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Record W7143815617 · doi:10.71465/ajml3081

Predicting Social Trends Using Machine Learning Models

2025· article· W7143815617 on OpenAlexaff
Dr. Rachel Singh

Bibliographic record

VenueAmerican Journal of Machine Learning · 2025
Typearticle
Language
FieldComputer Science
TopicSentiment Analysis and Opinion Mining
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsSocial mediaTime seriesSocial learningSupport vector machinePredictive modelling

Abstract

fetched live from OpenAlex

Predicting social trends has become increasingly important in various fields, including marketing, politics, and public health. This paper explores the use of machine learning (ML) models in forecasting social trends by analyzing large datasets from social media platforms, online forums, and other digital sources. Machine learning algorithms, such as time series forecasting, sentiment analysis, and classification models, are evaluated for their ability to predict societal shifts in real time. The paper discusses the advantages and challenges of applying these models in dynamic and complex social environments and provides insights into their practical applications.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.588
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.005
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.004
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 head0.028
GPT teacher head0.302
Teacher spread0.275 · 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.

Study designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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