TipMaster: A Knowledge Base of Authoritative Local News Sources on Social Media
Bibliographic record
Abstract
Twitter has become an important online source for real-time news dissemination. Especially, official accounts of local government and media outlets have provided newsworthy and authoritative information, revealing local trends and breaking news. In this paper, we describe TipMaster an automatically constructed knowledge base of Twitter accounts that are likely to report local news, from government agencies to local media outlets. First, we implement classifiers for detecting these accounts by integrating heterogeneous information from the accounts' textual metadata, profile images, and their tweet messages. Next, we demonstrate two use cases for TipMaster: 1) as a platform that monitors real-time social media messages for local breaking news, and 2) as an authoritative source for verifying nascent rumors. Experimental results show that our account classification algorithms achieve both high precision and recall (around 90%). The demonstrated case studies prove that our platform is able to detect local breaking news or debunk emergent rumors faster than mainstream media sources.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".