Methods

Exploratory-first approach

SignalMap privileges discovery over hypothesis testing. The platform is designed to surface patterns, anomalies, and temporal shifts in public discourse before formal hypotheses are formulated. Analyses are structured to support open-ended exploration rather than confirmatory inference.

Time-window anchoring

All analyses are anchored to explicit time windows. Data are aggregated within defined intervals (e.g., days, weeks, or months) to produce longitudinal signals. Time boundaries are fixed at analysis outset to avoid post hoc window selection and reduce temporal confounding.

Signals

In SignalMap, a signal is a time-varying measurement derived from observable data. Examples include subscriber counts (from archival sources or platform APIs), follower counts, sentiment scores, and hashtag frequency. Signals are not ground truth; they are platform-dependent, sparsely sampled, and subject to measurement error. Different platforms expose different metrics at different granularities, and archival coverage is uneven across time and source.

Confidence and missing data are treated as first-class concerns. When extraction is uncertain or data are absent, values are omitted rather than imputed. Missing values are expected and do not imply failure; they reflect the inherent incompleteness of available sources. Analysis is conducted relative to events and time windows, not absolute values. The emphasis is on conditional change and comparative patterns rather than point estimates.

Where relevant, exogenous macroeconomic signals (e.g., Brent crude oil price from FRED) are used to contextualize discourse patterns. These signals are treated as external context, not as dependent variables, and are sourced from the Federal Reserve Economic Data (FRED) series DCOILBRENTEU.

Aggregation-only analysis

Individual-level profiling is explicitly avoided. All metrics are computed at aggregate levels (e.g., per time window, per topic, per hashtag cluster). No attempt is made to attribute sentiment, affiliation, or behavior to identifiable persons. The focus is on distributional and structural patterns rather than individual classification.

Multilingual text handling

Text is processed in a language-agnostic manner where possible. Sentiment and topic models are applied with awareness of multilingual contexts. No assumption is made that a single language or script dominates the corpus. Cross-lingual comparisons are treated with caution given known limitations of transfer and calibration.

Archival and contextual signals

Where available, archival sources (e.g., web archives, historical snapshots) are used to contextualize temporal patterns. These sources provide supplementary signals about the state of discourse at given points in time. Archived material is treated as a lens on historical context, not as a primary corpus for inference.

Limitations and uncertainty

Findings are preliminary and subject to revision. Sampling bias, platform-specific artifacts, and model limitations may affect results. No claim is made to external validity or generalizability beyond the defined corpus and time window. Uncertainty is reported where feasible; absence of uncertainty estimates does not imply certainty.