Exploring crowdsourcing accountability for mapping Antarctica: a case study using 5 years of social media data
Bibliographic record
Abstract
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 presents 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".