Eysenbach, 2006
Digital-trace early warning
Aggregated public behavior can carry an early signal. Watchdogs treats it as a leading trace—not a representative survey.
Research and methodology
Watchdogs combines established methods for digital-trace monitoring, semantic clustering, change detection and calibrated forecasting into one evidence-linked loop.
Research foundations
No single method is presented as magic. The value comes from linking collection, structured interpretation, versioned change and forecast grading without dropping provenance.
Aggregated public behavior can carry an early signal. Watchdogs treats it as a leading trace—not a representative survey.
An emerging theme appears as a sharp temporary increase. Cross-source requirements help distinguish a broad shift from one viral item.
A topic rarely lives on one platform. Evidence is combined across venues while preserving the source that produced each observation.
Specific probability-scored expectations create more accountability than vague predictions that cannot be cleanly graded.
A responsible signal product states what its outputs do not mean.
Theme frequency describes the collected evidence inside a configured watch. It does not estimate the share of an entire population.
A trend can support a forecast trajectory, but it cannot prove what caused the change or guarantee what happens next.
Each platform has its own incentives, demographics and moderation. Attribution remains visible so those effects can be considered.
Language models can misclassify or compress nuance. Verbatim evidence and source links remain available for challenge.
Auditability
Watchdogs is designed so a reviewer can move backward from a verdict to a bet, from a bet to matched themes, and from each theme to the exact public evidence behind it.
Evidence path
The goal is not to remove interpretation. It is to make interpretation inspectable, dated and revisable.
Put a watchdog on it