2 - Crowd knowledge creation plays a central role in many types of Web based information systems, ranging from community question-answering (CQA) systems (e.g. StackOverflow and Quora) to micro-task crowdsourcing systems (e.g. Amazon mTurk and CrowdFlower). In these systems, knowledge demands are generally fulfilled by means of tasks (e.g. questions in CQA systems, micro-tasks in crowdsourcing systems) executed by group of individuals (e.g. contributors in CQA systems, workers in crowdsourcing systems). Despite of the success in some platforms, knowledge creation tasks so far are assumed to be of low cognitive complexity and are generally solved as a bottom-up process; as a consequence, outcomes are heavily dependent on the spontaneous and autonomous contribution of crowds. This limits our ability to control the volume, speed, and quality of knowledge creation. By unlocking the value of features related to human knowledge, e.g. expertise and motivation, we envision that crowd knowledge creation can reach its full potential where complex, cognitively intensive tasks are solved and thus high-quality knowledge is efficiently generated...

AB - Crowd knowledge creation plays a central role in many types of Web based information systems, ranging from community question-answering (CQA) systems (e.g. StackOverflow and Quora) to micro-task crowdsourcing systems (e.g. Amazon mTurk and CrowdFlower). In these systems, knowledge demands are generally fulfilled by means of tasks (e.g. questions in CQA systems, micro-tasks in crowdsourcing systems) executed by group of individuals (e.g. contributors in CQA systems, workers in crowdsourcing systems). Despite of the success in some platforms, knowledge creation tasks so far are assumed to be of low cognitive complexity and are generally solved as a bottom-up process; as a consequence, outcomes are heavily dependent on the spontaneous and autonomous contribution of crowds. This limits our ability to control the volume, speed, and quality of knowledge creation. By unlocking the value of features related to human knowledge, e.g. expertise and motivation, we envision that crowd knowledge creation can reach its full potential where complex, cognitively intensive tasks are solved and thus high-quality knowledge is efficiently generated...

KW - Knowledge Creation

KW - Acceleration

KW - Human Computation

KW - Crowd-sourcing

KW - Recommender Systems

KW - User Modeling

UR - http://resolver.tudelft.nl/uuid:ed22a51a-3469-4699-836d-19322b9537c9

U2 - 10.4233/uuid:ed22a51a-3469-4699-836d-19322b9537c9

DO - 10.4233/uuid:ed22a51a-3469-4699-836d-19322b9537c9

M3 - Dissertation (TU Delft)

SN - 978-94-6186-865-7

ER -

ID: 31115214