Lexical Semantic Relatedness for Twitter Analytics
Bibliographic record
Abstract
Existing work in the semantic relatedness literature has already considered various information sources such as WordNet, Wikipedia and Web search engines to identify the semantic relatedness between two words. We will show that existing semantic relatedness measures might not be directly applicable to microblogging content such as tweets due to i) the informality and short length of microblogging content, which can lead to shift in the meaning of words when used in microblog posts, ii) the presence of non-dictionary words that have their semantics defined/evolved by the Twitter community. Therefore, we propose the Twitter Space Semantic Relatedness (TSSR) technique that relies on the latent relation hypothesis to measure semantic relatedness of words on Twitter. We construct a graph representation of terms in tweets and apply a random walk procedure to produce a stationary distribution for each word, which is the basis for relatedness calculation. Our experiments examine TSSR from three different perspectives and show that TSSR is better suited for Twitter analytics compared to the standard semantic relatedness techniques.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".