Networked campaigns: Traffic tags and cross platform analysis on the web
Bibliographic record
Abstract
This article defines a new methodological framework to examine emerging forms of political campaigning on and across Web 2.0 platforms (i.e. Facebook, Youtube, Twitter) in the North-American context. The proposed method seeks to identify the new strategies that make use of campaign text s, users, keywords, information networks and software code to spread a political communications and rally voters across distributed, and therefore seemingly unmanageable spheres of online communication. The proposed method differentiates itself from previous Web 1.0 methods focused on mapping hyperlinked networks. In particular, we pay attention to the new materiality of the Web 2.0 as constituted by shared objects that circulate across modular platforms. In this paper we develop an object-centered method through the concept of traffic tags – unique identifiers that by enabling the circulation of web objects across platforms organize political activity online. By tracing the circulation of traffic tags, we can map different sets of relationships among uploaded and shared web objects (text, images, videos, etc.), political actors (online partisans, political institutions, bloggers, etc.), and web based platforms (social network sites, search engines, political websites, blogs, etc.).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".