7,899 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
Crowdsourcing Without a Crowd: Reliable Online Species Identification Using Bayesian Models to Minimize Crowd Size
We present an incremental Bayesian model that resolves key issues of crowd size and data quality for consensus labeling. We evaluate our method using data collected from a real-world citizen science program, BeeWatch, which invites members of the public in the United Kingdom to classify (label) photographs of bumblebees as one of 22 possible species. The biological recording domain poses two key and hitherto unaddressed challenges for consensus models of crowdsourcing: (1) the large number of potential species makes classification difficult, and (2) this is compounded by limited crowd availability, stemming from both the inherent difficulty of the task and the lack of relevant skills among the general public. We demonstrate that consensus labels can be reliably found in such circumstances with very small crowd sizes of around three to five users (i.e., through group sourcing). Our incremental Bayesian model, which minimizes crowd size by re-evaluating the quality of the consensus label following each species identification solicited from the crowd, is competitive with a Bayesian approach that uses a larger but fixed crowd size and outperforms majority voting. These results have important ecological applicability: biological recording programs such as BeeWatch can sustain themselves when resources such as taxonomic experts to confirm identifications by photo submitters are scarce (as is typically the case), and feedback can be provided to submitters in a timely fashion. More generally, our model provides benefits to any crowdsourced consensus labeling task where there is a cost (financial or otherwise) associated with soliciting a label
Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
(abridged for arXiv) With the first direct detection of gravitational waves,
the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has
initiated a new field of astronomy by providing an alternate means of sensing
the universe. The extreme sensitivity required to make such detections is
achieved through exquisite isolation of all sensitive components of LIGO from
non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to
a variety of instrumental and environmental sources of noise that contaminate
the data. Of particular concern are noise features known as glitches, which are
transient and non-Gaussian in their nature, and occur at a high enough rate so
that accidental coincidence between the two LIGO detectors is non-negligible.
In this paper we describe an innovative project that combines crowdsourcing
with machine learning to aid in the challenging task of categorizing all of the
glitches recorded by the LIGO detectors. Through the Zooniverse platform, we
engage and recruit volunteers from the public to categorize images of glitches
into pre-identified morphological classes and to discover new classes that
appear as the detectors evolve. In addition, machine learning algorithms are
used to categorize images after being trained on human-classified examples of
the morphological classes. Leveraging the strengths of both classification
methods, we create a combined method with the aim of improving the efficiency
and accuracy of each individual classifier. The resulting classification and
characterization should help LIGO scientists to identify causes of glitches and
subsequently eliminate them from the data or the detector entirely, thereby
improving the rate and accuracy of gravitational-wave observations. We
demonstrate these methods using a small subset of data from LIGO's first
observing run.Comment: 27 pages, 8 figures, 1 tabl
A Review on the Applications of Crowdsourcing in Human Pathology
The advent of the digital pathology has introduced new avenues of diagnostic
medicine. Among them, crowdsourcing has attracted researchers' attention in the
recent years, allowing them to engage thousands of untrained individuals in
research and diagnosis. While there exist several articles in this regard,
prior works have not collectively documented them. We, therefore, aim to review
the applications of crowdsourcing in human pathology in a semi-systematic
manner. We firstly, introduce a novel method to do a systematic search of the
literature. Utilizing this method, we, then, collect hundreds of articles and
screen them against a pre-defined set of criteria. Furthermore, we crowdsource
part of the screening process, to examine another potential application of
crowdsourcing. Finally, we review the selected articles and characterize the
prior uses of crowdsourcing in pathology
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