Caught in the Network: The Impact of WhatsApp’s 2021 Privacy Policy Update on Users’ Messaging App Ecosystems
Bibliographic record
Abstract
In January 2021, WhatsApp announced an update to their privacy policy, sparking an outcry that saw millions of users install other messaging apps such as Telegram and Signal. This presented a rare opportunity to study users’ experiences when trying to leave the world’s most popular communication app. We conducted surveys in February and May with 1525 WhatsApp users from Mexico, Spain, South Africa, and the United Kingdom. Over a quarter wanted to switch at least part of their communication to other apps, but 74% of them failed to do so. By May, 27% had increased their use of other apps, and only 16% used WhatsApp less. Beyond network effects, users struggled with making informed choices of alternative apps and with differences in their design and functionality. We suggest messaging interoperability as an approach to alleviate switching costs and discuss implications for HCI research and competition regulation of digital services.
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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 | 0.021 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".