Exploring Viral Patterns & Social Media Trends (2020-2025)
A comprehensive exploratory data analysis of trending hashtags on Twitter/X from 2020 to 2025. This project analyzes over 12,000 trending entries to uncover patterns in viral content, major world events, and social media engagement metrics.
Using Python, Pandas, and data visualization libraries, I examined temporal patterns, identified peak trending periods, and analyzed how major global events influenced social media conversations.
Analysis of trending activity across six years (2020-2025). Shows a consistent high engagement from 2020-2023, with a notable decline in 2024-2025. This decrease may reflect users leaving or reducing their use of the platform following the ownership and platform changes.
The most viral hashtags over the entire period. Dominated by major cultural figures (Kanye, Trump), holidays (New Year, Thanksgiving), and global events (Ukraine, Israel/Gaza, Iran).
Note: Some hashtags appear as boxes due to Japanese character rendering limitations, reflecting the dataset's international scope.
Temporal patterns revealing seasonal variations in trending activity. March and July show consistently high engagement across multiple years, while late 2024 shows decreased activity.
Monthly patterns reveal strong early-year engagement, coinciding with New Year momentum and major cultural events. Activity drops sharply in late fall/winter, suggesting seasonal behavioral shifts where users are less active on social media during holiday periods.
Weekend activity (Saturday-Sunday) shows slightly higher engagement compared to weekdays, indicating users are more active on social media during their time off work.
Distribution analysis reveals a heavily right-skewed pattern: most hashtags receive moderate engagement (under 1M tweets), while a small number of viral outliers achieve massive reach exceeding 60M tweets. The boxplot shows increasing variance in 2025, with more extreme viral events compared to previous years.
Year-by-year breakdown of the top 10 trending hashtags, showing the evolution of viral content from COVID-dominated 2020-2021 to more diverse topics in recent years.
Note: Some hashtags appear as boxes due to Japanese character rendering limitations, reflecting the dataset's international scope.