MétaCan
Menu
Back to cohort
Record W2035369336 · doi:10.4018/jssoe.2012010103

On Spam Susceptibility and Browser Updating

2012· article· en· W2035369336 on OpenAlexaff
Eric Luong, Toan Huynh, James Miller

Bibliographic record

VenueInternational Journal of Systems and Service-Oriented Engineering · 2012
Typearticle
Languageen
FieldComputer Science
TopicSpam and Phishing Detection
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWorld Wide WebComputer scienceParsingWeb browserInternet privacyThe InternetInformation retrieval

Abstract

fetched live from OpenAlex

This study examines the intersection between the group of users susceptible to spam and users who continue to use out-dated browsers. Specifically, it empirically determines if an association between unsafe user behaviour and the use of an out-dated browser exists. A case study is conducted wherein spam-like emails are sent to 25,000 random email users. The emails each contain a link to a webpage that records information on any visitors. The collected data is parsed and analyzed. Information was recorded on 90 distinct visitors. Analysis showed that approximately 66% of visitors were using out-dated browsers. The work implies that future research on the problem of spam should include browser version information (as a dichotomous variable) as a covariant in their analysis. The results suggest that greater effort must be put into educating the public about safe online behaviour and best practices, including the importance of updating software.

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)0.004
metaresearch head score (Gemma)0.081
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.081
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.003
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Systems and Service-Oriented EngineeringSame topicSpam and Phishing DetectionFrench-language works237,207