AgoraLens applies structured language analysis to transform unstructured public communication into consistent, comparable analytical indicators.
The platform is designed to help analysts observe how public political discourse evolves over time — not to judge intent, beliefs, legality, or personal character.
AgoraLens continuously monitors public, on-record accounts of elected officials and key political figures using supported platform APIs.
Public accounts of Finnish Members of Parliament and major party leadership.
New public posts are detected and processed continuously, with typical update intervals of approximately 15 minutes.
Each new public post is processed using structured language analysis models designed to identify observable rhetorical features.
These models evaluate characteristics of language such as:
The Rhetorical Intensity Index is a composite indicator summarizing the overall rhetorical force of a public statement based on weighted linguistic features.
All scores are contextual indicators, not factual claims or judgments.
AgoraLens also analyzes how audiences respond to public statements over time.
In addition to response volume and timing patterns, AgoraLens analyzes the content of public replies to identify how audiences are reacting to specific statements.
Reaction analysis is based on a representative sample of public replies and focuses on observable patterns, including:
Backlash is defined as a statistically notable concentration of negative or oppositional reactions relative to the baseline response pattern for similar posts.
Reaction labels describe observable reply patterns only. They do not assert intent, coordination, motivation, or legitimacy of the reactions.
Reaction classification combines structured language interpretation with deterministic thresholding to ensure consistency across time and actors. While individual linguistic features are probabilistic, the assignment of reaction categories (e.g. backlash detected vs not detected) follows explicit, versioned rules.
Rather than focusing on absolute engagement volume alone, we examine both audience reaction composition and response volatility — changes in how and when reactions occur following specific posts.
Response volatility indicators may reflect:
These patterns describe audience behavior, not endorsement or opposition by AgoraLens.
In low-volume contexts, reaction analysis may be marked as preliminary or unavailable when insufficient reply data is present. These cases are explicitly labeled to prevent over-interpretation.
Automated analysis of language is inherently imperfect.
AgoraLens is designed as a decision-support tool, not an automated judge.
All metrics are probabilistic and should be interpreted alongside original public content and contextual knowledge.
To support transparency and responsible use, we publish:
This transparency allows users to understand how results are produced and where their limitations lie.
This section details the specific logic and criteria behind key platform features.
The High Risk Feed automatically surfaces posts that exhibit elevated rhetorical intensity combined with specific risk markers. It is designed to help analysts quickly identify statements that may signal escalation.
A post appears in the High Risk Feed if it meets one or more of the following conditions:
Note: Not all high-risk alerts represent harmful or policy-violating content. A "High Risk" label indicates structural linguistic escalation that warrants analyst attention, not necessarily a judgment of the content's validity.
The Analysis Snapshot on individual post pages provides a frozen view of analytical metrics at a specific moment in time. Because social media content is dynamic, scores are anchored to a "Snapshot Window" to ensure consistency in reporting.
This histogram visualizes the rate of audience interactions over time. Peaks indicate moments of viral acceleration or coordinated response bursts.
If the pattern of replies meets the deterministic criteria for Audience Backlash (statistically high concentration of negative/oppositional language), a warning label appears in the snapshot. This serves as a structural indicator of audience reception, distinct from the post's content.
Note: The snapshot captures the state of the post at the time of analysis. If a post is deleted or significantly altered later, the snapshot serves as a historical record of its state during the active monitoring window.
The Metric Deep Dive widget breaks down the high-level Rhetorical Intensity score into its constituent linguistic components. These sub-scores help verify why a particular intensity level was detected.
Detects language that evokes conflict, physical struggle, or militaristic framing. High scores correlate with rhetoric that frames politics as a battle or existential fight.
Identifies references to "Them," "The Elite," "Globalists," or other generalized out-groups. This is a key marker of populist or polarizing rhetoric, distinct from specific criticism of named individuals.
Measures the extent to which negative traits or actions are ascribed to entire groups rather than individuals. High scores often signal stereotyping or collective blame.
Detects attempts to bridge divides, acknowledge nuance, or de-escalate. Unlike the other metrics, a high score here reduces the overall Rhetorical Intensity.