user communication style and personality detection research
web research findings for amp thread analysis project.
personality detection from text
big five framework (preferred over MBTI)
the big five personality traits are the most validated framework for automated personality detection:
- openness to experience - creativity, curiosity, intellectual interests
- conscientiousness (responsibility) - organization, dependability, self-discipline
- extraversion - sociability, assertiveness, positive emotions
- agreeableness - cooperation, trust, helpfulness
- neuroticism (emotional stability) - anxiety, moodiness, stress response
research from university of barcelona (saeteros et al., PLOS One 2025) shows BERT and RoBERTa models can detect these traits from text. the MBTI model has “serious limitations for automatic personality assessment” - models trained on it tend to rely on artifacts rather than real patterns.
key techniques
integrated gradients - explainable AI technique that identifies exactly which words/phrases contribute to personality predictions. allows “opening the black box” of algorithms.
contextual understanding - crucial for accuracy. words like “hate” traditionally associated with negative traits can appear in kind contexts (“i hate to see others suffer”). without context, wrong conclusions.
BERT layer hierarchy - bottom layers encode word-level info, middle layers encode syntax, top layers encode complex contextual info. layers 11-12 most useful for personality prediction.
model performance benchmarks
- personality from PRODUCED text: r ≈ 0.29 (state of art)
- personality from CONSUMED text: r ≈ 0.12 (novel approach)
- 300 facebook likes → more accurate personality prediction than spouse
communication preferences
four communication styles (LeadershipIQ research)
| style | traits | wants to hear |
|---|---|---|
| intuitive | unemotional, freeform | bottom-line, short, no time waste |
| analytical | unemotional, linear | data, facts, numbers, expertise |
| functional | emotional, linear | process, steps A→B→C→Z, control |
| personal | emotional, freeform | relationships, feelings, who’s involved |
detection signals:
- intuitive: “what’s the bottom line?”, “give me the short version”
- analytical: “where’s your data from?”, “how do we know?”
- functional: “what’s the process?”, “who does what?”
- personal: “who will be involved?”, “how do they feel?”
professions cluster: IT/Finance/Operations → analytical/intuitive; HR/Marketing/Sales → personal
decision-making patterns
behavioral indicators
drivers/doers (logical style):
- goal-oriented, independent, need space to concentrate
- struggle with: communication overhead, patience for planning
- detection: terse messages, action-oriented language, frustration with delays
guardians/le@swift_solverrs (detail-oriented style):
- meticulous, organized, diligent, risk-averse
- struggle with: speed, seeing bigger picture, potential micromanagement
- detection: longer detailed messages, many clarifying questions, perfectionism
integrators (supportive style):
- relationship-focused, empathetic, conflict-averse
- struggle with: decisiveness, formal environments
- detection: feeling words, concern for team dynamics, emotional involvement
visionaries (idea-oriented style):
- imaginative, boundary-pushing, brainstorming-focused
- struggle with: detail execution, structured processes
- detection: creative language, many ideas, less follow-through
collaboration styles
five collaborative writing patterns (Lowry et al. 2004)
- sequential - each author writes sections independently, clear boundaries
- group single - many ideate, one compiles → consistent style despite collaboration
- horizontal division - sub-documents combined by editor → may preserve individual styles
- stratified division - role-based (author/editor/reviewer)
- reactive - synchronous editing → blurred style boundaries
for amp thread analysis: most threads likely follow sequential or stratified patterns between human and AI turns.
text analytics approaches
thematic analysis (recommended)
bottom-up extraction of themes without predefined categories:
- no training data required
- combines AI automation with human validation
- captures “unknown unknowns”
linguistic markers to extract
- response time patterns - urgency, engagement level
- thread lengths - depth of engagement, complexity preference
- vocabulary complexity - expertise level, communication formality
- emotional language - sentiment, stress indicators
- question patterns - learning style, uncertainty comfort
- directive language - leadership style, collaboration mode
application to amp threads
recommended analysis dimensions
- personality proxies - map big five traits from user message patterns
- communication style - intuitive/analytical/functional/personal classification
- work style - driver/guardian/integrator/visionary indicators
- collaboration pattern - how user structures interactions with AI
practical signals
| dimension | high signal | low signal |
|---|---|---|
| extraversion | long messages, many topics | terse, single-focus |
| conscientiousness | structured requests, follow-up | scattered, abandoned threads |
| openness | exploratory queries, novel combinations | routine, repeated patterns |
| agreeableness | polite language, acknowledgment | direct, no social niceties |
| neuroticism | urgency markers, iteration, doubt | confidence, single-shot |
caveats
- small effect sizes at individual level become meaningful at scale
- context crucial - same words mean different things
- MBTI-style typing less valid than big five
- multimodal signals (timing, volume, variety) complement text analysis
sources
- saeteros et al. (2025) “text speaks louder: insights into personality from NLP” - PLOS One
- university of bristol reddit study on personality from consumed text (PMC10276193)
- LeadershipIQ communication styles research
- thematic.com text analytics approaches review
- runn.io work styles taxonomy
- togetherplatform collaboration styles framework