BEHAVE/BEHAVE-TEXT/README.md

behave-text

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Text/messaging-domain behavioral observation registry. Defines what can be observed about an actor through their written messaging activity — stylometric fingerprints, lexical patterns, interaction rhythms, and governance-role signals.

BEHAVE-TEXT operates on derived features, not raw text. Sensors hash, aggregate, and classify before emitting — the raw message content never enters a BEHAVE observation. This is a tighter constraint than BEHAVE-SHELL because the source signal is text content; the PII risk is higher.

The topic prefix is actor.observation.text (not attacker.) because chat groups include non-attacker roles — admins, buyers, sellers, bots, lurkers. The framing is deliberately neutral: BEHAVE-TEXT observes actors, not adversaries.

Install

pip install behave-text

For local development:

pip install -e ../core/ -e ".[dev]"

Quickstart

from behave_text.spec import Observation, Window, TOPIC_PREFIX, event_topic_for

obs = Observation(
    primitive="stylometric.capitalization_habit",
    value="lowercase",
    confidence=0.91,
    window=Window(start_ts=1714000000.0, end_ts=1714086400.0),
    source="behave/text-sensor/stylometry.py",
)
topic = event_topic_for("stylometric.capitalization_habit")
# → "actor.observation.text.stylometric.capitalization_habit"

Public API (behave_text.spec)

Symbol	Description
Observation	Registry-aware subclass of behave_core.spec.Observation. Validates primitive and value against PRIMITIVE_REGISTRY.
Window	Re-exported from behave_core.
ObservationValue	Re-exported union type.
PRIMITIVE_REGISTRY	dict[str, ValueTypeSpec] — the full primitive catalog (35 entries).
ValueKind	Enum: CATEGORICAL, NUMERIC, HASH, ARRAY, FREE_STRING, BOOL.
ValueTypeSpec	Pydantic model: kind, allowed values, bounds, notes.
is_known(primitive)	bool — whether a primitive path is registered.
get(primitive)	Returns the ValueTypeSpec; raises KeyError if unknown.
TOPIC_PREFIX	"actor.observation.text"
event_topic_for(primitive)	Returns the full event bus topic string.

Note: to_event_payload / from_event_payload (full round-trip helpers) are present in behave-shell but not yet implemented here — status: planned.

Primitives

35 primitives across 6 categories.

---

stylometric.* — Writing style fingerprints (12 primitives)

Stylometric primitives capture the unconscious writing habits that distinguish one author from another. The field goes back to the Mosteller-Wallace Federalist Papers study (1963): function-word frequencies alone can attribute authorship with high accuracy in long-form English text. BEHAVE-TEXT adapts these methods to short-form Spanish chat, which introduces domain-specific challenges (short messages, informal register, code-switching, emoji). Calibration results from the Rutify corpus are noted inline where they affect interpretation.

Primitive	Kind	Description
stylometric.punctuation_style	hash	Canonical punctuation-pattern fingerprint hash. Captures the author's consistent punctuation tics (double spaces, comma habits, no-period endings) as a searchable signature.
stylometric.capitalization_habit	categorical	Dominant capitalization rule. lowercase = no capitals. proper = standard sentence/title case. random_caps = no consistent rule. mixed_i = consistent lowercase 'i' mid-sentence — common in Spanish chat where the standalone-'I' habit doesn't apply but the behavior transfers.
stylometric.emoji_usage	categorical	Rate of emoji use. none, occasional, frequent, exclusive (messages rarely without emoji). Captures tone and register.
stylometric.emoji_placement	categorical	Emoji position relative to sentence-ending punctuation. pre_punctuation = 'Hola 😊.' post_punctuation = 'Hola. 😊' Individual authors are strikingly consistent in this micro-habit.
stylometric.message_length_class	categorical	Median message length bucket: short 1-5 words, medium 6-20, long 21-50, paragraph >50. See also message_length_variance_class for distribution shape.
stylometric.message_length_variance_class	categorical	Distribution shape of per-message word counts. tight CV<0.5 (always 1-3 words). varied 0.5≤CV<1.5 (normal mix). bimodal CV≥1.5 (mostly short with occasional rants). Two authors can share the same median length but have wildly different variance.
stylometric.linebreak_style	categorical	Whether the author sends one complete thought per message or bursts multiple short sequential messages. multi_line = habitual 3-5 short messages per turn. wall_of_text = dense blocks, rarely uses line breaks. Captures a stylistic rhythm that is hard to consciously alter.
stylometric.typo_signature	hash	SHA-256 of the canonical persistent-typo set — the specific recurring errors the author makes consistently (e.g. always writes tener as tenet, or porque as xq). Persistent typos are strong authorship signals because they reflect keyboard-motor habits.
stylometric.function_word_distribution_top50	hash	64-bit SimHash over the 50 most common Spanish function-word frequency vector. Based on the Mosteller-Wallace method. Calibration note (2026-05-02, Rutify corpus): within-author and cross-author Hamming distance distributions overlap (within median 8 bits, cross median 10 bits) in short-message chat — this primitive alone cannot discriminate authors. Engines should weight it low and composite with character n-grams and distinctive vocabulary. Kept in v0 for calibration grids.
stylometric.function_word_distribution_top200	hash	64-bit SimHash over the 200 most common Spanish function words. The wider list reaches into the long tail (rare-but-individual words like tampoco, aunque, mientras) that carry more discriminating signal in short-message corpora. Not yet emitted by v0 prototype — populated in v0.2.
stylometric.character_ngram_simhash	hash	64-bit SimHash over character n-gram frequencies (default n=3), lowercased. Orthogonal to function-word distributions: captures punctuation tics, accent-stripping habits, typo patterns, and idiom fragments that survive paraphrase. Accents are preserved because accent-stripping is itself a stylistic tic. Source label declares n size (e.g. #char3gram).
stylometric.distinctive_vocabulary_signature	hash	64-bit SimHash over a TF-IDF-weighted top-K rare-word vector. Captures the author's distinctive lexicon — words they use that other authors in the same corpus do not. Complementary to function-word distributions: where function_word_* captures common-word style, this captures individual lexical choice. Requires the full corpus for IDF computation. Source label declares top-K and corpus tag (e.g. #tfidf-top50).

---

lexical.* — Vocabulary and linguistic patterns (8 primitives)

Lexical primitives characterize what and how an actor writes at the word and sentence level. Where stylometric primitives fingerprint unconscious micro-habits, lexical primitives capture deliberate linguistic choices — vocabulary richness, how questions are formed, register.

Primitive	Kind	Description
lexical.vocabulary_richness	numeric [0,1]	Moving-Average Type-Token Ratio (MATTR) over a sliding window (default 50 tokens). Volume-independent: each window contributes its own unique/total ratio, the value is the mean. Avoids the standard TTR bias where larger corpora mechanically score lower. Source label declares window size.
lexical.slang_density	numeric [0,1]	Rate of slang terms per message, against a locale-tuned slang corpus.
lexical.code_switching_rate	numeric [0,1]	Language switches per N tokens (Solorio & Liu metric). A speaker who switches between Spanish and English, or Spanish and lunfardo/caló, will have a higher rate than a monolingual writer.
lexical.code_switching_matrix_language	free_string	BCP-47 tag of the dominant (matrix) language in code-switching texts (e.g. es-CL, es-AR). The matrix language is the grammatical scaffold; embedded languages appear as inserts.
lexical.code_switching_embedded_languages	array[free_string]	BCP-47 list of non-matrix languages observed in the actor's messages.
lexical.sentence_complexity_class	categorical	Dominant clause structure. simple = single-clause. compound = two independent clauses joined by coordinating conjunctions (pero, y, o). complex = dependent clauses and subordination (aunque, porque, cuando). Reflects education level and cognitive investment.
lexical.question_formation_style	categorical	How questions are formed. punctuation_only = question mark without interrogative words ('¿Cuánto?') — very common in Spanish chat. lexical = explicit interrogatives (¿qué, cómo, cuándo). formal = inverted subject-verb or formal register.
lexical.imperative_style	categorical	How commands and requests are framed. informal_directive = tú/vos imperative (dame, hazlo). formal_directive = usted imperative (hágame el favor). polite = conditional/modal softening (¿podría...?). Stable per-author trait in hierarchical contexts.

---

temporal_evolution.* — Behavioral change over time (1 primitive)

Primitive	Kind	Description
temporal_evolution.lifecycle_phase	categorical	Auto-classified lifecycle stage from windowed within-corpus analysis. arrival_burst = first 24hr, first-window volume dominates (empirically validated against OxPayload's first 12 hours in Rutify). stable_member = low drift across the full tenure. fluctuating_member = tenure ≥24hr with median drift between stable and inflection thresholds — established noisy regulars (e.g. lamarabitch). inflection_member = long-tenure actor with a real behavioral shift in at least one window-pair. declining_member = monotonically decreasing per-window message counts. unknown = insufficient data. Window size adapts to tenure: <24hr → 2h, <7d → 12h, <30d → 1d, otherwise 7d.

---

network.* — Governance and role signals (2 primitives)

Network primitives capture the actor's structural role in the group — inferred from interaction patterns rather than content — and a bot detector. These are heuristic composites built from other primitives; treat them as candidate signals, not verdicts.

Primitive	Kind	Description
network.is_likely_bot	categorical	Heuristic bot detector. likely_bot when conversation_initiation_rate ≥ 0.95 AND attention_pattern = broadcast AND vocabulary_richness < 0.65. Validated (2026-05-03) against SangMata_beta_bot (caught) vs 11 high-volume humans (no false positives). Low-volume bots (e.g. QuotLyBot, 9 messages) sit below the fingerprint threshold. Source label declares heuristic version (e.g. #bot-heuristic-v1).
network.governance_role_signal	categorical	Heuristic role shape from interaction primitives + lifecycle. admin_pattern = init_rate ≥ 0.80, attention reciprocal, non-bot, non-arrival_burst. responder_pattern = init_rate ≤ 0.45, attention reciprocal. bot_pattern = matches is_likely_bot. regular = everything else above volume threshold. Empirically caught 4/4 high-volume Rutify admins, sebaImlI as responder, SangMata as bot. NOT a ground-truth admin label.

---

interaction.* — Messaging behavior (6 primitives)

Interaction primitives characterize how the actor participates in conversations — timing, initiation rate, and attention patterns.

Primitive	Kind	Description
interaction.response_latency_class	categorical	How quickly the actor responds to messages directed at them. immediate <30s (suggests active monitoring or automation). fast 30s-5min. normal 5-60min. slow 1-24hr. sporadic = no consistent pattern.
interaction.conversation_initiation_rate	numeric [0,1]	Thread-starting messages / total messages. High rate = the actor drives conversations.
interaction.message_burst_rate	categorical	Whether the actor sends multiple messages per turn. habitual = almost always bursts (3+ messages before any reply). single = almost always one message per turn. Tied to stylometric.linebreak_style multi_line.
interaction.active_hours_class	free_string	UTC active-hours window summary (e.g. 05:00-14:00 UTC). Free string — the window shape varies by actor and doesn't fit a closed enum.
interaction.session_duration_class	categorical	Typical session length: short <15min, medium 15-90min, long 90min-4hr, marathon >4hr. Shares the enum with behave_shell's temporal.session_duration.
interaction.attention_pattern	categorical	Reply-graph centrality shape. broadcast = sends to many, replies to few (one-to-many). focused = concentrates on a small set of interlocutors. reciprocal = balanced give-and-take.

---

content.* — Content-derived signals, EXPERIMENTAL (6 primitives)

Content primitives are derived from message text through classifiers rather than structural/timing analysis. They carry the highest risk of false positives, are brittle to vocabulary drift, and are locale-specific. An attribution engine may choose to weight these at zero until field-validated against labeled data.

Primitive	Kind	Description
content.role_signal	categorical	Locale-tuned role-vocabulary classifier. Values: admin, seller, buyer, lurker, newbie. May be moved to a separate IOC/keyword-detection layer after Rutify testing. EXPERIMENTAL
content.transactional_language	numeric [0,1]	Rate of transactional terms per message. Locale-specific; brittle to vocabulary drift. EXPERIMENTAL
content.opsec_awareness	numeric [0,1]	Rate of security-conscious phrases. HIGH FALSE-POSITIVE RISK on casual conversation about deleting files/messages. EXPERIMENTAL
content.targeting_language	array[free_string]	IOC-shaped target patterns (bank names, government portals, RUT ranges). Consider moving to a dedicated IOC layer. EXPERIMENTAL
content.boasting_pattern	categorical	Success-claim frequency: none, occasional, frequent. Corpus-dependent regex. EXPERIMENTAL
content.conflict_style	categorical	Dispute-tone classification: aggressive, defusing, appellate. Needs labelled training data. EXPERIMENTAL

---

Schema

Machine-readable JSON Schema: json/observation.schema.json

Regenerate after model changes:

python scripts/generate_schema.py

Tests

pytest tests/

Attribution recipes

attribution-recipes.md — placeholder document sketching how an external attribution engine would consume actor.observation.text.* topics to build actor profiles (credential_broker, low_skill_buyer, group_admin, etc.). Not populated yet — awaiting Rutify corpus calibration. Not part of the BEHAVE spec.

License

Code and schemas: GPL-3.0-or-later Spec prose (this file, attribution-recipes.md): CC-BY-SA-4.0