Crowdsourcing Analysis of Twitter Data on Climate Change: Paid Workers vs. Volunteers
Bibliographic record
Abstract
Web based crowdsourcing has become an important method of environmental data processing. Two alternatives are widely used today by researchers in various fields: paid data processing mediated by for-profit businesses such as Amazon’s Mechanical Turk, and volunteer data processing conducted by amateur citizen-scientists. While the first option delivers results much faster, it is not quite clear how it compares with volunteer processing in terms of quality. This study compares volunteer and paid processing of social media data originating from climate change discussions on Twitter. The same sample of Twitter messages discussing climate change was offered for processing to the volunteer workers through the Climate Tweet project, and to the paid workers through the Amazon MTurk platform. We found that paid crowdsourcing required the employment of a high redundancy data processing design to obtain quality that was comparable with volunteered processing. Among the methods applied to improve data processing accuracy, limiting the geographical locations of the paid workers appeared the most productive. Conversely, we did not find significant geographical differences in the accuracy of data processed by volunteer workers. We suggest that the main driver of the found pattern is the differences in familiarity of the paid workers with the research topic.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".