“Every Small Action Helps Towards the Greater Cause:” Online Communities Scaling Up Online Community-Led Citizen Science in Addressing Litter Challenges in Scotland
Social media is now a new means of engagement and a catalyst for citizen science; still, less attention has been paid to understanding the influence of online communities on community-led citizen science projects. This study used the Fife Street Champions public Facebook group as a case study to exp...
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Noleen Rutendo Chikowore [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Übergeordnetes Werk: |
In: Citizen Science: Theory and Practice - Ubiquity Press, 2018, 8(2023), 1, Seite 55-55 |
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| Übergeordnetes Werk: |
volume:8 ; year:2023 ; number:1 ; pages:55-55 |
| Links: |
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| DOI / URN: |
10.5334/cstp.579 |
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DOAJ091548187 |
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10.5334/cstp.579 doi (DE-627)DOAJ091548187 (DE-599)DOAJd13e4c1913724edf92998e91fdec1151 DE-627 ger DE-627 rakwb eng Noleen Rutendo Chikowore verfasserin aut “Every Small Action Helps Towards the Greater Cause:” Online Communities Scaling Up Online Community-Led Citizen Science in Addressing Litter Challenges in Scotland 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Social media is now a new means of engagement and a catalyst for citizen science; still, less attention has been paid to understanding the influence of online communities on community-led citizen science projects. This study used the Fife Street Champions public Facebook group as a case study to explore how online community-led citizen science projects generate citizen science data to understand littering challenges in Scotland and to examine the impact of the group’s activities and the challenges they face. Data driven-content analysis was used to analyse Facebook user-generated data of 337 posts with comments and images to identify key themes that emerge in the data. Results indicate that group members develop their own data collection tools, share, analyse and present their litter-picking activities to understand the magnitude of littering and the impact of their litter-picking activities. However, the findings highlight inconsistencies in how group members collect and record data from their litter-picking activities. The group also provides informational support, environmental awareness and advocacy, and environmental citizenship. Members also share concerns about eco-anxiety. Lastly, safety and health concerns, COVID-19, and seagulls are challenges experienced by online-based litter pickers. The results contribute to our understanding of the opportunity that social media platforms can provide to build more robust online community-driven citizen science projects that can inform further research. Key stakeholders need to collaborate with such communities to improve on collecting scientifically meaningful data. citizen science user-generated data litter picking online citizen science environmental citizenship Science Q In Citizen Science: Theory and Practice Ubiquity Press, 2018 8(2023), 1, Seite 55-55 (DE-627)1024123413 (DE-600)2932178-5 20574991 nnns volume:8 year:2023 number:1 pages:55-55 https://doi.org/10.5334/cstp.579 kostenfrei https://doaj.org/article/d13e4c1913724edf92998e91fdec1151 kostenfrei https://account.theoryandpractice.citizenscienceassociation.org/index.php/up-j-cstp/article/view/579 kostenfrei https://doaj.org/toc/2057-4991 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 8 2023 1 55-55 |
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“every small action helps towards the greater cause:” online communities scaling up online community-led citizen science in addressing litter challenges in scotland |
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“Every Small Action Helps Towards the Greater Cause:” Online Communities Scaling Up Online Community-Led Citizen Science in Addressing Litter Challenges in Scotland |
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Social media is now a new means of engagement and a catalyst for citizen science; still, less attention has been paid to understanding the influence of online communities on community-led citizen science projects. This study used the Fife Street Champions public Facebook group as a case study to explore how online community-led citizen science projects generate citizen science data to understand littering challenges in Scotland and to examine the impact of the group’s activities and the challenges they face. Data driven-content analysis was used to analyse Facebook user-generated data of 337 posts with comments and images to identify key themes that emerge in the data. Results indicate that group members develop their own data collection tools, share, analyse and present their litter-picking activities to understand the magnitude of littering and the impact of their litter-picking activities. However, the findings highlight inconsistencies in how group members collect and record data from their litter-picking activities. The group also provides informational support, environmental awareness and advocacy, and environmental citizenship. Members also share concerns about eco-anxiety. Lastly, safety and health concerns, COVID-19, and seagulls are challenges experienced by online-based litter pickers. The results contribute to our understanding of the opportunity that social media platforms can provide to build more robust online community-driven citizen science projects that can inform further research. Key stakeholders need to collaborate with such communities to improve on collecting scientifically meaningful data. |
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Social media is now a new means of engagement and a catalyst for citizen science; still, less attention has been paid to understanding the influence of online communities on community-led citizen science projects. This study used the Fife Street Champions public Facebook group as a case study to explore how online community-led citizen science projects generate citizen science data to understand littering challenges in Scotland and to examine the impact of the group’s activities and the challenges they face. Data driven-content analysis was used to analyse Facebook user-generated data of 337 posts with comments and images to identify key themes that emerge in the data. Results indicate that group members develop their own data collection tools, share, analyse and present their litter-picking activities to understand the magnitude of littering and the impact of their litter-picking activities. However, the findings highlight inconsistencies in how group members collect and record data from their litter-picking activities. The group also provides informational support, environmental awareness and advocacy, and environmental citizenship. Members also share concerns about eco-anxiety. Lastly, safety and health concerns, COVID-19, and seagulls are challenges experienced by online-based litter pickers. The results contribute to our understanding of the opportunity that social media platforms can provide to build more robust online community-driven citizen science projects that can inform further research. Key stakeholders need to collaborate with such communities to improve on collecting scientifically meaningful data. |
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Social media is now a new means of engagement and a catalyst for citizen science; still, less attention has been paid to understanding the influence of online communities on community-led citizen science projects. This study used the Fife Street Champions public Facebook group as a case study to explore how online community-led citizen science projects generate citizen science data to understand littering challenges in Scotland and to examine the impact of the group’s activities and the challenges they face. Data driven-content analysis was used to analyse Facebook user-generated data of 337 posts with comments and images to identify key themes that emerge in the data. Results indicate that group members develop their own data collection tools, share, analyse and present their litter-picking activities to understand the magnitude of littering and the impact of their litter-picking activities. However, the findings highlight inconsistencies in how group members collect and record data from their litter-picking activities. The group also provides informational support, environmental awareness and advocacy, and environmental citizenship. Members also share concerns about eco-anxiety. Lastly, safety and health concerns, COVID-19, and seagulls are challenges experienced by online-based litter pickers. The results contribute to our understanding of the opportunity that social media platforms can provide to build more robust online community-driven citizen science projects that can inform further research. Key stakeholders need to collaborate with such communities to improve on collecting scientifically meaningful data. |
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