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Record W1581905769 · doi:10.1002/meet.2014.14505101145

Privacy and control in online social profiles: Toward a typology of users

2014· article· en· W1581905769 on OpenAlexaff
Jacquelyn Burkell, Alexandre Fortier

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsWestern University
Fundersnot available
KeywordsTypologyInternet privacyControl (management)Social network (sociolinguistics)PsychologyFrame (networking)Computer scienceUser-generated contentPersonally identifiable informationSocial controlSocial psychologySociologyWorld Wide WebSocial mediaComputer securityArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT This poster uses Q methodology to investigate subjective perspectives on privacy of and control over social network profiles. Earlier research using interview and focus group techniques suggests that the default conception of online social networks is as public spaces where there is little or no expectation of control over content or distribution. This conception, however, applies largely to information posted by others, and participants frame their own online participation in different ways. The current study explicitly investigates these different subjective perspectives. The results suggest three different user profiles or privacy orientations: one group views their online profiles as spaces for social display, but exert control over content and audience; a second group treats their profiles as spaces for open social display, exercising little control over either content or audience; and a third group views social network profiles as places to post personal information to a controlled audience. These different perspectives lead to different privacy needs and expectations.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.004
Science and technology studies0.0050.015
Scholarly communication0.0110.015
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.364
Teacher spread0.333 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

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