EmailTime: visual analytics and statistics for temporal email
Bibliographic record
Abstract
Although the discovery and analysis of communication patterns in large and complex email datasets are difficult tasks, they can be a valuable source of information. We present EmailTime, a visual analysis tool of email correspondence patterns over the course of time that interactively portrays personal and interpersonal networks using the correspondence in the email dataset. Our approach is to put time as a primary variable of interest, and plot emails along a time line. EmailTime helps email dataset explorers interpret archived messages by providing zooming, panning, filtering and highlighting etc. To support analysis, it also measures and visualizes histograms, graph centrality and frequency on the communication graph that can be induced from the email collection. This paper describes EmailTime's capabilities, along with a large case study with Enron email dataset to explore the behaviors of email users within different organizational positions from January 2000 to December 2001. We defined email behavior as the email activity level of people regarding a series of measured metrics e.g. sent and received emails, numbers of email addresses, etc. These metrics were calculated through EmailTime. Results showed specific patterns in the use email within different organizational positions. We suggest that integrating both statistics and visualizations in order to display information about the email datasets may simplify its evaluation.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".