From 50K to 8.2 Million in 24 Hours: Vozinha's Algorithmic Consecration at the 2026 FIFA World Cup
收藏资源简介:
This deposit provides the official v0.1 dataset and evidence package for the study “From 50K to 8.2 Million in 24 Hours: Vozinha’s Algorithmic Consecration at the 2026 FIFA World Cup.” The package documents the public follower-count trajectory, source evidence, narrative-frame taxonomy, multilingual corpus schema, and methodological materials used to analyze the viral visibility of Vozinha (@vozinha1), Cape Verde’s goalkeeper, after the Spain 0–0 Cape Verde match at the 2026 FIFA World Cup. The dataset reconstructs a conservative timeline from public journalistic sources, screenshots, structured evidence logs, and one primary scraper collection point. The strongest exact anchor is the author’s Apify collection, which recorded 8,235,652 Instagram followers at 2026-06-16 15:47 UTC. The pre-match baseline is treated as an estimated range of 45,000–56,000 followers, not as a single exact value. The package includes processed CSV files, a reconstructed timeline, a source log, a multilingual corpus schema, a narrative-frame taxonomy, player market-value and influencer-value estimate tables, evidence hashes, visual evidence documentation, scripts, and methodological notes. The visual evidence includes public screenshots of X/Twitter posts and Instagram Stories, but these screenshots are treated as visual-discursive evidence and not as API-equivalent measurements. This deposit should be understood as a research dataset and evidence package, not as a complete historical Instagram archive. The follower timeline is reconstructed, not continuous, and values are explicitly classified as exact, reported, threshold, estimate, inferred, or screenshot-based. Volatile X/Twitter engagement metrics are not treated as verified API data. Zenodo is the canonical archived research package and citation target for this release. A Kaggle version may be provided separately as an exploratory mirror for notebook-based analysis and public data-science reuse.



