Severity
How consequential is the underlying issue to customers, people or operations?
Brandleap Research Labs · Report 03
When a story about your brand spreads, the hard part is knowing what still matters—and what to do next.
A deep research review of negative sentiment across digital sources, connecting the original evidence to the narratives AI systems may encounter. Then a practical, prioritized plan to improve the context people—and machines—can find.
The pages shown are sample research for Six Flags—not a client endorsement, live monitoring, or independently verified current facts.
Page 01 / 12 · Executive summary
3,503
negative opinions in sample
1,565
negative source pages
318
domains represented
10 / 19
channel types / themes
The question
A search for “sentiment” can flatten years of issues into one number. This report looks at the shape of the conversation: how many sources repeat a claim, how serious it is, whether it is still active, and where it appears.
Each finding is tied back to source material and organized into themes. That lets a team distinguish a new spike from an entrenched narrative—and act on evidence rather than a vague reputation score.
Gather the trail
Review thousands of potential digital sources across news, reviews, forums, social, video and search.
See what persists
Cluster dated evidence by theme, reach, severity and staying power without confusing pages with unique channels.
Prioritize credible action
Build a sequence of practical fixes, source context and monitoring—not promises of instant reputation repair.
How to read the blast radius
No metric is a verdict on a brand. The dimensions make trade-offs visible so a team can decide what deserves investigation, correction, context or simply continued observation.
How consequential is the underlying issue to customers, people or operations?
How widely does the claim recur across distinct sources and domains?
Does the story fade—or keep resurfacing over time?
Is the current conversation changing, growing or cooling?
Are relevant sources likely to be retrievable in AI-assisted discovery?
AI exposure is a research lens on discoverability and source context—not a universal ranking factor or a guarantee of inclusion, citation or position in any answer system.
From evidence to resolution
Prioritization turns the map into a sequence. The best next action depends on what the source actually says and who can credibly address it.
Trace high-consequence claims to original sources, dates and what can be independently established.
Route operational or customer concerns to the people able to investigate and make a real change.
Make accurate, specific information easier to find on authoritative owned and third-party sources.
Invite honest feedback and strengthen credible references. Do not suppress legitimate criticism or fabricate consensus.
Track whether sources, themes and retrieval context change; revisit priorities as evidence evolves.
Inside the report
All 12 previews are actual pages from the sample report. Open a page to inspect the evidence; use arrow keys to move through the report.
PAGE 01
The central finding and the clearest decisions for a response.
PAGE 02
Source excerpts organized by what happened and what people say.
PAGE 03
How recency, persistence and repeated claims shape next steps.
PAGE 04
Dated opinions placed by age and theme, not flattened into a score.
PAGE 05
Where negative conversation is wide, deep and sticking.
PAGE 06
Breadth, intensity and staying power across 19 themes.
PAGE 07
Separate a fresh spike from a story that has endured.
PAGE 08
A historical lens on the themes that continue to surface.
PAGE 09
Compare each location without losing the shape of its story.
PAGE 10
Two high-consequence narratives, examined separately.
PAGE 11
How the language changes—and where the harshest words live.
PAGE 12
Newer damage at the center; entrenched narratives at the edge.
A note on interpretation. Screenshots are sample research, not a live monitor. Counts describe this sample’s collected material; they are not a measure of all public opinion or a universal AI ranking factor.
What this is—and is not
Useful for
Finding the source behind a repeated claim, surfacing where context is thin, deciding what to fix first and setting up a monitoring baseline.
Not a promise
No guaranteed rankings or citations. Adding credible context can help discovery, but does not erase negative commentary or rewrite a model’s memory.
More evidence-led research from Brandleap Research Labs.
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