Cat Linguistic Style Matching

Communication Accommodation Theory (CAT) and Linguistic Style Matching

Overview

Measure how a user converges toward community linguistic norms by comparing their function-word usage against community baselines via Linguistic Style Matching (LSM). The core principle: people unconsciously adjust their function-word usage to match the groups they engage with, and the degree of this adjustment -- measured as LSM -- reveals which communities exert the strongest stylistic pull on a user. Function words (pronouns, articles, prepositions, conjunctions, auxiliary verbs, adverbs, negations, quantifiers) are the signal because they reflect cognitive style rather than topic -- two people discussing completely different subjects will still converge on function-word usage if they are accommodating to each other.

Theoretical foundation: Communication Accommodation Theory (Giles, 1973; Giles & Ogay, 2007) predicts that individuals converge their communication style toward groups they identify with and diverge from groups they distance from. Linguistic Style Matching (Ireland et al., 2011; Gonzales, Hancock & Pennebaker, 2010) operationalizes this convergence through function-word similarity, using the formula developed by Pennebaker and colleagues. The key insight from Pennebaker's research is that function words are produced largely automatically and are resistant to conscious manipulation, making them a more reliable indicator of accommodation than content words.

LSM score range: Scores range from approximately 0.50 to 1.00, where higher values indicate greater stylistic convergence. An LSM of 1.00 means identical function-word distributions; 0.50 indicates near-random similarity. In practice, scores above 0.80 suggest strong accommodation, 0.65-0.80 moderate accommodation, and below 0.65 weak or no accommodation.

When to Use

• Quantifying how strongly a user adapts their writing style to different communities
• Ranking communities by the degree of linguistic accommodation a user exhibits
• Tracking whether a user's style converges toward or diverges from a community over time
• Identifying "linguistic home" communities where a user's style most closely matches norms
• Comparing accommodation patterns across multiple communities to find style-adoption targets
• Establishing function-word baselines for community writing norms
• Feeding accommodation scores into archetype or persona analysis

When NOT to use:

• Corpus contains fewer than 50 words per text sample (LSM requires minimum 50 words per sample; Pennebaker recommends more)
• User has fewer than 5 posts in a community (function-word frequencies are unstable)
• Community baseline has fewer than 20 posts from other members (baseline is unreliable)
• Text is machine-translated (translation alters function-word distributions)
• Inferring social motivation or group loyalty from LSM alone (LSM measures style similarity, not intent)
• Non-English text without validated function-word categories for that language

digraph cat_decision {
    rankdir=TB;
    "User has 5+ posts\nin community?" [shape=diamond];
    "Community baseline\nhas 20+ posts?" [shape=diamond];
    "Average post\nlength 50+ words?" [shape=diamond];
    "English text?" [shape=diamond];
    "Proceed with\nfull LSM analysis" [shape=box];
    "Report insufficient\nuser data" [shape=box];
    "Report insufficient\nbaseline data" [shape=box];
    "Flag short texts;\nwiden confidence bands" [shape=box];
    "STOP: Function-word\nlists not validated" [shape=box];

    "User has 5+ posts\nin community?" -> "Community baseline\nhas 20+ posts?" [label="yes"];
    "User has 5+ posts\nin community?" -> "Report insufficient\nuser data" [label="no"];
    "Community baseline\nhas 20+ posts?" -> "Average post\nlength 50+ words?" [label="yes"];
    "Community baseline\nhas 20+ posts?" -> "Report insufficient\nbaseline data" [label="no"];
    "Average post\nlength 50+ words?" -> "English text?" [label="yes"];
    "Average post\nlength 50+ words?" -> "Flag short texts;\nwiden confidence bands" [label="no, but 20+"];
    "English text?" -> "Proceed with\nfull LSM analysis" [label="yes"];
    "English text?" -> "STOP: Function-word\nlists not validated" [label="no"];
}

Quick Reference

The Nine Function-Word Categories for LSM

LSM Formula

For each function-word category c, the category-level LSM between user u and community k is:

LSM_c(u, k) = 1 - ( |freq_c(u) - freq_c(k)| / (freq_c(u) + freq_c(k) + 0.0001) )

Where:

• freq_c(u) = percentage of user's words in category c (within community k)
• freq_c(k) = percentage of community's words in category c (excluding the user)
• 0.0001 prevents division by zero

Composite LSM is the average of the nine category-level LSM scores:

LSM(u, k) = (1/9) * SUM over c in {9 categories} of LSM_c(u, k)

Key Thresholds

Workflow

Copy this checklist and track progress:

CAT Linguistic Style Matching Progress:
- [ ] Step 1: Validate corpus suitability and segment by community
- [ ] Step 2: Build function-word dictionaries
- [ ] Step 3: Compute community baselines (excluding target user)
- [ ] Step 4: Compute user function-word frequencies per community
- [ ] Step 5: Calculate LSM scores (per-category and composite)
- [ ] Step 6: Rank communities by accommodation strength
- [ ] Step 7: Temporal accommodation analysis (if timestamps available)
- [ ] Step 8: Write findings to docs/analysis/17-cat-linguistic-style-matching.md

Step 1: Validate Corpus Suitability and Segment by Community

Before computing LSM, verify the data can support the analysis.

Suitability checks:

Segmentation: Group all posts by community identifier. For each community, separate the target user's posts from all other members' posts. The community baseline must EXCLUDE the target user's posts to avoid self-matching bias.

Step 2: Build Function-Word Dictionaries

Use these validated function-word category lists derived from LIWC research (Pennebaker, Booth & Francis, 2007; Tausczik & Pennebaker, 2010):

FUNCTION_WORD_CATEGORIES = {
    'personal_pronouns': {
        'i', 'me', 'my', 'mine', 'myself',
        'you', 'your', 'yours', 'yourself', 'yourselves',
        'he', 'him', 'his', 'himself',
        'she', 'her', 'hers', 'herself',
        'we', 'us', 'our', 'ours', 'ourselves',
        'they', 'them', 'their', 'theirs', 'themselves',
        'it', 'its', 'itself',   # NOTE: some frameworks separate these as impersonal
    },
    'impersonal_pronouns': {
        'that', 'this', 'these', 'those',
        'what', 'which', 'who', 'whom', 'whose',
        'anything', 'something', 'everything', 'nothing',
        'anyone', 'someone', 'everyone', 'nobody',
        'another', 'other', 'others',
    },
    'articles': {
        'a', 'an', 'the',
    },
    'conjunctions': {
        'and', 'but', 'or', 'nor', 'so', 'yet', 'for',
        'because', 'although', 'though', 'while', 'whereas',
        'if', 'unless', 'until', 'since', 'whether',
        'as', 'than', 'that',  # subordinating uses
    },
    'prepositions': {
        'to', 'of', 'in', 'for', 'on', 'with', 'at', 'by',
        'from', 'about', 'into', 'through', 'during', 'before',
        'after', 'above', 'below', 'between', 'under', 'over',
        'against', 'along', 'among', 'around', 'behind', 'beside',
        'beyond', 'near', 'toward', 'upon', 'within', 'without',
    },
    'auxiliary_verbs': {
        'is', 'am', 'are', 'was', 'were', 'be', 'been', 'being',
        'have', 'has', 'had', 'having',
        'do', 'does', 'did',
        'will', 'would', 'shall', 'should',
        'can', 'could', 'may', 'might', 'must',
    },
    'high_freq_adverbs': {
        'very', 'really', 'just', 'so', 'also', 'too', 'quite',
        'almost', 'always', 'never', 'often', 'still', 'already',
        'even', 'only', 'now', 'then', 'here', 'there',
        'perhaps', 'maybe', 'probably', 'certainly', 'definitely',
    },
    'negations': {
        'not', 'no', 'never', 'neither', 'nor',
        'nobody', 'nothing', 'none', 'nowhere',
        "n't",  # Handle contractions: "don't" -> "do" + "n't"
        'cannot',
    },
    'quantifiers': {
        'many', 'few', 'some', 'all', 'every', 'each', 'any',
        'much', 'more', 'most', 'less', 'least', 'several',
        'enough', 'plenty', 'both', 'half', 'numerous',
    },
}

Tokenization requirements:

• Lowercase all tokens before matching (function words are case-insensitive)
• Split contractions: "don't" -> "do" + "n't"; "isn't" -> "is" + "n't"
• Remove URLs, markup, and non-word tokens before counting
• Preserve all remaining tokens for total word count (denominator)

Step 3: Compute Community Baselines

For each community k, compute the function-word frequency profile from ALL posts by members OTHER than the target user.

import re
from collections import Counter

def tokenize(text):
    """Tokenize text for function-word analysis."""
    text = re.sub(r'https?://\S+|www\.\S+', '', text)  # Remove URLs
    text = re.sub(r'<[^>]+>', '', text)                  # Remove HTML/markup
    text = text.lower()
    # Split contractions to capture negation particles
    text = re.sub(r"n't", " n't", text)
    text = re.sub(r"'s", " 's", text)
    text = re.sub(r"'re", " 're", text)
    text = re.sub(r"'ve", " 've", text)
    text = re.sub(r"'ll", " 'll", text)
    text = re.sub(r"'m", " 'm", text)
    tokens = re.findall(r"[a-z']+", text)
    return tokens

def compute_function_word_profile(texts, categories):
    """Compute function-word frequency profile from a list of texts.
    Returns dict: {category_name: percentage_of_total_words}"""
    all_tokens = []
    for text in texts:
        all_tokens.extend(tokenize(text))

    total_words = len(all_tokens)
    if total_words == 0:
        return None

    profile = {}
    for cat_name, cat_words in categories.items():
        count = sum(1 for token in all_tokens if token in cat_words)
        profile[cat_name] = (count / total_words) * 100  # percentage

    profile['_total_words'] = total_words
    return profile

def compute_community_baseline(community_posts, user_id, categories):
    """Compute community baseline EXCLUDING the target user."""
    other_posts = [p['text'] for p in community_posts if p['author'] != user_id]
    if len(other_posts) < 20:
        return None  # Insufficient baseline
    return compute_function_word_profile(other_posts, categories)

Critical: Always exclude the target user from their own community baseline. Including the user inflates LSM by measuring self-similarity.

Step 4: Compute User Function-Word Frequencies per Community

For each community the user participates in, compute their function-word profile from ONLY their posts in that community.

def compute_user_profile_per_community(all_posts, user_id, categories):
    """Compute user's function-word profile per community."""
    communities = set(p['community'] for p in all_posts if p['author'] == user_id)
    user_profiles = {}

    for community in communities:
        user_posts = [
            p['text'] for p in all_posts
            if p['author'] == user_id and p['community'] == community
        ]
        if len(user_posts) < 5:
            continue  # Insufficient user data in this community

        profile = compute_function_word_profile(user_posts, categories)
        if profile and profile['_total_words'] >= 200:
            user_profiles[community] = profile

    return user_profiles

Step 5: Calculate LSM Scores

Apply the Pennebaker LSM formula for each function-word category, then average for composite LSM.

def compute_lsm_category(user_freq, community_freq):
    """Compute LSM for a single function-word category.
    Returns value between 0 and 1."""
    return 1 - (abs(user_freq - community_freq) /
                (user_freq + community_freq + 0.0001))

def compute_lsm(user_profile, community_baseline, categories):
    """Compute composite LSM and per-category breakdown."""
    category_scores = {}
    for cat_name in categories:
        u_freq = user_profile.get(cat_name, 0)
        c_freq = community_baseline.get(cat_name, 0)
        category_scores[cat_name] = compute_lsm_category(u_freq, c_freq)

    composite = sum(category_scores.values()) / len(category_scores)
    return {
        'composite_lsm': composite,
        'category_scores': category_scores,
        'user_total_words': user_profile.get('_total_words', 0),
        'baseline_total_words': community_baseline.get('_total_words', 0),
    }

Step 6: Rank Communities by Accommodation Strength

Sort communities by composite LSM to identify where the user accommodates most and least.

Ranking table structure:

Interpretation guidance:

Per-category deviation analysis: For each community, identify which function-word categories show the greatest convergence (highest category LSM) and greatest divergence (lowest category LSM). This reveals WHICH aspects of style the user adapts vs. resists.

Step 7: Temporal Accommodation Analysis

If timestamps are available, track how LSM changes over time to detect convergence trajectories.

Temporal windowing:

import pandas as pd

def compute_temporal_lsm(user_posts, community_baseline, categories, window='M'):
    """Compute LSM over temporal windows to track convergence."""
    df = pd.DataFrame(user_posts)
    df['date'] = pd.to_datetime(df['date'])
    df = df.sort_values('date')

    windows = df.groupby(pd.Grouper(key='date', freq=window))
    trajectory = []

    for window_start, window_posts in windows:
        texts = window_posts['text'].tolist()
        if len(texts) < 3:
            continue  # Skip sparse windows

        user_profile = compute_function_word_profile(texts, categories)
        if user_profile is None or user_profile['_total_words'] < 100:
            continue

        lsm_result = compute_lsm(user_profile, community_baseline, categories)
        trajectory.append({
            'window_start': window_start,
            'composite_lsm': lsm_result['composite_lsm'],
            'post_count': len(texts),
            'word_count': user_profile['_total_words'],
            'category_scores': lsm_result['category_scores'],
        })

    return trajectory

Trajectory patterns:

If no timestamps are available: Skip Step 7 entirely. Report that temporal analysis was not possible. Compute only the static (aggregate) LSM scores from Steps 5-6.

Step 8: Write Report

Write all findings to docs/analysis/17-cat-linguistic-style-matching.md.

Report Output Template

The final report MUST be written to docs/analysis/17-cat-linguistic-style-matching.md with this structure:

# Communication Accommodation Theory: Linguistic Style Matching Analysis

## Methodology
- **Framework:** Communication Accommodation Theory (Giles, 1973) operationalized via Linguistic Style Matching (Ireland et al., 2011)
- **LSM formula:** LSM_c = 1 - (|freq_c(user) - freq_c(community)| / (freq_c(user) + freq_c(community) + 0.0001)), averaged across 9 function-word categories
- **Function-word categories:** Personal pronouns, impersonal pronouns, articles, conjunctions, prepositions, auxiliary verbs, high-frequency adverbs, negations, quantifiers
- **Corpus:** [N total user posts across K communities, date range, total word count]
- **Baseline construction:** Community baselines computed from non-user posts (minimum 20 posts, 5 unique authors per community)
- **Temporal window:** [window size if temporal analysis performed, or "N/A -- static analysis only"]

## Corpus Suitability Assessment

| Community | User Posts | User Words | Baseline Posts | Baseline Authors | Baseline Words | Status |
|-----------|-----------|------------|----------------|-----------------|----------------|--------|
| [name] | [N] | [N] | [N] | [N] | [N] | [suitable / insufficient user data / insufficient baseline / marginal] |

- **Communities analyzed:** [N of K met minimum thresholds]
- **Communities excluded:** [list with reasons]
- **Overall suitability:** [suitable / suitable with caveats / insufficient data]

## Community Accommodation Ranking

| Rank | Community | Composite LSM | Level | User Words | Baseline Words | Top Converging Category | Top Diverging Category |
|------|-----------|---------------|-------|------------|----------------|------------------------|----------------------|
| 1 | [name] | [0.XX] | [strong/moderate/weak] | [N] | [N] | [cat: 0.XX] | [cat: 0.XX] |

### Interpretation
[Narrative synthesis: which communities does the user accommodate most? What does the ranking pattern suggest about engagement depth, identity alignment, or stylistic flexibility?]

## Per-Community LSM Breakdown

### [Community Name] (Composite LSM: X.XX)

| Function-Word Category | User Rate (%) | Community Rate (%) | Category LSM | Delta |
|----------------------|--------------|-------------------|-------------|-------|
| Personal pronouns | [X.XX] | [X.XX] | [0.XX] | [+/- X.XX] |
| Impersonal pronouns | [X.XX] | [X.XX] | [0.XX] | [+/- X.XX] |
| Articles | [X.XX] | [X.XX] | [0.XX] | [+/- X.XX] |
| Conjunctions | [X.XX] | [X.XX] | [0.XX] | [+/- X.XX] |
| Prepositions | [X.XX] | [X.XX] | [0.XX] | [+/- X.XX] |
| Auxiliary verbs | [X.XX] | [X.XX] | [0.XX] | [+/- X.XX] |
| High-freq adverbs | [X.XX] | [X.XX] | [0.XX] | [+/- X.XX] |
| Negations | [X.XX] | [X.XX] | [0.XX] | [+/- X.XX] |
| Quantifiers | [X.XX] | [X.XX] | [0.XX] | [+/- X.XX] |

[Repeat for each analyzed community]

## Temporal Accommodation Trajectory (if timestamps available)

### [Community Name]

| Window | Composite LSM | Post Count | Word Count | Trend |
|--------|---------------|-----------|------------|-------|
| [date range] | [0.XX] | [N] | [N] | [converging / stable / diverging] |

### Trajectory Patterns Detected
- **Convergence:** [communities where LSM increased over time]
- **Stable match:** [communities with consistent LSM]
- **Divergence:** [communities where LSM decreased over time]
- **Step changes:** [sudden LSM shifts with window dates]

### Trajectory Interpretation
[Narrative: how has the user's accommodation changed over their participation history? Are they becoming more integrated into certain communities? Pulling away from others?]

## Cross-Community Style Profile

### User's Baseline Style (aggregate across all communities)
| Function-Word Category | User Overall Rate (%) | Range Across Communities |
|----------------------|---------------------|------------------------|
| Personal pronouns | [X.XX] | [min - max] |
| [etc.] | | |

### Style Flexibility Index
[How much does the user's function-word profile vary across communities? High variation = high accommodation flexibility. Low variation = consistent personal style regardless of community.]

## Limitations and Caveats
- LSM measures function-word similarity, not social motivation. High LSM does not prove the user deliberately accommodates; it may reflect natural stylistic fit, topic-driven convergence, or shared demographic language patterns.
- Accommodation can be conscious or unconscious (Giles & Ogay, 2007). LSM cannot distinguish between intentional style-shifting and automatic linguistic alignment.
- Small communities (< 50 baseline posts) produce unstable baselines. LSM scores for these communities should be interpreted cautiously.
- Function-word frequencies are affected by text length: very short posts (< 50 words) produce noisy per-post estimates. Aggregation mitigates this but reduces temporal granularity.
- LSM does not capture all dimensions of accommodation. Lexical choice, syntax complexity, tone, and discourse structure are not measured by function-word LSM.
- [Corpus-specific limitations from Step 1]
- These results should NOT be used to infer group loyalty, social belonging, or psychological alignment. LSM is a stylistic similarity metric, not a measure of identity.

## References
- Giles, H. (1973). Accent mobility: A model and some data. *Anthropological Linguistics*, 15, 87-105.
- Giles, H. & Ogay, T. (2007). Communication Accommodation Theory. In B.B. Whaley & W. Samter (Eds.), *Explaining Communication: Contemporary Theories and Exemplars*.
- Ireland, M.E., Slatcher, R.B., Eastwick, P.W., Scissors, L.E., Finkel, E.J., & Pennebaker, J.W. (2011). Language style matching predicts relationship initiation and stability. *Psychological Science*, 22(1), 39-44.
- Gonzales, A.L., Hancock, J.T., & Pennebaker, J.W. (2010). Language style matching as a predictor of social dynamics in small groups. *Communication Research*, 37(1), 3-19.
- Pennebaker, J.W., Booth, R.J., & Francis, M.E. (2007). *Linguistic Inquiry and Word Count: LIWC2007*. Austin, TX: LIWC.net.
- Tausczik, Y.R. & Pennebaker, J.W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. *Journal of Language and Social Psychology*, 29(1), 24-54.
- Niederhoffer, K.G. & Pennebaker, J.W. (2002). Linguistic style matching in social interaction. *Journal of Language and Social Psychology*, 21(4), 337-360.

Good Patterns

• Use function words exclusively for LSM -- content words reflect topic, not style; function words are produced automatically and reveal cognitive accommodation patterns independent of subject matter
• Exclude the target user from community baselines -- including user posts in their own baseline inflates LSM by measuring self-similarity
• Compute per-category LSM before averaging -- the composite hides which categories converge vs. diverge; per-category detail reveals what aspects of style the user adapts
• Require minimum sample sizes -- at least 50 words per text, 200+ total words per user-community pair, 20+ baseline posts, 5+ baseline authors
• Compare across multiple communities -- a single LSM score is hard to interpret; relative ranking across communities reveals accommodation patterns
• Track accommodation over time -- static LSM shows current similarity; temporal trajectory shows convergence/divergence dynamics
• Report community baseline stability -- note when baselines are small or dominated by few authors, which makes the "community norm" unreliable
• Aggregate short posts before computing frequencies -- per-post function-word rates from 20-word posts are noise; combine all user posts in a community into one aggregate

Anti-Patterns

Boundaries

This skill SHOULD produce:

• Community-specific function-word baselines from aggregate member data
• Per-category and composite LSM scores for the user in each community
• Community accommodation ranking (strongest to weakest convergence)
• Per-category deviation analysis showing which function-word types converge vs. diverge
• Temporal accommodation trajectories if timestamps are available
• Cross-community style flexibility assessment
• Written report at docs/analysis/17-cat-linguistic-style-matching.md

This skill should NOT:

• Infer social motivation, group loyalty, or belonging from LSM scores alone (LSM measures style similarity, not intent)
• Claim accommodation is always deliberate or strategic (much is unconscious)
• Use LSM as sole evidence of community membership or identity
• Ignore that small communities produce unstable baselines
• Apply the nine-category English function-word framework to non-English text without validation
• Treat LSM as a personality trait (it reflects interaction-specific style adjustment, not fixed characteristic)
• Compare LSM scores across studies or corpora with different baseline sizes (LSM magnitude is sensitive to baseline composition)
• Produce scores from fewer than 200 aggregated user words per community
• Present accommodation rankings without per-category breakdowns

Insufficient Data Handling

digraph insufficient_data {
    rankdir=TB;
    node [shape=diamond, style=filled, fillcolor="#f0f0f0"];

    user_posts [label="User posts in\ncommunity?", shape=diamond];
    few_posts [label="< 5 posts", shape=box, fillcolor="#ffcccc"];
    some_posts [label="5-15 posts", shape=box, fillcolor="#fff3cc"];
    good_posts [label="15+ posts", shape=box, fillcolor="#ccffcc"];

    user_posts -> few_posts [label="< 5"];
    user_posts -> some_posts [label="5-15"];
    user_posts -> good_posts [label="15+"];

    few_action [label="EXCLUDE community.\nReport: insufficient user data.\nDo NOT compute LSM.", shape=box, fillcolor="#ffcccc"];
    some_action [label="Compute LSM but flag as\nLOW CONFIDENCE.\nAggregate all posts into\none text block.", shape=box, fillcolor="#fff3cc"];
    good_action [label="Full LSM analysis.\nPer-category breakdown.\nTemporal if timestamps available.", shape=box, fillcolor="#ccffcc"];

    few_posts -> few_action;
    some_posts -> some_action;
    good_posts -> good_action;
}

Common Mistakes

References

• Giles, H. (1973). Accent mobility: A model and some data. Anthropological Linguistics, 15, 87-105.
• Giles, H. & Ogay, T. (2007). Communication Accommodation Theory. In B.B. Whaley & W. Samter (Eds.), Explaining Communication.
• Ireland, M.E., Slatcher, R.B., Eastwick, P.W., Scissors, L.E., Finkel, E.J., & Pennebaker, J.W. (2011). Language style matching predicts relationship initiation and stability. Psychological Science, 22(1), 39-44.
• Gonzales, A.L., Hancock, J.T., & Pennebaker, J.W. (2010). Language style matching as a predictor of social dynamics in small groups. Communication Research, 37(1), 3-19.
• Pennebaker, J.W., Booth, R.J., & Francis, M.E. (2007). Linguistic Inquiry and Word Count: LIWC2007. Austin, TX: LIWC.net.
• Tausczik, Y.R. & Pennebaker, J.W. (2010). The psychological meaning of words. Journal of Language and Social Psychology, 29(1), 24-54.
• Niederhoffer, K.G. & Pennebaker, J.W. (2002). Linguistic style matching in social interaction. Journal of Language and Social Psychology, 21(4), 337-360.
• LIWC-22 Manual: Development and Psychometrics.
• Pennebaker, J.W. Language Style Matching Calculator.
• LIWC LSM Help Documentation.
• Rains, S.A. (2025). Communication accommodation theory in quantitative research. Communication Monographs.