Improving interpretations of topic modeling in microblogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Topic models were proposed to detect the underlying semantic structure of large collections of text documents to facilitate the process of browsing and accessing documents with similar ideas and topics. Applying topic models to short text documents to extract meaningful topics is challenging. The problem becomes even more complicated when dealing with short and noisy micro‐posts in Twitter that are about one general topic. In such a case, the goal of applying topic models is to extract subtopics. This results in topics represented by similar sets of keywords, which in turn makes the process of topic interpretation more confusing. In this paper we propose a new method that incorporates Twitter‐LDA, WordNet, and hashtags to enhance the keyword labels that represent each topic. We emphasize the importance of different keywords to different topics based on the semantic relationships and the co‐occurrences of keywords in hashtags. We also propose a method to find the best number of topics to represent the text document collection. Experiments on two real‐life Twitter datasets on fashion suggest that our method performs better than the original Twitter‐LDA in terms of perplexity, topic coherence, and the quality of keywords for topic labeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it