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Determining the accuracy of crowdsourced tweet verification for auroral research

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Determining the accuracy of crowdsourced tweet verification for auroral research. / Case, Nathan Anthony; MacDonald, Elizabeth A.; McCloat, Sean; Lalone, Nicolas; Tapia, Andrea.

In: Citizen Science: Theory and Practice, Vol. 2016, 21.12.2016.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Case, NA, MacDonald, EA, McCloat, S, Lalone, N & Tapia, A 2016, 'Determining the accuracy of crowdsourced tweet verification for auroral research', Citizen Science: Theory and Practice, vol. 2016. https://doi.org/10.5334/cstp.52

APA

Case, N. A., MacDonald, E. A., McCloat, S., Lalone, N., & Tapia, A. (2016). Determining the accuracy of crowdsourced tweet verification for auroral research. Citizen Science: Theory and Practice, 2016. https://doi.org/10.5334/cstp.52

Vancouver

Case NA, MacDonald EA, McCloat S, Lalone N, Tapia A. Determining the accuracy of crowdsourced tweet verification for auroral research. Citizen Science: Theory and Practice. 2016 Dec 21;2016. https://doi.org/10.5334/cstp.52

Author

Case, Nathan Anthony ; MacDonald, Elizabeth A. ; McCloat, Sean ; Lalone, Nicolas ; Tapia, Andrea. / Determining the accuracy of crowdsourced tweet verification for auroral research. In: Citizen Science: Theory and Practice. 2016 ; Vol. 2016.

Bibtex

@article{fe83a5dc476d49d698cd81034d7080c3,
title = "Determining the accuracy of crowdsourced tweet verification for auroral research",
abstract = "The Aurorasaurus citizen science project harnesses volunteer crowdsourcing to identify sightings of an aurora (or the {"}northern/southern lights{"}) posted by citizen scientists on Twitter. Previous studies have demonstrated that aurora sightings can be mined from Twitter but with the caveat that there is a high level of accompanying non-sighting tweets, especially during periods of low auroral activity. Aurorasaurus attempts to mitigate this, and thus increase the quality of its Twitter sighting data, by utilizing volunteers to sift through a pre-filtered list of geo-located tweets to verify real-time aurora sightings. In this study, the current implementation of this crowdsourced verification system, including the process of geo-locating tweets, is described and its accuracy(which, overall, is found to be 68.4%) is determined. The findings suggest that citizen science volunteers are able to accurately filter out unrelated, spam-like, Twitter data but struggle when filtering out somewhat related, yet undesired, data. The citizen scientists particularly struggle with determining the real-time nature of the sightings and care must therefore be taken when relying on crowdsourced identification.",
keywords = "twitter, crowdsourcing, aurora, sightings, citizen science",
author = "Case, {Nathan Anthony} and MacDonald, {Elizabeth A.} and Sean McCloat and Nicolas Lalone and Andrea Tapia",
year = "2016",
month = dec,
day = "21",
doi = "10.5334/cstp.52",
language = "English",
volume = "2016",
journal = "Citizen Science: Theory and Practice",
issn = "2057-4991",

}

RIS

TY - JOUR

T1 - Determining the accuracy of crowdsourced tweet verification for auroral research

AU - Case, Nathan Anthony

AU - MacDonald, Elizabeth A.

AU - McCloat, Sean

AU - Lalone, Nicolas

AU - Tapia, Andrea

PY - 2016/12/21

Y1 - 2016/12/21

N2 - The Aurorasaurus citizen science project harnesses volunteer crowdsourcing to identify sightings of an aurora (or the "northern/southern lights") posted by citizen scientists on Twitter. Previous studies have demonstrated that aurora sightings can be mined from Twitter but with the caveat that there is a high level of accompanying non-sighting tweets, especially during periods of low auroral activity. Aurorasaurus attempts to mitigate this, and thus increase the quality of its Twitter sighting data, by utilizing volunteers to sift through a pre-filtered list of geo-located tweets to verify real-time aurora sightings. In this study, the current implementation of this crowdsourced verification system, including the process of geo-locating tweets, is described and its accuracy(which, overall, is found to be 68.4%) is determined. The findings suggest that citizen science volunteers are able to accurately filter out unrelated, spam-like, Twitter data but struggle when filtering out somewhat related, yet undesired, data. The citizen scientists particularly struggle with determining the real-time nature of the sightings and care must therefore be taken when relying on crowdsourced identification.

AB - The Aurorasaurus citizen science project harnesses volunteer crowdsourcing to identify sightings of an aurora (or the "northern/southern lights") posted by citizen scientists on Twitter. Previous studies have demonstrated that aurora sightings can be mined from Twitter but with the caveat that there is a high level of accompanying non-sighting tweets, especially during periods of low auroral activity. Aurorasaurus attempts to mitigate this, and thus increase the quality of its Twitter sighting data, by utilizing volunteers to sift through a pre-filtered list of geo-located tweets to verify real-time aurora sightings. In this study, the current implementation of this crowdsourced verification system, including the process of geo-locating tweets, is described and its accuracy(which, overall, is found to be 68.4%) is determined. The findings suggest that citizen science volunteers are able to accurately filter out unrelated, spam-like, Twitter data but struggle when filtering out somewhat related, yet undesired, data. The citizen scientists particularly struggle with determining the real-time nature of the sightings and care must therefore be taken when relying on crowdsourced identification.

KW - twitter

KW - crowdsourcing

KW - aurora

KW - sightings

KW - citizen science

U2 - 10.5334/cstp.52

DO - 10.5334/cstp.52

M3 - Journal article

VL - 2016

JO - Citizen Science: Theory and Practice

JF - Citizen Science: Theory and Practice

SN - 2057-4991

ER -