ViralRadar: Building a Reddit Trend Intelligence Dashboard
After working on machine learning and analytics projects, I wanted to build something that felt closer to how people discover trends in the real world. Most trends do not start as formal reports. They usually begin as conversations, posts, comments, and sudden spikes in online engagement.
That idea led me to build ViralRadar, a trend intelligence dashboard that tracks online discussions and helps identify which topics are gaining attention.
Why I Built ViralRadar
The goal of ViralRadar was to build a system that could monitor social discussion data and surface topics that appear to be going viral.
Instead of manually scrolling through platforms and trying to guess what is trending, I wanted to create a dashboard that collects posts, measures engagement signals, and presents the most active topics in a clear format.
This project helped me explore how data engineering, analytics, and product thinking can come together in a social intelligence tool.
Project Overview
ViralRadar analyzes online discussion posts and ranks topics based on engagement signals such as post score, number of comments, upvote ratio, subreddit activity, and recency.
The dashboard helps answer questions like:
Which topics are gaining attention right now?
Which topics are showing unusual spikes in activity?
Which posts are receiving the strongest user reactions?
How can raw social media activity be turned into useful trend signals?
The main idea was to turn noisy online conversations into a structured trend dashboard.
ViralRadar dashboard showing total posts, detected topics, viral alerts, highest spike, and sentiment distribution.
Tech Stack
The project was built using:
Python for data collection and backend logic
Pandas for cleaning and organizing post data
Reddit/social discussion data as the source of trend signals
Plotly/Dash dashboard UI for presenting insights visually
JSON/CSV data handling for storing and processing collected posts
Git and GitHub for version control and release management
How ViralRadar Works
The system starts by collecting discussion data from online communities. Each post includes information such as the title, subreddit, score, number of comments, upvote ratio, timestamp, and URL.
Once the data is collected, ViralRadar processes it into a structured format. The dashboard then uses those fields to highlight posts and topics that are gaining attention.
A topic with a high score, strong comment activity, and a strong spike compared to its recent baseline can indicate that people are actively engaging with it. By combining these signals, ViralRadar makes it easier to identify trends instead of relying only on manual browsing.
Viral alerts table and engagement charts showing how topics are ranked using recent mentions, spike multiplier, score, comments, and sentiment.
Turning Engagement into Trend Signals
One of the most interesting parts of this project was thinking about what “viral” actually means from a data perspective.
A topic is not trending only because one post has many likes. Real trend detection needs multiple signals. For ViralRadar, I looked at engagement indicators such as:
Post score
Comment count
Spike multiplier
Sentiment score
Topic/category
Recency of the post
This helped me understand that trend detection is not just about collecting data. It is about deciding which signals matter and how to present them in a way that makes sense.
Building the Dashboard
The dashboard was designed to make trend data easy to understand. Instead of looking at raw JSON or tables, users can view topics, scores, comments, spike multipliers, sentiment distribution, and viral alerts in a cleaner visual format.
The goal was to make the dashboard useful for anyone who wants to quickly understand what people are talking about online, such as marketers, analysts, creators, researchers, or product teams.
This made the project feel more like a real social listening tool rather than just a data collection script.
Challenges I Faced
One challenge was handling social data in a consistent way. Online discussion data can be messy because posts come from different communities, have different activity levels, and change quickly over time.
Another challenge was deciding how to define a “trend.” A post with many comments may be controversial, while a topic with a high spike multiplier may be growing quickly compared to its recent baseline. Building ViralRadar helped me understand how important it is to combine multiple signals instead of depending on just one number.
I also focused on making the project easier to present by adding documentation, screenshots, and a GitHub release.
What I Learned
ViralRadar helped me learn how to think about data products beyond traditional machine learning models.
Some of my biggest takeaways were:
How to collect and structure social discussion data
How to analyze engagement signals
How to rank topics based on multiple indicators
How to build a dashboard around real-world online activity
How to turn raw data into a useful product experience
How to document and release a project professionally
This project showed me that analytics is not only about historical data. It can also be used to understand what is happening right now.
What’s Next
There are several improvements I would like to add to ViralRadar:
Sentiment analysis improvements to better understand whether a trend is positive, negative, or neutral
Real-time topic tracking
AI-generated summaries of trending discussions
Alerts for sudden spikes in engagement
Better filters by topic and time range
Deployment so the dashboard can be accessed publicly
Conclusion
ViralRadar was a fun and practical project because it connected data analytics with real-world online behavior.
By collecting discussion data, analyzing engagement signals, and presenting trends through a dashboard, I was able to build a project that feels useful for marketing, social listening, research, and product analysis.
This project helped me better understand how online trends can be detected, measured, and visualized using data.
Project Links
GitHub Repository: ViralRadar
GitHub Release: ViralRadar v1.0
Live Demo: Coming Soon
