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Record W3120292695 · doi:10.24908/iqurcp.8229

Gretchen Is: On Your Facebook Profile, Analyzing the Sociolinguistics of Your Status Message

2018· article· en· W3120292695 on OpenAlexvenueno aff
Gretchen McCulloch

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsnot available
Fundersnot available
KeywordsConversationLuckSocial statusSocial psychologyPsychologyPerceptionSociolinguisticsPresentation (obstetrics)Expression (computer science)Relation (database)Section (typography)Computer scienceSociologyLinguisticsCommunicationEpistemology

Abstract

fetched live from OpenAlex

Some Facebook status messages get dozens of comments and "likes", while others are not interacted with at all. But is this social success just good luck, or are there certain features that can allow us to predict whether a status will be popular or not? This presentation examines a cross‐section of authentic Facebook status messages in an attempt to figure out what makes a status socially successful. Three types of status message are identified: the narcissistic status, the informative status, and the shared‐reference status. The narcissistic status is the stereotypical expression of emotion or mundane activity, where the audience is less important, and consequently less engaged, than the author. The informative status provides basic information about the user's current location or activity, which can allow him or her to be more easily contacted by friends, but does not tend to inspire much conversation. However, it is the shared‐reference status, which refers to an experience that the author shares with a relatively small group of friends, that inspires the greatest levels of dialogue in the form of comments and "likes." Further details to be discussed include the varieties of shared‐reference status and the relation of Facebook statuses to external perception of them and to real‐life rules of social interaction.

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 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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0030.001
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 head0.171
GPT teacher head0.411
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2018
Admission routes1
Has abstractyes

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