Topic
Crowdsourcing
About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.
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01 Mar 2013
TL;DR: A faceted analysis of crowdsourcing from a practitioner’s perspective is provided, and how the major crowdsourcing genres fill different parts of this multi-dimensional space is summarized.
Abstract: Crowdsourcing has emerged as a new method for obtaining annotations for training models for machine learning. While many variants of this process exist, they largely differ in their methods of motivating subjects to contribute and the scale of their applications. To date, there has yet to be a study that helps the practitioner to decide what form an annotation application should take to best reach its objectives within the constraints of a project. To fill this gap, we provide a faceted analysis of crowdsourcing from a practitioner's perspective, and show how our facets apply to existing published crowdsourced annotation applications. We then summarize how the major crowdsourcing genres fill different parts of this multi-dimensional space, which leads to our recommendations on the potential opportunities crowdsourcing offers to future annotation efforts.
140 citations
TL;DR: A novel multi-armed bandit (MAB) model, the bounded MAB is introduced, and an algorithm to solve it efficiently, called bounded e-first, is developed, which outperforms existing crowdsourcing methods by up to 300%, while achieving up to 95% of a hypothetical optimum with full information.
140 citations
02 Jul 2018
TL;DR: The first private and anonymous decentralized crowdsourcing system ZebraLancer is designed and implemented, and the outsource-then-prove methodology resolves the tension between blockchain transparency and data confidentiality, which is critical in crowdsourcing use-case.
Abstract: We design and implement the first private and anonymous decentralized crowdsourcing system ZebraLancer, and overcome two fundamental challenges of decentralizing crowdsourcing, i.e. data leakage and identity breach. First, our outsource-then-prove methodology resolves the tension between blockchain transparency and data confidentiality, which is critical in crowdsourcing use-case. ZebraLancer ensures: (i) a requester will not pay more than what data deserve, according to a policy announced when her task is published via the blockchain; (ii) each worker indeed gets a payment based on the policy, if he submits data to the blockchain; (iii) the above properties are realized not only without a central arbiter, but also without leaking the data to the open blockchain. Furthermore, the transparency of blockchain allows one to infer private information about workers and requesters through their participation history. On the other hand, allowing anonymity will enable a malicious worker to submit multiple times to reap rewards. ZebraLancer overcomes this problem by allowing anonymous requests/submissions without sacrificing the accountability. The idea behind is a subtle linkability: if a worker submits twice to a task, anyone can link the submissions, or else he stays anonymous and unlinkable across tasks. To realize this delicate linkability, we put forward a novel cryptographic concept, i.e. the common-prefix-linkable anonymous authentication. We remark the new anonymous authentication scheme might be of independent interest. Finally, we implement our protocol for a common image annotation task and deploy it in a test net of Ethereum. The experiment results show the applicability of our protocol with the existing real-world blockchain.
139 citations
TL;DR: The authors analyzes successful platforms to identify patterns of effective crowdsourcing-based business models, and provides guidance for managers who need to create new (or adapt existing) business models to adapt existing business models.
Abstract: Technology has transformed individuals from mere consumers of products to empowered participants in value co-creation. While numerous firms experiment with involving a crowd in value creation, few companies turn crowdsourcing projects into thriving platforms with a powerful business model. To address this challenge, this article analyzes successful platforms to identify patterns of effective crowdsourcing-based business models. The results provide guidance for managers who need to create new (or adapt existing) business models.
139 citations
01 Apr 2014
TL;DR: A framework is developed that makes it possible to analyze Twitter comments or “Tweets” as having positive, negative or neutral sentiments, which can be applied in a wide range of applications ranging from politics to marketing.
Abstract: Social media platforms such as blogs, social networking sites, content communities and virtual worlds are tremendously becoming one of the most powerful sources for news, markets, industries, and much more They are a wide platform full of thoughts, emotions, reviews and feedback, which can be used in many aspects Despite these great avails, and with the increasingly enormous number of Arabic users on the internet, little research has tied these two together in a high and accurate professional manner [1] This paper deals with Arabic Sentiment Analysis We developed a framework that makes it possible to analyze Twitter comments or “Tweets” as having positive, negative or neutral sentiments This can be applied in a wide range of applications ranging from politics to marketing This framework has many novel aspects such as handling Arabic dialects, Arabizi and emoticons Also, crowdsourcing was utilized to collect a large dataset of tweets
139 citations