research-article

Using social media for collaborative species identification and occurrence: issues, methods, and tools

Authors:

Tyng-Ruey Chuang,

Kwang-Tsao Shao,

Cheng-Hsin Hsu,

Menno-Jan KraakAuthors Info & Claims

GEOCROWD '12: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information

Pages 22 - 29

https://doi.org/10.1145/2442952.2442957

Published: 06 November 2012 Publication History

Abstract

The emergence of social media enables people to interact with others on the web in ways that are media-rich ("updates" or "posts" can be text, photo, audio, video, etc), time-shifted (correspondence need not happen at once or within a pre-defined time frame), and social in nature. By utilizing social media, citizen science projects can potentially engage many participants to contribute their observations covering a large geographic region and over a long time period. This is an improvement, for example, over traditional biodiversity surveys which typically involve relatively few people in confined regions and periods.

As social media is not designed for scientific data collection and analysis, there is a problem in transferring unstructured information items (e.g. free-form text, unidentified images, etc.) often found in social media to structured data records for scientific tasks. To help bridge this gap, we propose an approach comprised of three steps: (1) Information Extraction, (2) Information Formalization, and (3) Information Reuse. We apply this approach to processing posts and comments from two Facebook interest groups on species observations. Our study demonstrates that with principled methods and proper tools, crowdsourced social media contents such as those from Facebook interest groups can be used for collaborative species identification and occurrence.

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Cited By

Lotfian MIngensand JBrovelli M(2021)The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data QualitySustainability10.3390/su1314808713:14(8087)Online publication date: 20-Jul-2021
https://doi.org/10.3390/su13148087
Shan PSun W(2021)Research on 3D urban landscape design and evaluation based on geographic information systemEnvironmental Earth Sciences10.1007/s12665-021-09886-y80:17Online publication date: 23-Aug-2021
https://doi.org/10.1007/s12665-021-09886-y
Lin YDeng DLin WLemmens RCrossman NHenle KSchmeller D(2015)Uncertainty analysis of crowd-sourced and professionally collected field data used in species distribution models of Taiwanese mothsBiological Conservation10.1016/j.biocon.2014.11.012181(102-110)Online publication date: Jan-2015
https://doi.org/10.1016/j.biocon.2014.11.012
Show More Cited By

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Using social media for collaborative species identification and occurrence: issues, methods, and tools

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Published In

cover image ACM Conferences

GEOCROWD '12: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information

November 2012

98 pages

ISBN:9781450316941

DOI:10.1145/2442952

Editors:
Mike Goodchild
University of Santa Barbara
,
Dieter Pfoser
Research Center "ATHENA", Greece
,
Dan Sui
Ohio State University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2012

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SIGSPATIAL'12

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SIGSPATIAL

SIGSPATIAL'12: SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems

November 6, 2012

California, Redondo Beach

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Overall Acceptance Rate 17 of 30 submissions, 57%

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Cited By

Lotfian MIngensand JBrovelli M(2021)The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data QualitySustainability10.3390/su1314808713:14(8087)Online publication date: 20-Jul-2021
https://doi.org/10.3390/su13148087
Shan PSun W(2021)Research on 3D urban landscape design and evaluation based on geographic information systemEnvironmental Earth Sciences10.1007/s12665-021-09886-y80:17Online publication date: 23-Aug-2021
https://doi.org/10.1007/s12665-021-09886-y
Lin YDeng DLin WLemmens RCrossman NHenle KSchmeller D(2015)Uncertainty analysis of crowd-sourced and professionally collected field data used in species distribution models of Taiwanese mothsBiological Conservation10.1016/j.biocon.2014.11.012181(102-110)Online publication date: Jan-2015
https://doi.org/10.1016/j.biocon.2014.11.012
Ko CFan MLin RCheng CLee P(2014)Monitoring Breeding Bird Populations in TaiwanIntegrative Observations and Assessments10.1007/978-4-431-54783-9_3(51-63)Online publication date: 6-May-2014
https://doi.org/10.1007/978-4-431-54783-9_3

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