GoogleTrendArchive: A Year-Long Archive of Real-Time Web Search Trends Worldwide

Abstract

GoogleTrendArchive is a comprehensive archive of Google Trending Now data spanning over one year (from November 28, 2024 to January 3, 2026) across 125 countries and 1,358 locations. Unlike Google Trends, which requires specifying search terms in advance, Trending Now captures search queries experiencing real-time surges, offering a way to inductively discover trending patterns across regions for studying collective attention dynamics. However, Google does not provide historical access to this data beyond seven days. Our dataset addresses this gap by presenting an archive of Trending Now data. The dataset contains over 7.6 million trend episodes. Each record includes the trend identifier, search volume bucket, precise timestamps, duration, geographic location, and related query clusters. This dataset, among other, enables systematic studies of information diffusion patterns, cross-cultural attention dynamics, crisis responses, and the temporal evolution of collective information-seeking at a global scale. The comprehensive geographic coverage facilitates fine-grained cross-country or cross-regional comparative analyses.

Figures (4)

• Figure 1: We release the GoogleTrendArchive dataset. We collected daily exports of Google's Trending Now data across 125 countries and 1,358 total geographic locations, creating a multilingual archive of 7.6M+ trend episodes spanning over one year (Nov 2024--Jan 2026). Unlike Google Trends, which requires pre-specified queries, this dataset enables inductive discovery of what captured collective attention globally. This supports research on cross-national information diffusion, crisis communication, and cultural variation in real-time search behavior.
• Figure 2: Top 30 Trends of 2025 (Country-level data only) by Total Search Volume.
• Figure 3: Distribution of topics among the top 50 trending searches in 2025 for 20 randomly selected countries. To avoid risks of large-scale automated annotations baumann2025llmhacking, topics were manually annotated by the authors based on trend titles and associated queries. For languages not spoken by the authors, trend titles were automatically translated to English, with web search used to clarify ambiguous terms or culturally specific references.
• Figure 4: Geographic spread and temporal dynamics of multi-country trends; only trend episodes with actual---i.e., not estimated---durations are included. (A) Distribution of geographic spread showing power-law pattern. (B) Time span distribution for trends in 10+ countries. (C) Positive correlation between geographic spread and search volume (Spearman $\rho$ = 0.43, p $<$ 0.001). (D) Non-monotonic relationship between geographic spread and time span, with medium-spread trends (20-40 countries) showing shorter durations than broadly global trends (60+ countries). Orange lines: LOESS smoothing; gray shading: 95% CI. Country-level trends with 5+ countries, $<$ 1 week.