26,289 research outputs found
Competing or aiming to be average?: Normification as a means of engaging digital volunteers
Engagement, motivation and active contribution by digital volunteers are key requirements for crowdsourcing and citizen science projects. Many systems use competitive elements, for example point scoring and leaderboards, to achieve these ends. However, while competition may motivate some people, it can have a neutral or demotivating effect on others. In this paper we explore theories of personal and social norms and investigate normification as an alternative approach to engagement, to be used alongside or instead of competitive strategies. We provide a systematic review of existing crowdsourcing and citizen science literature and categorise the ways that theories of norms have been incorporated to date. We then present qualitative interview data from a pro-environmental crowdsourcing study, Close the Door, which reveals normalising attitudes in certain participants. We assess how this links with competitive behaviour and participant performance. Based on our findings and analysis of norm theories, we consider the implications for designers wishing to use normification as an engagement strategy in crowdsourcing and citizen science systems
Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation
A human computation system can be viewed as a distributed system in which the
processors are humans, called workers. Such systems harness the cognitive power
of a group of workers connected to the Internet to execute relatively simple
tasks, whose solutions, once grouped, solve a problem that systems equipped
with only machines could not solve satisfactorily. Examples of such systems are
Amazon Mechanical Turk and the Zooniverse platform. A human computation
application comprises a group of tasks, each of them can be performed by one
worker. Tasks might have dependencies among each other. In this study, we
propose a theoretical framework to analyze such type of application from a
distributed systems point of view. Our framework is established on three
dimensions that represent different perspectives in which human computation
applications can be approached: quality-of-service requirements, design and
management strategies, and human aspects. By using this framework, we review
human computation in the perspective of programmers seeking to improve the
design of human computation applications and managers seeking to increase the
effectiveness of human computation infrastructures in running such
applications. In doing so, besides integrating and organizing what has been
done in this direction, we also put into perspective the fact that the human
aspects of the workers in such systems introduce new challenges in terms of,
for example, task assignment, dependency management, and fault prevention and
tolerance. We discuss how they are related to distributed systems and other
areas of knowledge.Comment: 3 figures, 1 tabl
A data-driven game theoretic strategy for developers in software crowdsourcing: a case study
Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward
Resilient seed systems for climate change adaptation and sustainable livelihoods in the East Africa sub-region: Report of training workshop, Addis Ababa Ethiopia, 17-21 September 2019
Bioversity International is implementing a Dutch-supported project entitled: Resilient seed systems for climate change adaptation and sustainable livelihoods in the East Africa sub-region. This work aims to boost timely and affordable access to good-quality seed for a portfolio of crops / varieties for millions of women and men farmers’ and their communities across east Africa.
A first project training: i) contextualized farmer varietal selection, ii) provided practical demonstrations of tools for climate-change analysis, iii) introduced policy issues associated with managing crop diversity, iv) outlined characterization and evaluation of genetic resources, and v) articulated associated gender issues, and issues related to disseminating elite materials. The training concluded with a contextualizing field trip.
In the workshop evaluation, 98% participants declared their overall satisfaction level to be high (74%) or medium (24%), indicating the training furnished them with good ideas for networking and using the tools and methods they learned about
Optimization in Knowledge-Intensive Crowdsourcing
We present SmartCrowd, a framework for optimizing collaborative
knowledge-intensive crowdsourcing. SmartCrowd distinguishes itself by
accounting for human factors in the process of assigning tasks to workers.
Human factors designate workers' expertise in different skills, their expected
minimum wage, and their availability. In SmartCrowd, we formulate task
assignment as an optimization problem, and rely on pre-indexing workers and
maintaining the indexes adaptively, in such a way that the task assignment
process gets optimized both qualitatively, and computation time-wise. We
present rigorous theoretical analyses of the optimization problem and propose
optimal and approximation algorithms. We finally perform extensive performance
and quality experiments using real and synthetic data to demonstrate that
adaptive indexing in SmartCrowd is necessary to achieve efficient high quality
task assignment.Comment: 12 page
Social Turing Tests: Crowdsourcing Sybil Detection
As popular tools for spreading spam and malware, Sybils (or fake accounts)
pose a serious threat to online communities such as Online Social Networks
(OSNs). Today, sophisticated attackers are creating realistic Sybils that
effectively befriend legitimate users, rendering most automated Sybil detection
techniques ineffective. In this paper, we explore the feasibility of a
crowdsourced Sybil detection system for OSNs. We conduct a large user study on
the ability of humans to detect today's Sybil accounts, using a large corpus of
ground-truth Sybil accounts from the Facebook and Renren networks. We analyze
detection accuracy by both "experts" and "turkers" under a variety of
conditions, and find that while turkers vary significantly in their
effectiveness, experts consistently produce near-optimal results. We use these
results to drive the design of a multi-tier crowdsourcing Sybil detection
system. Using our user study data, we show that this system is scalable, and
can be highly effective either as a standalone system or as a complementary
technique to current tools
- …