cat-linguistic-style-matching

name	cat-linguistic-style-matching
description	Use when measuring how a user's writing style converges toward or diverges from community norms, computing Linguistic Style Matching scores from function words, comparing accommodation strength across multiple communities, tracking stylistic convergence over time, or identifying which communities a user accommodates most strongly

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

Category	Examples	Why It Matters
Personal pronouns	I, he, she, they, we, you, me, him, her, them, us	Self-focus vs. other-focus; social orientation
Impersonal pronouns	it, that, this, what, anything, something, everything	Abstraction level; referential style
Articles	a, an, the	Concrete vs. abstract thinking; noun-phrase complexity
Conjunctions	and, but, or, so, because, although, while, if	Reasoning complexity; clause linking style
Prepositions	to, of, in, for, on, with, at, by, from, about	Spatial/relational framing; structural complexity
Auxiliary verbs	is, was, am, are, be, been, have, has, had, do, will, would, could, should	Tense and modality preferences
High-frequency adverbs	very, really, just, so, also, too, quite, almost, always, never	Intensity and hedging patterns
Negations	not, no, never, neither, nobody, nothing, none, cannot, don't, won't	Disagreement and exclusion patterns
Quantifiers	many, few, some, all, every, each, any, much, more, most, several	Specificity and generalization tendencies

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

Parameter	Value	Source
LSM score range	~0.50 to 1.00	Pennebaker (secretlifeofpronouns.com)
Strong accommodation	LSM >= 0.80	Ireland et al. (2011)
Moderate accommodation	0.65 <= LSM < 0.80	Gonzales, Hancock & Pennebaker (2010)
Weak/no accommodation	LSM < 0.65	Below typical conversational baseline
Minimum words per sample	50 words	Pennebaker LSM calculator requirement
Recommended words per sample	200+ words	Larger samples improve stability
Minimum user posts per community	5 posts	Below this, function-word rates are noise
Minimum community baseline posts	20 posts (excluding user)	Below this, baseline is unstable
Minimum community members for baseline	5 unique authors	Single-author baselines conflate individual style with community norm

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:

Check	Pass Condition	Fail Action
Community segmentation	Posts are tagged with community/group identifiers	Without community labels, LSM cannot be computed per-community. Report failure.
User post count per community	5+ posts in at least 2 communities	Below 5 posts: exclude that community. Below 2 communities total: cannot compare accommodation.
Community baseline size	20+ posts from other members per community	Below 20: flag baseline as "unstable" and widen confidence interpretation.
Community author diversity	5+ unique authors in baseline	Below 5: baseline reflects individual styles, not community norms. Flag prominently.
Average text length	50+ words per post average	Below 50: function-word frequencies are unreliable per-post. Aggregate all user posts within a community into one combined text before computing frequencies.
Language	Predominantly English	Non-English text invalidates the function-word category lists below.
User text volume per community	200+ total words per community (aggregated)	Below 200 total words: exclude community from analysis.

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:

Rank	Community	Composite LSM	Accommodation Level	User Words	Baseline Words	Strongest Category	Weakest Category
1	[name]	[0.XX]	[strong/moderate/weak]	[N]	[N]	[category: score]	[category: score]

Interpretation guidance:

Composite LSM	Accommodation Level	Interpretation
>= 0.80	Strong	User's function-word usage closely mirrors community norms. Suggests deep engagement or natural stylistic fit.
0.65 - 0.79	Moderate	Partial convergence. User has adopted some community style patterns but maintains distinct voice.
< 0.65	Weak / Divergent	User's style differs substantially from community norms. May indicate peripheral participation, intentional divergence, or stylistic mismatch.

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:

User's Participation Span	Recommended Window	Minimum Posts per Window
< 3 months	Weekly	3+ posts
3 months - 1 year	Bi-weekly or monthly	5+ posts
1+ years	Monthly or quarterly	5+ posts

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:

Pattern	Detection	Interpretation
Convergence	LSM increases over 3+ consecutive windows	User is adopting community style over time
Stable match	LSM varies < 0.05 across windows	User entered already matching or has fully converged
Divergence	LSM decreases over 3+ consecutive windows	User is moving away from community norms (disengagement, identity differentiation)
Oscillation	LSM alternates up/down between windows	Inconsistent engagement or topic-driven variation
Step change	LSM shifts > 0.10 between adjacent windows	Possible event-driven accommodation shift

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

Anti-Pattern	Why It Fails	Instead
Using content words for LSM	Content words reflect topic (sports, cooking), not style. Two sports fans discussing sports will match on content words regardless of accommodation	Use only the nine function-word categories: pronouns, articles, prepositions, conjunctions, auxiliary verbs, adverbs, negations, quantifiers
Computing LSM without a community baseline	Without a baseline, you are measuring the user's style against nothing. A high "score" is meaningless without a reference distribution	Always compute community baseline from other members' posts first, then measure user distance from that baseline
Including the user in their own baseline	Self-similarity inflates LSM. The user's posts matching their own posts is not accommodation	Exclude the target user from community baseline computation
Treating high LSM as "good" and low as "bad"	High LSM means stylistic similarity, not social success. Low LSM may indicate intentional divergence, unique expertise voice, or healthy non-conformity	Report LSM as descriptive ("strong/moderate/weak accommodation") not evaluative ("good/bad")
Conflating topic similarity with style matching	Two users discussing the same topic will share content words but may have very different function-word patterns	LSM already handles this by using only function words, but remind readers that LSM measures HOW people write, not WHAT they write about
Ignoring that accommodation can be unconscious	Claiming a user "chose to adapt" based on LSM alone over-interprets the data. Much function-word accommodation is automatic	Note that LSM captures stylistic similarity regardless of whether it is conscious or unconscious
Computing per-post LSM on short texts	A 15-word post might have 0% articles purely by chance. Per-post LSM on short texts is dominated by sampling noise	Aggregate all user posts within a community before computing function-word frequencies
Using a single community's baseline as universal	Different communities have genuinely different function-word norms (technical communities use more prepositions; casual communities use more personal pronouns)	Compute a separate baseline for each community
Reporting only composite LSM	The composite hides meaningful variation across categories. A user might match perfectly on pronouns but diverge on articles	Always report per-category breakdown alongside composite score

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;
}

Condition	Action
User has < 5 posts in a community	Exclude that community from analysis. Report it as "insufficient user data." Do NOT compute LSM.
User has 5-15 posts in a community	Compute LSM but flag as "low confidence." Aggregate all posts into a single text block before computing function-word frequencies. Skip temporal analysis for this community.
User has 15+ posts in a community	Full analysis including per-category breakdown. Temporal analysis possible if timestamps available and 3+ posts per window.
Community baseline has < 20 posts	Flag baseline as "unstable." Compute LSM but note that the baseline may not represent true community norms. Widen interpretive caution.
Community baseline has < 5 unique authors	Flag baseline as "individual-dominated." The "community norm" may reflect one or two prolific authors, not a genuine community style. Note prominently.
User's aggregated text < 200 words in a community	Exclude community. Function-word frequencies from < 200 words are dominated by sampling noise.
User participates in only 1 community	Cannot compute comparative accommodation ranking. Report single-community LSM with caveat that cross-community comparison is the primary value of this analysis.
User participates in only 2 communities	Compute LSM for both but note that ranking two communities provides limited insight. Report as preliminary.
No timestamps available	Skip Step 7 (temporal analysis). Report static aggregate LSM only. Note that convergence/divergence trajectories could not be assessed.
All LSM scores cluster tightly (range < 0.05)	User may have a rigid personal style that does not accommodate, or all communities may have similar function-word norms. Investigate baseline diversity before concluding low accommodation.

Common Mistakes

Mistake	Fix
Including user posts in community baseline	Always filter out the target user's posts before computing baseline frequencies
Using raw word counts instead of percentages	LSM formula requires percentages (count / total_words * 100), not raw counts
Forgetting the 0.0001 epsilon in the denominator	Without epsilon, categories where both user and community have 0% cause division-by-zero
Computing per-post LSM on posts shorter than 50 words	Aggregate all user posts per community into one combined text; compute frequencies on the aggregate
Reporting only composite LSM without category breakdown	Always include per-category scores; the composite hides which aspects of style converge vs. diverge
Comparing LSM scores from communities with very different baseline sizes	A baseline of 20 posts vs. 2,000 posts will have different stability. Note baseline sizes when comparing.
Interpreting low LSM as "bad" or "disengaged"	Low LSM means stylistic difference, not social failure. The user may have expertise voice, deliberate divergence, or natural mismatch
Claiming conscious accommodation from LSM alone	LSM measures style similarity, not intent. Note that accommodation may be unconscious.
Skipping contraction splitting in tokenization	"don't" must become "do" + "n't" to properly count auxiliary verbs and negations
Using community baselines from a different time period than user posts	If the user posted in 2020 but the baseline is from 2024, style norms may have shifted. Match temporal windows when possible.

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.