TextVista: NLP-Enriched Time-Series Text Data Visualizations
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
There is a vast amount of unstructured text data generated every day analyzing and making sense of these text-based datasets is a complex, cumbersome task. The existing visualization tools that analyze text data leveraging Natural Language Processing (NLP) techniques, are often tailored for structured text-based data. They also fail to support reading, a crucial analysis task to validate the output of NLP techniques. We designed and developed TextVista, an NLP-enriched visualization tool that supports analysts during their analysis of unstructured text with temporal references. Our tool combines techniques including clustering, sentiment analysis, and threat detection with three views that visualize high-level patterns in the data to encourage reading. We report on TextVista’s iterative design process, which included a focus group to distill design requirements, a think-aloud interview study with data analysts to understand their impressions of the tool, and a diary study to assess its long-term usage. Through this process, we identified how TextVista supported the analysis of unstructured text with temporal references using NLP techniques and fostered methods to promote reading in situ. TextVista also encouraged serendipity when analyzing data via its question-focused overviews and flexible avenues to explore data.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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