Traffic Characterization of Instant Messaging Apps: A Campus-Level View
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
Over the past decade, Instant Messaging (IM) apps have become an extremely popular tool for billions of people to communicate online. In this paper, we use a combination of active and passive measurement techniques to study one week of IM app traffic on a large campus edge network. Despite the challenges of end-to-end encryption, user privacy, NAT, DHCP, and high traffic volumes, we identify the key characteristics of four popular IM apps: Facebook Messenger, Google Hangouts, Snapchat, and WeChat. The main observations from our study indicate a rich ecosystem of IM apps, many of which exhibit strong diurnal patterns, complex user interactions, and heavy-tailed distributions for connection durations and transfer sizes. Collectively, these four IM apps contribute about 650 GB of daily traffic volume on our campus network.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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