Analyzing data

When you search for a term in Nebula, in this case, “validator”, the right-side panel provides rich, structured insights into the data, breaking it down into Labels, Posts Over Time, and Sentiment.

Here’s how to understand each section and apply it to your analysis.

Labels: Who’s Talking, and How Often

Labels in Nebula are tags automatically assigned to message embeddings. These labels represent usernames, data, or other meaningful identifiers extracted from the Bittensor Discord server.

rhef is the top label, appearing 1,437 times, making up 3% of all results.

Other top labels include p383_54249, sid_data_universe, angeldark7665, and alexdrocks, each accounting for 1–2% of the total.

These labels reveal who is most active in the conversation around “validator". If rhef is showing up in 3% of the 53,854 data points, that suggests this user is a key voice in validator related discussions.

Clicking these labels would let you see their specific contributions

Posts Over Time: When Are Validators Being Talked About?

This graph displays the volume of posts related to "validator" over time.

The early half of 2024 shows consistently high volume , reflecting key events in validator performance + updates.

The downtrend in late 2024 shows the validator topic stabilized.

Spotting spikes or dips in post activity can reveal community reactions to network events

Sentiment: How Do People Feel About Validators?

The sentiment line graph shows how the emotional tone of the conversation shifts over time, based on automatic sentiment labeling.

The purple (negative) sentiment appears more intense and variable, with frequent spikes below the zero line.

The green (positive) sentiment holds steady across the year, with a few upward bumps.

The frequent negative sentiment spikes may correspond to community frustration, perhaps validator downtime, or performance concerns. The steadier positive sentiment may reflect ongoing support from core contributors and validators doing well.

By combining all three, you can:

• Identify key influencers or repeat contributors

• Investigate time-specific sentiment spikes

• Track the lifecycle of community conversations over months

This view is especially useful for community leads, researchers, and validators themselves who want to monitor their reputation or respond to emerging concerns.