The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index; none of the 45 sources its claims cite is checked for support yet.
Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers
Open accessAbstract
Previous studies have found systematic associations between personality and individual differences in word use. Such studies have typically focused on broad associations between major personality domains and aggregate word categories, potentially masking more specific associations. Here I report the results of a large-scale analysis of personality and word use in a large sample of blogs (N=694). The size of the dataset enabled pervasive correlations with personality to be identified for a broad range of lexical variables, including both aggregate word categories and individual English words. The results replicated category-level findings from previous offline studies, identified numerous novel associations at both a categorical and single-word level, and underscored the value of complementary approaches to the study of personality and word use.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This study analyzed personality and word use in 694 blogs, finding pervasive associations between Big Five traits/facets and both LIWC categories and individual words. It replicated prior offline findings while showing that broad category-level analyses can mask or contradict narrower facet- and word-level associations.
Key findings
- Of 330 Big Five–LIWC category correlations, 145 (43.9%) were significant at p < .05 and 49 (14.8%) at p < .001; the study replicated 15 of 30 correlations from Pennebaker and King (1999) and 15 of 24 from Hirsh and Peterson (2009).
- Facet-level analyses found 28.8% of 1,980 facet–LIWC correlations significant at p < .05 and 152 (7.7%) at p < .001, with substantial heterogeneity: Extraversion and Openness showed 42 and 56 heterogeneous categories, respectively.
- Openness had far more word-level correlates than other traits: it correlated significantly (p < .001) with 393 words, whereas Neuroticism, Extraversion, and Conscientiousness each correlated with fewer than 30 words.
- Word-level analyses revealed opposite-direction associations within categories, e.g., Agreeableness correlated positively with affection words like “loves,” “love,” and “hug” (all ρ ≥ .2, p < .001) but negatively with “porn,” “gay,” and “fuck” (all ρ < −.21, p < .001).
Methods
- Naturalistic sample of 694 blogs (524 female; mean age = 36.2 years, range 18–78, SD = 11.7), recruited via Google Blog Search and email; blogs averaged 115,423 words and spanned a mean of 23.9 months.
- Personality was measured with the 50-item IPIP NEO-FFI for Big Five traits (N = 576) and the 300-item IPIP NEO-PI-R for 30 facets (N = 431); language was analyzed with LIWC 2001 across 66 categories and with normalized counts of the 5,608 most frequent words.
- Analyses used Spearman rank correlations; word-level analyses included only blogs with ≥50,000 words (N = 406), used p < .001 thresholding, and applied split-half reliability and False Discovery Rate checks.
Limitations
- The paper states likely selection bias: only a small proportion of bloggers publicly display email addresses and only a fraction agreed to participate, potentially restricting personality variance and ruling out direct comparison with the general population.
- The paper states that many correlations were modest (largest LIWC–Big Five correlation = .23; mean absolute significant correlation = .14) and that all language variables were simple word counts, ignoring context, irony, images, and higher-order semantics.
- Evident from the text: the sample was three-quarters female, included only Google Blogger blogs, relied on self-report personality, and was correlational, so causal and generalizability claims are constrained.
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Claims the paper makes
48 Own result · 22 Not checked yet · 20 Own interpretation · 11 Own method · 4 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Conversely, Artistic Interests and Emotionality failed to show the robust positive associations with Articles and Prepositions demonstrated by the other facets and total Openness scores.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unanticipated associations included correlations between Self-Consciousness and ‘sizes’ (ρ = .27), Intellect and ‘against’ (ρ = .37), Trust and ‘summer’ (ρ = .31), and Cooperation and ‘unusual’ (ρ = .3), to name a few (all of these examples survived even an extremely conservative Bonferroni correction for 5,000 comparison—i.e., p < .00001).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The analysis revealed significant heterogeneity for a large proportion of trait/category combinations (44%)5.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, Agreeableness showed a small but statistically significant positive correlation with use of Sexual words (ρ = .08, p < .05), and Extraversion, a trait often associated with increased incentive facilitation and agentic behavior (Depue & Collins, 1999), correlated negatively with several categories reflecting goal orientation and work-related achievement (Occupation, Job/work, and”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Big Five traits
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unanticipated associations included correlations between Self-Consciousness and ‘sizes’ (ρ = .27), Intellect and ‘against’ (ρ = .37), Trust and ‘summer’ (ρ = .31), and Cooperation and ‘unusual’ (ρ = .3), to name a few (all of these examples survived even an extremely conservative Bonferroni correction for 5,000 comparison—i.e., p < .00001).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Consistent with previous studies of personality and affective reactivity (Costa & McCrae, 1980; Larsen & Ketelaar, 1991), Neuroticism correlated positively with usage of several different negative emotion word categories, including Anxiety/Fear, Sadness, Anger, and total Negative Emotions (Table 1; Figure S1).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Big Five traits
What the sources say
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“inspection of the local context of “sizes” revealed that the word was most commonly used in the context of clothing sizes (e.g., “a few sizes too big”, “bras of all sizes”, “dropping dress sizes”, etc.)”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“For example, Agreeableness showed a small but statistically significant positive correlation with use of Sexual words (ρ = .08, p < .05), and Extraversion, a trait often associated with increased incentive facilitation and agentic behavior (Depue & Collins, 1999), correlated negatively with several categories reflecting goal orientation and work-related achievement (Occupation, Job/work, and”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Big Five traits
What the sources say
- Not checked yetcites Depue & Collins, 1999 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Not surprisingly, many of the facet-level results reaffirmed the domain-level results; for example, most Neuroticism facets correlated positively with negative emotion word use (Table 3, Figure S1); most Extraversion facets correlated positively with use of categories related to positive emotions and social processes (Table 3, Figure S2); and nearly all Agreeableness facets correlated negatively”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unanticipated associations included correlations between Self-Consciousness and ‘sizes’ (ρ = .27), Intellect and ‘against’ (ρ = .37), Trust and ‘summer’ (ρ = .31), and Cooperation and ‘unusual’ (ρ = .3), to name a few (all of these examples survived even an extremely conservative Bonferroni correction for 5,000 comparison—i.e., p < .00001).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 9
- It may state two separate claims as one. 35
- The quote only partly states this claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In total, the full sample contained 694 blogs (524 female; mean age = 36.2 years, range = 18 -78, sd = 11.7), though the actual sample size was smaller for some analyses because not all participants provided personality data (see below).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Method > Participants and Procedure
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the 330 different correlation coefficients between the Big Five traits and the 66 LIWC categories, 145 (43.9%) were statistically significant at p < .05, and 49 (14.8%) were statistically significant at p < .001.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Big Five traits
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
None of the sources cited for this claim have been checked yet.
In the paper
“Moreover, the results directly replicated previous findings at a rate substantially greater than chance; specifically, the present study successfully replicated 15 of 30 correlations between Big Five dimensions and LIWC categories reported by Pennebaker & King (1999), and 15 of 24 Big Five correlations reported by Hirsh & Peterson (2009).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Big Five traits
What the sources say
- Not checked yetcites Pennebaker & King (1999)
- Not checked yetcites Hirsh & Peterson (2009)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In particular, Openness correlated significantly (p < .001) with 393 words, whereas Neuroticism, Extraversion and Conscientiousness all correlated with fewer than 30 words.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the 1980 different correlation coefficients between the 30 facets and the 66 LIWC categories, (28.8%) were statistically significant at p < .05, and 152 (7.7%) were statistically significant at p < .0014.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most notably, Openness to Experience, which one might have expected to correlate positively with categories associated with emotional, intellectual, or sensory experience, was negatively correlated with 37 of the 66 LIWC categories, and positively correlated with only 4 categories.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Big Five traits
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Importantly, these dissociations could not be explained by heterogeneity in the Openness facets themselves. Intercorrelations between the six facets were all directionally positive, and were moderate or strong in most cases.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Third, the Assertiveness and Activity Level facets showed generally weaker relations with the LIWC categories than the other facets, with the notable exception that Activity Level was the only Extraversion facet to correlate positively with categories related to goal-directed and achievement-seeking behavior (School, Job/work, and Achievement).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
In the paper
“This pattern appeared to reflect a fundamental difference in language style rather than content (Chung & Pennebaker 2007); people high on Openness tended to use more Articles (ρ = .2, p < .001) and Prepositions (ρ = .17, p < .001) than people low on Openness, suggesting a potential tendency to favor high-frequency function words at the expense of the lower frequency content words that made up most”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Big Five traits
What the sources say
- Not checked yetcites Chung & Pennebaker 2007
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, Excitement-Seeking was the only Extraversion facet to correlate positively with the Negative Emotions, Anger, and Swearing categories or negatively with the Inclusive, Home, and Grooming categories.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For Neuroticism, most of the heterogeneity appeared to stem primarily from a single facet: Self-Consciousness differed from the other facets in that it showed no positive correlation with negative affect categories, and conversely, was the only Neuroticism facet to correlate negatively with categories related to interpersonal interaction (Table 3, Figure S1).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The number of heterogeneous categories was relatively low for Conscientiousness (6 categories), Neuroticism (16), and Agreeableness (15).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, the facets of Friendliness, Gregariousness, and Cheerfulness all showed consistent positive correlations with most categories related to positive affect, communality, and interpersonal interaction (e.g., Positive Emotions, Social Processes, 1st Person Plural references, Friends, Family, and Sexual words), whereas the other facets did not.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, Agreeableness correlated positively with some words in the Sexual words category (e.g., ‘loves’, ‘love’, and ‘hug’; all ρ's >= .2, p's < .001) but negatively with others (e.g., ‘porn’, ‘gay’, and ‘fuck’; all ρ's < -.21, p < .001).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Artistic Interests and Emotionality correlated positively with use of the Position Emotion, Inclusive, and Physical States categories, whereas the other four facets generally showed negative correlations.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, Artistic Interests correlated .33 with Intellect, despite the fact that the two facets showed robust correlations in opposite directions with Positive Emotion word use (ρ's = .18 and -.24, respectively; p's < .0001).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the present study, 44% of all correlations between the Big Five traits and LIWC categories were statistically significant, yet the mean absolute correlation was only .14.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A post-hoc analysis supported this supposition, as Agreeableness correlated strongly in opposite directions with two four-word categories related to love and affection (‘love’, ‘loves’, ‘loved’, and ‘loving’; ρ = .33) versus sexual behavior (‘fuck’, ‘porn’, ‘gay’, and ‘rape’; ρ = -.4), and the two 4-word categories were themselves negatively correlated (ρ= -.15, p < .01).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, 83% of participants (N = 576) had scores for the Big Five factors of personality (Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness), and 62% (N = 431) had additional facet-level scores2.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Method > Personality measures
plainn = 576- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As discussed below, such writing samples limit the types of analyses researchers can conduct, as it is generally not possible to reliably estimate usage rates for individual words, but only for aggregate categories.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that because most participants were recruited via email, the resulting sample was not truly random: bias could arise either because some people were more likely to publish their email address on their blog (a requisite for being contacted), or because some people were more likely to respond to the invitation than others.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Method > Participants and Procedure
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, data are typically gathered from participants on a small number of occasions (often just one) spanning several hours or days; such datasets cannot be used to establish whether any identified associations between personality and language remain stable over much longer periods of time (i.e., months or years), or reflect transient influences (e.g., mood).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The fact that females comprised three-quarters of the sample raised the possibility that results might be disproportionately driven by one gender; however, partial correlation analysis demonstrated that controlling for gender and age had negligible effects on the results. For virtually all analyses, > 90% of statistically significant correlations continued to show a significant correlation in the”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Method > Participants and Procedure
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It remains unclear to what extent the results of such studies generalize to less constrained real-world situations where people's personalities can influence not only how they write or talk about specific topics, but also what topics they choose to write or talk about (cf. Pennebaker, Mehl, & Niederhoffer, 2003).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Abstract
What the sources say
- Not checked yetcites Pennebaker, Mehl, & Niederhoffer, 2003 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“studies have consistently identified theoretically predicted correlations between the dimensions of Extraversion and Neuroticism and usage of words related to a variety of positive and negative emotion categories (Hirsh & Peterson, 2009; Lee, Kim, Seo, & Chung, 2007; Pennebaker & King 1999).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Abstract
What the sources say
- Not checked yetcites Hirsh & Peterson, 2009
- Not checked yetcites Lee, Kim, Seo, & Chung, 2007
- Not checked yetcites Pennebaker & King 1999
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A split-half reliability analysis (i.e., randomly dividing each participants posts into two halves, and then correlating each category's frequency across halves for all participants) confirmed this supposition: the mean split-half correlation for the 66 categories was .81 (range = .43 - .94), and only 2 categories (Anxiety and Inhibition) had correlations lower than .6.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Method > Category-based analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“To minimize this risk, interpretation of statistically significant findings was based primarily on the aggregate pattern of results with multiple categories or traits rather than on individual correlation coefficients. Additionally, the presence or absence of statistical significance is reported in key tables using a complementary False Discovery Rate criterion (FDR), which adaptively controls the”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Method > Category-based analyses
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“First, only the 5,608 words that occurred most frequently across all blogs were analyzed3; second, only blogs containing 50,000 or more words were included in the word-level analyses (N = 406).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Method > Word-based analyses
plainn = 406- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Word-level results were thresholded at p < .001 in order to minimize the incidence of false positives.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Method > Word-based analyses
plainn = 406- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“suggesting that highly self-conscious people may be more attuned and concerned with their physical appearance”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This pattern appeared to reflect a fundamental difference in language style rather than content (Chung & Pennebaker 2007); people high on Openness tended to use more Articles (ρ = .2, p < .001) and Prepositions (ρ = .17, p < .001) than people low on Openness, suggesting a potential tendency to favor high-frequency function words at the expense of the lower frequency content words that made up most”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Big Five traits
What the sources say
- Not checked yetcites Chung & Pennebaker 2007
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
None of the sources cited for this claim have been checked yet.
In the paper
“Collectively, these findings are consistent with the notion that the affiliative and agentic aspects of Extraversion are relatively distinct and have only partially overlapping correlates (Depue & Morrone-Strupinsky, 2005; Watson & Clark, 1997).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Category-based analyses: Lower-order facets
What the sources say
- Not checked yetcites Depue & Morrone-Strupinsky, 2005 source
- Not checked yetcites Watson & Clark, 1997
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The blog content analyzed was authored by the participant blogger themselves.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The analysis suggested that reliability was high to moderate (> .6) for the first 2,000 – 3,000 or so words, and somewhat lower thereafter. However, even for low-ranked words, split-half correlations generally remained above .4, a level considered acceptable for present purposes given that word-level analyses focused primarily on the aggregate pattern of associations with personality rather than”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Method > Word-based analyses
hedgedcorrelationaln = 406- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The explanation for this seemingly paradoxical finding was that the positively correlated words occurred relatively infrequently and predominantly expressed relational spatial concepts (e.g., ‘among’, ‘between’, ‘further’, and ‘under’).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Individual differences in personality have previously been linked to differences in linguistic style in laboratory and experience-sampling settings (Fast & Funder, 2008; Hirsh & Peterson, 2009; Mehl et al., 2006; Pennebaker & King 1999).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Discussion
What the sources say
- Not checked yetcites Fast & Funder, 2008
- Not checked yetcites Hirsh & Peterson, 2009
- Not checked yetcites Mehl et al., 2006 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results demonstrate that high-level associations between broad traits and aggregate word categories can mask, and in some cases even contradict, robust but relatively narrow associations.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Discussion
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In contrast, Hirsh and Peterson (2009) found fewer than 15% of tested effects to be statistically significant, yet obtained a much larger mean statistically significant r of .23.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Discussion
What the sources say
- Not checked yetcites Hirsh and Peterson (2009)
plainn = 94- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“it is reasonable to suppose that highly Open individuals use “big words” more often, effectively resulting in strong positive correlations for many individual words (but conversely, producing negative correlations for LIWC categories that tend to be made up of a relatively small number of high-frequency words).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Discussion
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it is important to note that the primary consequence of selection bias would be a restricted distribution of personality scores among self-selected participants, which would generally tend to deflate effect sizes and statistical power, leading to results that actually underestimate the magnitude of true population effects.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Discussion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Bloggers who participated had systematically different personalities from those who did not.
None of the sources cited for this claim have been checked yet.
In the paper
“The results converge with other recent findings suggesting that, contrary to popular wisdom, people do not present themselves in an idealized and overly positive way online (Turkle, 1997), and maintain online identities that reflect the way they genuinely see themselves and are seen by others (Back et al., 2010; Vazire & Gosling, 2004).”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Discussion
What the sources say
- Not checked yetcites Turkle, 1997
- Not checked yetcites Back et al., 2010 source
- Not checked yetcites Vazire & Gosling, 2004
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, although the present study explored language use at the level of both aggregate categories and individual words, all language variables were ultimately derived from simple counts of word use, and no contextual factors or higher-order semantic variables were taken into consideration.”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Bloggers were unaware that their writing would be analyzed in relation to personality at the time of writing.
“Because bloggers were free to write about any topic of their choosing and were (at the time of writing) unaware that their writing would be analyzed in relation to personality, the data provided an naturalistic window into the influence of personality on language use that could not be influenced by demand characteristics.”
The paper relies on this without support.
The blog content analyzed was authored by the participant blogger themselves.
“The contents of participants' blogs were subsequently downloaded and parsed using a set of custom scripts written in the Ruby programming language.”
The authors state this as an assumption.
Bloggers who participated had systematically different personalities from those who did not.
“It is reasonable to suppose that bloggers who participated had systematically different personalities from those who did not (e.g., they might be more Agreeable or Open).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“He identified single word results which would not have been caught with LIWC, such as ‘hug’ correlating positively with agreeableness (there is no physical affection category inLIWC), but, considering the sparse nature of words, 406 blogs does not result in comprehensive view.”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [27]
In the citing paper
“To date, this represents the largest study, by an order of magnitude, of language and personality.”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [27]
In the citing paper
“openness exhibits the most variation across conditions of other studies (for examples, see [25], [27], [65]), and its component traits are most loosely related [85].”
From Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013) · cites this paper as [27]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Method > Word-based analyses
“First, only the 5,608 words that occurred most frequently across all blogs were analyzed3”
§ Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers > Results > Word-based analyses
“I correlated bloggers' personality scores with a set of 5,068 individual words.”
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816
Life on the Screen: Identity in the Age of the Internet
Turkle, S. (1997) · Cited by 6,340
The international personality item pool and the future of public-domain personality measures
Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R., & Gough, H. G. (2006) · Journal of Research in Personality · doi:10.1016/j.jrp.2005.08.007 · Cited by 3,185
Influence of extraversion and neuroticism on subjective well-being: Happy and unhappy people
Costa, P. T., & McCrae, R. R. (1980) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.38.4.668 · Cited by 2,652 · cited 1×
“Consistent with previous studies of personality and affective reactivity (Costa & McCrae, 1980; Larsen & Ketelaar, 1991), Neuroticism correlated positively with usage of several different negative emotion word categories, including Anxiety/Fear, Sadness, Anger, and total Negative Emotions (Table 1;…”
ResultInfluence of extraversion and neuroticism on subjective well-being: Happy and unhappy people.
Costa, P. T., & McCrae, R. R. (1980) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.38.4.668 · Cited by 2,648
Psychological Aspects of Natural Language Use: Our Words, Our Selves
Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003) · Annual Review of Psychology · doi:10.1146/annurev.psych.54.101601.145041 · Cited by 2,624
Comparing correlated correlation coefficients
Meng, X.-L., Rosenthal, R., & Rubin, D. B. (1992) · Psychological Bulletin · doi:10.1037/0033-2909.111.1.172 · Cited by 2,472
Linguistic Inquiry and Word Count (LIWC)
Chung, C. K., & Pennebaker, J. W. (2012) · IGI Global eBooks · doi:10.4018/978-1-60960-741-8.ch012 · Cited by 2,215
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Koutsoumpis, A. (2024) · doi:10.5463/thesis.714 · Cited by 1
The Written Self: Decoding Personality and Sex Differences Through Explainable AIPreprint
Saeteros, D., Domínguez-Álvarez, B., Gallardo‐Pujol, D., & Ortiz-Martínez, D. (2025) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/eja7r · Cited by 1
A standardized personality lexicon for enhancing personalized human-machine interaction
Jin, T., Cai, H., Shi, X., Kou, X., Xialian, H. U., Zhong, H., Yang, Y., Jiang, J., Li, Y., & Zhang, W. (2026) · Scientific Data · doi:10.1038/s41597-026-06783-6 · Cited by 1
A Pilot Study of Comparing Social Network Behaviors between Onlies and Others
Nie, D., Yan, Z., Zhao, N., & Zhu, T. (2015) · International Journal of Cyber Behavior Psychology and Learning · doi:10.4018/ijcbpl.2015070105
Ducheneaut, N., & Yee, N. (2013) · Wiley eBooks · doi:10.1002/9781118427378.ch29
Cyber Behavior of Microblog Users: Onlies Versus OthersPreprint
Nie, D., Hao, B., Yan, Z., & Zhu, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.4621
Empirical analysis of complex social and financial networks
Kondor, D. (2015) · Eötvös Loránd Tudományegyetem · doi:10.15476/elte.2015.083 · Cited by 1
Influence diffusion detection between linked bloggers
Tan, L. K. W. (2014) · doi:10.32657/10356/62264
InsightMe: Raising Awareness of Conveyed Personality in Social Media Traces
Xu, B., Gou, L., Xu, A., Cosley, D., & Mahmud, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14787
Hacker, J. V., Piazza, A., & Kelley, T. (2017) · Lecture notes in business information processing · doi:10.1007/978-3-319-52464-1_16
A Framework for Domain-Driven Development of Personal Health Informatics Technologies
Murnane, E. L. (2017) · eCommons (Cornell University) · doi:10.7298/x4cr5rbr
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Mendieta, J., Baquerizo-Neira, G., Villavicencio, M., & Vaca, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67256-4_21
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Iatan, I. F. (2017) · Review of the Air Force Academy/Revista Academiei Forţelor Aeriene "Henri Coandă" · doi:10.19062/1842-9238.2017.15.2.2
Wisdom in Adversity: A Twitter Study of the Japanese Tsunami
Casani, P., Iso, H., Wakamiya, S., & Aramaki, E. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508253
Semi-Supervised Psychometric Scoring of Document Collections
Suyunu, B., Aycı, G., Ogretir, M., Cemgil, A. T., Üsküdarlı, S., Zeytinoglu, H., Özel, B., & Boyacı, A. (2018) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2018.00194
Using latent semantic analysis to detect non-cognitive variables of academic performance
Aalderks, D. R. (2014)
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Saloni, J., Mansi, J., Patil, P., & Sunita, S. (2019) · International Journal of Advanced engineering Management and Science · doi:10.22161/ijaems.512.7
Воронин, А. Н., Анатольевна, К. Т., Smirnov, I. V., & Станкевич, М. (2020) · Bulletin of the Moscow State Regional University · doi:10.18384/2224-0209-2020-3-1031
Xue, Y., & Zhou, Y. (2019) · International Journal of Social Media and Online Communities · doi:10.4018/ijsmoc.2019070102
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Krijestorac, H., Garg, R., & Saar‐Tsechansky, M. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3366561 · Cited by 2
How can introverts become happier? The importance of holding a relation-centered theory of happiness
신지은, Suh, E. M., Lim, N., & Kim, J.-K. (2017) · 한국심리학회지 사회및성격 · doi:10.21193/kjspp.2017.31.1.003
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Saleem, S., & Shameer, S. (2021) · UWSpace (University of Waterloo)
Exploring individual differences through network topologyPreprint
Samoilov-Katz, Y., Louzoun, Y., Muchnik, L., & Zaidel, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.13672
This Is A Lemon and This Is Not – Was uns Künstliche Intelligenz über unsere Nutzer sagen kann
Lamprecht, J., & Robra-Bissantz, S. (2021) · Forum Dienstleistungsmanagement · doi:10.1007/978-3-658-34326-2_4
How situation affects personality expression in word use : trait relevance and situation strength
Chen, J. (2021) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/151751
Retweet Prediction Based on User Behavior
Firdaus, S. N. (2021) · doi:10.32920/ryerson.14657001.v1
Carrothers, J. (2021) · UNC Charlotte electronic theses and dissertations
Text mining for online mental health state and personality assessment
Ríssola, E. A. (2021) · RERO DOC (Universität Bern)
Yuan, L., Zhang, W., & Scheibe, K. P. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.164
Dispositional Mindfulness in Context: Cultural and Individual Perspectives
Karl, J. A. (2021) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.16892419
Sigrist, J.-F. (2020) · Wiley eBooks · doi:10.1002/9781119694731.refs
Sigrist, J. (2019) · Wiley eBooks · doi:10.1002/9781119686798.refs
Melonashi, E. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-7909-0.ch074
Zehn Prinzipien professioneller Personalauswahl
Kanning, U. P. (2022) · Springer eBooks · doi:10.1007/978-3-658-34394-1_62-1 · Cited by 1
Leutner, F., Akhtar, R., & Chamorro‐Premuzic, T. (2022) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83867-559-220221003
Classical Machine Learning vs Deep Learning for Detecting Cyber-Violence in Social Media
Zarnoufi, R., & Abık, M. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-04447-2_15
Part(y) of Speech: Partisan Divides in Stylistic FramingPreprint
McMahon, R. (2018) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/dg5nx
Naqvi, A. (2020) · Wiley eBooks · doi:10.1002/9781119601906.ch13
(2013) · Routledge eBooks · doi:10.4324/9781315827629-15
The Language of the Creative Person
Ahmed, S. T. (2021) · San José State University ScholarWorks (San Jose State University) · doi:10.31979/etd.wnfw-ymf4
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Dandannavar, P., Mangalwede, S. R., & Kulkarni, P. M. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-07654-1_10
Gayoso, A. (2023) · Springer eBooks · doi:10.1007/978-981-19-7599-8_2
FFM-SVD: A Novel Approach for Personality-aware Recommender Systems
Widdeson, K., & Hadžidedić, S. (2022) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa56895.2022.10017865
Eignungsdiagnostische Methoden
Kanning, U. P. (2023) · Springer eBooks · doi:10.1007/978-3-662-64801-8_54-1
Zehn Prinzipien professioneller Personalauswahl
Kanning, U. P. (2023) · Springer eBooks · doi:10.1007/978-3-658-34388-0_62
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Lange, L. S., Engel, T., & Kock, A. (2026) · Creativity and Innovation Management · doi:10.1111/caim.70058
Hu, Y., Yu, G., & Guo, Y. (2026) · Current Psychology · doi:10.1007/s12144-026-09503-w
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van Beest, I., Stavrova, O., & Evans, A. (2022) · OSF Preprints (OSF Preprints)
Melson-Silimon, A. T., Harmata, R., LeFevre-Levy, R., Behrend, T. S., & Carter, N. T. (2022) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-64802-826-720251007
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Metadata
- Authors
- Yarkoni, T.
- Affiliations
- University of Colorado Boulder (US)
- Corresponding author
- Yarkoni, Tal
- ORCID
- Yarkoni, Tal
- Venue
- Journal of Research in Personality
- Publisher
- Elsevier BV
- Published
- 2010-06
- Volume
- 44
- Issue
- 3
- Pages
- 363-373
- ISSN
- 0092-6566 (ISSN-L)
- PMC
- PMC2885844
- OpenAlex
- W1973871257
- Cited by
- 637
- References
- 41
- Citations by year
- 2026: 54 · 2025: 40 · 2024: 41 · 2023: 39 · 2022: 57 · 2021: 73 · 2020: 56 · 2019: 62 · 2018: 37 · 2017: 44 · 2016: 32 · 2015: 29 · 2014: 25 · 2013: 22 · 2012: 15
- FWCI
- 16.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Journal of Research in Personality (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Nursing Research (F32NR012081)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Personality Traits and Psychology
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- personality, word usage, bloggers, English vocabulary, language and personality, replication
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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