Topic
Crowdsourcing
About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.
Papers published on a yearly basis
Papers
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TL;DR: A privacy-preserving task recommendation scheme (PPTR) for crowdsourcing is proposed, which achieves the task-worker matching while preserving both task privacy and worker privacy.
Abstract: Crowdsourcing is a distributed computing paradigm that utilizes human intelligence or resources from a crowd of workers. Existing solutions of task recommendation in crowdsourcing may leak private and sensitive information about both tasks and workers. To protect privacy, information about tasks and workers should be encrypted before being outsourced to the crowdsourcing platform, which makes the task recommendation a challenging problem. In this paper, we propose a privacy-preserving task recommendation scheme (PPTR) for crowdsourcing, which achieves the task-worker matching while preserving both task privacy and worker privacy. In PPTR, we first exploit the polynomial function to express multiple keywords of task requirements and worker interests. Then, we design a key derivation method based on matrix decomposition, to realize the multi-keyword matching between multiple requesters and multiple workers. Through PPTR, user accountability and user revocation are achieved effectively and efficiently. Extensive privacy analysis and performance evaluation show that PPTR is secure and efficient.
121 citations
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17 Sep 2013
TL;DR: This paper analyses the impact of two built-in Q&A and forum and three external social tools Facebook, Twitter and MentorMob in a MOOC on educational technologies and the lessons learned are summarized so that others may benefit.
Abstract: MOOCs have been a disruptive educational trend in the last months. Some MOOCs just replicate traditional teaching pedagogies, adding multimedia elements like video lectures. Others go beyond, trying to engage the massive number of participants by promoting discussions and relying on their contributions to the course. MOOC platforms usually provide some built-in social tools for this purpose, although instructors or participants may suggest others to foster discussions and crowdsourcing. This paper analyses the impact of two built-in Q&A and forum and three external social tools Facebook, Twitter and MentorMob in a MOOC on educational technologies. Most of the participants agreed on the importance of social tools to be in touch with their partners and share information related to the course, the forum being the one preferred. Furthermore, the lessons learned from the enactment of this MOOC employing social tools are summarized so that others may benefit from them.
121 citations
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TL;DR: The ExpertLens system and methodology is designed to save on the costs associated with traditional expert panels, while increasing accuracy in elicitation by reducing the potential for group process losses that can occur in large, diverse, and non-collocated panels whose members interact via asynchronous online discussion boards.
121 citations
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04 Aug 2017TL;DR: Crowdourcing is used to evaluate TrioVecEvent, a method that leverages multimodal embeddings to achieve accurate online local event detection and introduces discriminative features that can well characterize local events.
Abstract: Detecting local events (e.g., protest, disaster) at their onsets is an important task for a wide spectrum of applications, ranging from disaster control to crime monitoring and place recommendation. Recent years have witnessed growing interest in leveraging geo-tagged tweet streams for online local event detection. Nevertheless, the accuracies of existing methods still remain unsatisfactory for building reliable local event detection systems. We propose TrioVecEvent, a method that leverages multimodal embeddings to achieve accurate online local event detection. The effectiveness of TrioVecEvent is underpinned by its two-step detection scheme. First, it ensures a high coverage of the underlying local events by dividing the tweets in the query window into coherent geo-topic clusters. To generate quality geo-topic clusters, we capture short-text semantics by learning multimodal embeddings of the location, time, and text, and then perform online clustering with a novel Bayesian mixture model. Second, TrioVecEvent considers the geo-topic clusters as candidate events and extracts a set of features for classifying the candidates. Leveraging the multimodal embeddings as background knowledge, we introduce discriminative features that can well characterize local events, which enables pinpointing true local events from the candidate pool with a small amount of training data. We have used crowdsourcing to evaluate TrioVecEvent, and found that it improves the performance of the state-of-the-art method by a large margin.
120 citations
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01 Oct 2010
TL;DR: In this article, the authors used a collaborative research approach to investigate the main strategic difficulties encountered by firms whose business models rely on public web communities to create value, where the knowledge produced is the result of collective creativity carried out by communities of peers.
Abstract: Recent literature on open innovation suggests that firms can improve their performance by “opening” their business models, in other words, they can reduce
their R&D costs by incorporating external knowledge. This implies that firms will
be able to capture value through knowledge produced outside the organization. This, however, presents a number of difficulties notably where the knowledge produced is the result of collective creativity carried out by communities
of peers. Here, tension can arise when some of the business actors involved
take, or attempt to obtain, financial benefit from part of the value created by
the online communities. The purpose of this article is to address the following
research question: what are the main strategic difficulties encountered by firms
whose business models rely on public web communities to create value? Our
study used a collaborative research approach, and our empirical data is based
on the longitudinal strategic analysis of a web start-up, CrowdSpirit, a collaborative web-based platform which enables communities to imagine and design
innovative products. Our research highlights three main points that need to be
addressed in further research on open business models. First, we highlight
the fact that the ‘openness’ of the business model to online communities leads
to the development of a multi-level incentive model adapted to the different
profiles of the various contributors. Second, we suggest that crowdsourcing
platforms act as intermediaries in multi-sided markets and, as such, are at the
core of a knowledge-sharing and IP transfer process between multiple actors.
Finally, we suggest that the business model design and development can be
considered as an ongoing learning process.
120 citations