Analytics of big geosocial media and crowdsourced data
Bibliographic record
Abstract
<p>[Introduction]: "Numerous crowdsourcing and social media platforms such as CrowdSpring, Idea Bounty, DesignCrowd, Facebook, Twitter, Flickr, Weibo, WeChat, and Instagram are creating and sharing vast amounts of user-generated content that can reveal timely and useful information for detecting traffic patterns, mitigating security risks and other types of time-critical events, discovering social structures characteristics, predicting human movement, etc. Crowdsourcing, also known as volunteered geographic information (VGI), has added a new dimension to traditional geospatial data acquisition by providing fine-grained proxy data for human activity research in urban studies (Chen et al., <a href="https://www-tandfonline-com.ezproxy.lib.ryerson.ca/doi/full/10.1080/20964471.2021.1898780#" target="_blank">2016</a>; Niu & Silva, <a href="https://www-tandfonline-com.ezproxy.lib.ryerson.ca/doi/full/10.1080/20964471.2021.1898780#" target="_blank">2020</a>). However, analyzing big geosocial media and crowdsourced data brings significant methodological and theoretical challenges due to the uncertain user representability when referring to human behavior in general, the inherent noisy data that requires high-performance cost of preprocessing, and the heterogeneity in quality and quantity of sources. In particular, geosocial media data and their derived metrics can provide valuable insights and policy strategies, but they require a deep understanding of what the metrics actually measure (Zook, <a href="https://www-tandfonline-com.ezproxy.lib.ryerson.ca/doi/full/10.1080/20964471.2021.1898780#" target="_blank">2017</a>). All of these underpin complex assessments, not mentioning the ethnic and privacy issues. Therefore, new sets of methods and tools are required to analyze the big data from crowdsourcing and social media platforms."</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".