Classification of tweets with a mixed method based on pragmatic content and meta-information
Bibliographic record
Abstract
The sharp rise in social networks in any field of opinion has led to the increasing importance of content analysis.Due to the concretion of the texts published on Twitter from its limitation to 140 characters, this network is the most suitable for the analysis and classification of opinions according to different criteria.Therefore, there are multiple tweet analysis tools oriented from the perspective of semantics for trying to classify content characteristics such as feeling and polarity.In this paper, the authors present a new approach to classification from a different perspective.The proposed approach addresses a complex mixed model from a perspective of pragmatics, the analysis of opinions in the context of their issuer carried out by a panel of experts, along with the classification of the type of discourse by considering the meta-information of the tweet.From this new approach, the paper presents a complete and complex analysis process of Big Data, which covers all the characteristic phases of the life cycle: capture, storage, preprocessing and analysis of a tweets database.The aim is to classify the tweets as violent or non-violent in their reference to terrorist acts.If the classification models based on the metadata of tweets reach acceptable levels of accuracy, this methodology will offer a reliable and semiautomatic alternative for tweet classification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".