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Record W2910334260 · doi:10.21810/strm.v10i2.235

Time-Space Analysis of Facebook News Feeds

2018· article· en· W2910334260 on OpenAlexaffvenue
Felix Lo

Bibliographic record

VenueStream Interdisciplinary Journal of Communication · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTemporalitySocialitySpace (punctuation)SociologySpacetimeAestheticsMedia studiesComputer scienceEpistemologyArt

Abstract

fetched live from OpenAlex

In the 20th century, several scholars across different disciplines have explored the relations between sociality and the associated perceptions of time and space. This paper draws on their theories to study how the Facebook News Feed feature inscribes users with a certain kind of temporality and spatiality. Building on Manual Castells' characterization of online activities as a "temporal collage" it argues that, through the interactions with News Feeds, users encounter the desequencing of the temporality of their social space. It further analyzes a News Feed page as a temporal object as defined by Bernard Stiegler, and adopts his critique of cinematic time to reveal how this feature inscribes an "always on" behavior for users even when they are offline. It concludes by discussing the political significance of this temporality and spatiality in two different senses: the constant acceleration in the pace of life and online surveillance. It draws on David Harvey's concept of space-time compression to discuss the relations between the temporality of Facebook and capitalism, and on Anthony Gidden's time-space distantiation to discuss the power relations of online surveillance.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.020
GPT teacher head0.352
Teacher spread0.331 · 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 designObservational
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

Citations1
Published2018
Admission routes2
Has abstractyes

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