Crowdsourcing

About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.

Learning to fail: experiencing public failure online through crowdfunding

Abstract: Online crowdfunding platforms like Kickstarter are gaining attention among novice creatives as an effective platform for funding their ventures and engaging in creative work with others. However, a focus on financial success of crowdfunding has obscured the fact that over 58% of crowdfunding projects fail to achieve their funding goals. This population of failed creatives however, gives us an audience to study public creative failure in an online environment. We draw inspiration from work in organizational behavior on failure, and work in Human Computer Interaction (HCI) on online behavior, to study online public failure. Using a mixed-methods approach with data scraped from Kickstarter and interview data with failed crowdfunding project creators, we answer the following question: What do project creators on crowdfunding platforms learn and change through the process of failing? We find that creators who relaunch their projects succeed 43% of the time, and that most individuals find failure to be a positive experience. We conclude the paper with a series of design implications for future creative platforms where public failure is part of the creative process.

Crowdsourcing in health and medical research: a systematic review

Abstract: Crowdsourcing is used increasingly in health and medical research. Crowdsourcing is the process of aggregating crowd wisdom to solve a problem. The purpose of this systematic review is to summarize quantitative evidence on crowdsourcing to improve health. We followed Cochrane systematic review guidance and systematically searched seven databases up to September 4th 2019. Studies were included if they reported on crowdsourcing and related to health or medicine. Studies were excluded if recruitment was the only use of crowdsourcing. We determined the level of evidence associated with review findings using the GRADE approach. We screened 3508 citations, accessed 362 articles, and included 188 studies. Ninety-six studies examined effectiveness, 127 examined feasibility, and 37 examined cost. The most common purposes were to evaluate surgical skills (17 studies), to create sexual health messages (seven studies), and to provide layperson cardio-pulmonary resuscitation (CPR) out-of-hospital (six studies). Seventeen observational studies used crowdsourcing to evaluate surgical skills, finding that crowdsourcing evaluation was as effective as expert evaluation (low quality). Four studies used a challenge contest to solicit human immunodeficiency virus (HIV) testing promotion materials and increase HIV testing rates (moderate quality), and two of the four studies found this approach saved money. Three studies suggested that an interactive technology system increased rates of layperson initiated CPR out-of-hospital (moderate quality). However, studies analyzing crowdsourcing to evaluate surgical skills and layperson-initiated CPR were only from high-income countries. Five studies examined crowdsourcing to inform artificial intelligence projects, most often related to annotation of medical data. Crowdsourcing was evaluated using different outcomes, limiting the extent to which studies could be pooled. Crowdsourcing has been used to improve health in many settings. Although crowdsourcing is effective at improving behavioral outcomes, more research is needed to understand effects on clinical outcomes and costs. More research is needed on crowdsourcing as a tool to develop artificial intelligence systems in medicine. PROSPERO: CRD42017052835. December 27, 2016.

CrowdTruth: Machine-Human Computation Framework for Harnessing Disagreement in Gathering Annotated Data

Abstract: In this paper we introduce the CrowdTruth open-source software framework for machine-human computation, that implements a novel approach to gathering human annotation data for a variety of media (e.g. text, image, video). The CrowdTruth approach embodied in the software captures human semantics through a pipeline of four processes: a) combining various machine processing of media in order to better understand the input content and optimize its suitability for micro-tasks, thus optimize the time and cost of the crowdsourcing process; b) providing reusable human-computing task templates to collect the maximum diversity in the human interpretation, thus collect richer human semantics; c) implementing 'disagreement metrics', i.e. CrowdTruth metrics, to support deep analysis of the quality and semantics of the crowdsourcing data; and d) providing an interface to support data and results visualization. Instead of the traditional inter-annotator agreement, we use their disagreement as a useful signal to evaluate the data quality, ambiguity and vagueness. We demonstrate the applicability and robustness of this approach to a variety of problems across multiple domains. Moreover, we show the advantages of using open standards and the extensibility of the framework with new data modalities and annotation tasks.

Human-in-the-loop data integration

Abstract: Data integration aims to integrate data in different sources and provide users with a unified view. However, data integration cannot be completely addressed by purely automated methods. We propose a hybrid human-machine data integration framework that harnesses human ability to address this problem, and apply it initially to the problem of entity matching. The framework first uses rule-based algorithms to identify possible matching pairs and then utilizes the crowd to refine these candidate pairs in order to compute actual matching pairs. In the first step, we propose similarity-based rules and knowledge-based rules to obtain some candidate matching pairs, and develop effective algorithms to learn these rules based on some given positive and negative examples. We build a distributed in-memory system DIMA to efficiently apply these rules. In the second step, we propose a selection-inference-refine framework that uses the crowd to verify the candidate pairs. We first select some "beneficial" tasks to ask the crowd and then use transitivity and partial order to infer the answers of unasked tasks based on the crowdsourcing results of the asked tasks. Next we refine the inferred answers with high uncertainty due to the disagreement from the crowd. We develop a crowd-powered database system CDB and deploy it on real crowdsourcing platforms. CDB allows users to utilize a SQL-like language for processing crowd-based queries. Lastly, we provide emerging challenges in human-in-the-loop data integration.

Reputation-based Worker Filtering in Crowdsourcing

Abstract: In this paper, we study the problem of aggregating noisy labels from crowd workers to infer the underlying true labels of binary tasks. Unlike most prior work which has examined this problem under the random worker paradigm, we consider a much broader class of adversarial workers with no specific assumptions on their labeling strategy. Our key contribution is the design of a computationally efficient reputation algorithm to identify and filter out these adversarial workers in crowd-sourcing systems. Our algorithm uses the concept of optimal semi-matchings in conjunction with worker penalties based on label disagreements, to assign a reputation score for every worker. We provide strong theoretical guarantees for deterministic adversarial strategies as well as the extreme case of sophisticated adversaries where we analyze the worst-case behavior of our algorithm. Finally, we show that our reputation algorithm can significantly improve the accuracy of existing label aggregation algorithms in real-world crowdsourcing datasets.