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Record W4225010656 · doi:10.1145/3491102.3502032

Caught in the Network: The Impact of WhatsApp’s 2021 Privacy Policy Update on Users’ Messaging App Ecosystems

2022· article· en· W4225010656 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCHI Conference on Human Factors in Computing Systems · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsnot available
FundersEuropean Research CouncilCarlsbergfondetEuropean Commission
KeywordsInteroperabilityInternet privacyInstant messagingQuarter (Canadian coin)Privacy policyCompetition (biology)Computer scienceWorld Wide WebText messagingInformation privacyComputer securityBusiness

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.083
GPT teacher head0.365
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it