Enhancing Topic Detection in Twitter Using the Crowdsourcing Process
Bibliographic record
Abstract
A decade ago, the crowdsourcing term was first coined and used to represent a method for expressing the wisdom of the crowd in accomplishing tasks that need human intelligence rather than machines and can be more efficiently accomplished time and financial wise using the crowd rather than indoor experts. This crowdsourcing process mainly contains four modules: designing incentives, then collecting, aggregating and verifying received information. The expert discovery module can be added to reduce the cost and enhance reliability and accuracy. The crowdsourcing process is used in this work to harness the mental ability of reliable Internet users around the globe and to improve the knowledge discovery techniques over social media; especially Twitter. The main objective is to improve the quality of the Twitter Exemplar-based topic detection system. The feedback from the crowd is utilized to adjust weights of the cosine similarity function deployed in the Exemplar-based topic detection algorithm. Testing the system using the Football Association Cup (FA Cup) dataset, it is found that the crowdsourcing has achieved a constant increase in the topic recall (by up to 15%), term precision (by up to 4%) and term recall (by up to 3%). Therefore, the new weights succeeded in increasing the three measures of topic quality significantly.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".