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Record W2065140130 · doi:10.1109/hicss.2012.583

The Role of Social Interaction Filter and Visualization in Casual Browsing

2012· article· en· W2065140130 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of SaskatchewanMacEwan University
Fundersnot available
KeywordsVisualizationCasualFilter (signal processing)Computer scienceInformation visualizationHuman–computer interactionSocial relationFeature (linguistics)Data visualizationWorld Wide WebMultimediaArtificial intelligencePsychologyComputer visionSocial psychology

Abstract

fetched live from OpenAlex

Traces of social interaction in information spaces have the potential to improve information exploration. We evaluated interactive interfaces that utilize social interaction history. More specifically, we compared the value of a social filter and social interaction visualization in supporting casual browsing. We hypothesized that information filtering in general plays a more important role than visualization, as it provides additional control for users to browse information collections. Our experimental results showed that, compared to the baseline interface, the social filter increased subjective user satisfaction and was perceived by the study participants to enhance their effectiveness in finding interesting information. However, there were no significant differences between the social filter and social visualization systems. Our analysis suggested that the synergy of information filtering and visualization is more effective than each feature working separately.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.091

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.330
Teacher spread0.306 · 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

Quick stats

Citations5
Published2012
Admission routes1
Has abstractyes

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