Exploring crowdsourcing accountability for mapping Antarctica: a case study using 5 years of social media data


Creative Commons License

Gülnerman Gengeç A. G.

Turkish Journal of Earth Sciences, cilt.32, sa.SI-8, ss.1041-1051, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: SI-8
  • Basım Tarihi: 2023
  • Doi Numarası: 10.55730/1300-0985.1892
  • Dergi Adı: Turkish Journal of Earth Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Geobase, INSPEC, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.1041-1051
  • Anahtar Kelimeler: crowdsourcing, geo-social media, new forms of geodata, Polar monitoring
  • Ankara Hacı Bayram Veli Üniversitesi Adresli: Hayır

Özet

The continent of Antarctica is one of the most challenging regions in terms of generating and updating geodata. Extensive research has been conducted on geodata acquisition, primarily focusing on earth observation satellites and local surveys in polar regions. Earth observation satellites offer limited spatial, temporal, and semantic data, while local surveys are constrained by polar regions’ size and challenging conditions. This article delves into the crowdsourced data, aiming to significantly enhance geodata contribution in polar regions beyond current methods. To our knowledge, this study is the first of its kind to address social media data, a form of crowdsourcing, specifically for the Antarctic continent. Encompassing 5 years of social media data collection, the study uniquely pres-ents original insights by investigating data reliability based on user activity levels and user movement consistency. The primary outcome of the study is that the activity level of users negatively correlates with spatial behavior consistency. This indicates that dominant user influence has led to inconsistent content manipulation. However, while the overall rate summary indicates a high inconsistency ratio for the active group, there still exists consistent behavior within these groups. A tight method to discriminate these reliable data generators with consistent behavior should be aimed as proposed in this study to prevent valuable data loss. This study contributes to reliable data scrutiny techniques from social media data in a general sense while providing a glimpse into the spatial quality of data generated specifically for Antarctica. This glimpse will enable future assessments of data collected in Antarctica for reliability checks and offer benefits in terms of processing workload and result accuracy in preprocessing steps for text, image, and spatial-based data processing.