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Record W2295402514 · doi:10.1089/cyber.2015.0109

Predictors and Social Consequences of Online Interactive Self-Disclosure: A Literature Review from 2002 to 2014

2015· review· en· W2295402514 on OpenAlexaff
Malinda Desjarlais, Jillian Gilmour, Jasmine Sinclair, Kaitlyn B. Howell, Alyssa West

Bibliographic record

VenueCyberpsychology Behavior and Social Networking · 2015
Typereview
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMount Royal University
Fundersnot available
KeywordsSelf-disclosurePsychologySocial mediaProcess (computing)Computer-mediated communicationSocial psychologyComputer scienceThe InternetWorld Wide Web

Abstract

fetched live from OpenAlex

Computer-mediated communication has become ubiquitous in the lives of today's youth. The current review synthesizes recent findings regarding adolescents' and young adults' online interactive self-disclosure, with a particular emphasis on the direct antecedents and effects. Three broad categories of predictors are discussed, including demographic information and internal states, dispositional factors, as well as contextual factors. In addition, the synthesis of studies exploring consequences of online interactive self-disclosure indicates positive outcomes for social-related constructs. The article concludes with recommendations for future research, including the analysis of actual computer-mediated exchanges and longitudinal research that takes into account the dynamic process of self-disclosure over time and across media.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.054
GPT teacher head0.411
Teacher spread0.357 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2015
Admission routes1
Has abstractyes

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Same venueCyberpsychology Behavior and Social NetworkingSame topicImpact of Technology on AdolescentsFrench-language works237,207