Personality Expression in Chinese Language Use ... › home › linqiu › publications › ... ·...

International Journal of PsychologyInternational Journal of Psychology, 2017Vol. 52, No. 6, 463–472, DOI: 10.1002/ijop.12259

Personality expression in Chinese language use

Lin Qiu1, Jiahui Lu1, Jonathan Ramsay2, Shanshan Yang1, Weina Qu3, andTingshao Zhu3

1Division of Psychology, Nanyang Technological University, Singapore2SIM University, Singapore3Institute of Psychology, Chinese Academy of Sciences, Beijing, China

T o date, little research has investigated personality expressions in languages other than English. Given that the Chineselanguage has the largest number of native speakers in the world, it is vitally important to examine the associations

between personality and Chinese language use. In this research, we analysed Chinese microblogs and identified wordcategories and factorial structures associated with personality traits. We also compared our results with previous findings inEnglish and showed that linguistic expression of personality has both universal- and language-specific aspects. Expressionof personality via content words is more likely to be consistent across languages than expression via function words. Thismakes an important step towards uncovering universal patterns of personality expression in language.

Keywords: Personality; Chinese; Language use; Linguistic analysis; Microblog; Social media.

Language use reflects important social and psychologi-cal processes (Pennebaker, Mehl, & Niederhoffer, 2003).Studies have shown ample evidence of the associationbetween personality traits and word use in a variety ofEnglish writing samples (Pennebaker & King, 1999; Pen-nebaker et al., 2003). Nonetheless, little is known aboutthe extent to which personality is reflected in the verbalcontent of other languages (Pennebaker et al., 2003). Thisissue is made more pressing by the fact that about one-fifthof the world population lives in China, and almost 1.2 bil-lion people speak Chinese as their native language (Paul,Simons, & Fennig, 2015). Compared with English (whichhas around 335 million native speakers), Chinese is muchmore widely spoken. Given these disparities and the sheernumber of Chinese speakers, understanding how Chineselanguage use is associated with personality will improveour knowledge about the psychological processes of theworld’s largest language community and make an impor-tant step towards uncovering universal patterns of person-ality expression in language.

Past research on personality and the English languagehas often utilised Linguistic Inquiry and Word Count(LIWC; Tausczik & Pennebaker, 2010): a softwareprogram that analyses writing samples to assess the fre-quency of certain words. Developed by combining gram-matical rules with the content of various psychologicalmeasurement scales, LIWC can reliably measure many

Correspondence should be addressed to Dr Lin Qiu, Division of Psychology, Nanyang Technological University, HSS-04-15, 14 Nanyang Drive,Singapore 637332. (E-mail: [email protected]).

psychological attributes including emotions, personality,thinking styles and social relationships (Tausczik &Pennebaker, 2010). LIWC counts the frequency of wordsin pre-defined categories, each of which belongs to oneof two broad groups: content words and function words(Tausczik & Pennebaker, 2010). Content words expressthe semantic meaning of language and include categoriessuch as positive emotion and social processes. Functionswords indicate the grammatical relationships betweencontent words. LIWC contains functional word cate-gories such as pronouns and articles. Both content andfunction word categories have been found to predict BigFive personality traits in English samples (Pennebaker &King, 1999; Pennebaker et al., 2003).

Despite the lack of cross-cultural research onthe associations between language and personality,considerable evidence suggests that the structure ofpersonality—particularly the Big Five—model is stableacross cultures (McCrae & Costa, 1997). The model hasbeen validated not only in Western samples but also inAsian samples including Chinese (e.g., McCrae, Costa,del Pilar, Rolland, & Parker, 1998). Therefore, it is logicalto expect that many of the linguistic markers of personal-ity traits should be similar between Chinese and English.This relationship is likely to be particularly strong in thecase of content words, as such words typically take theform of nouns, regular verbs and adjectives (Tausczik

© 2016 International Union of Psychological Science

464 QIU ET AL.

& Pennebaker, 2010) and refer to objects, activities andconcepts with a defined meaning that the majority ofthem are likely to possess equivalents across languages(Brown, 1991).

However, Chinese and English have very differentgrammatical rules. For example, a number of Chinesefunction word categories such as second person plu-ral pronoun and preposition phrase endings do not haveequivalents in the English language. In Chinese, verbsdo not have tenses. The concept of time is expressedthrough the use of adverbs as tense markers. In addi-tion, pronoun drop, a linguistic phenomenon where thesubject pronoun of a sentence such as “I” or “You” isdropped, is common in Asian languages such as Chinese,Japanese or Korean, but is not permitted in Germanic lan-guages such as English, German and Swedish (Kashima& Kashima, 1998). It is not grammatically correct to omitthe subject “I” in such a phrase as “I had dinner withmy family yesterday” in English, but such a practice isacceptable in Chinese or Japanese. Given these substan-tial grammatical differences, it is likely that the Chineselanguage features associations between grammar and per-sonality that do not exist in English and vice versa. Conse-quently, we expected that the manifestation of personalityin function words would exhibit less correspondence withEnglish than manifestation in content words, as the use offunction words is determined by highly language-specificgrammatical rules.

PRESENT STUDY

In this study, we used LIWC to analyse writing samplesfrom two groups of users on Sina Weibo, one of the largestmicroblogging platforms in China. Social media was cho-sen as the preferred medium of analysis for two reasons:Ecological validity and accessibility. Traditional researchhas primarily relied on language samples collected fromcontrolled diary studies or decontextualized laboratoryenvironments (Tausczik & Pennebaker, 2010), compro-mising the ecological validity of the resulting findings.More research is needed to examine language use in nat-uralistic settings.

Furthermore, the use of online data allows access to awealth of content generated by a large number of people ingeographically remote places. This greatly increases thescope and convenience of research while also enhancingecological validity. Scholars have argued that psycholog-ical research should harness the benefits of online datato complement traditional methods (Gosling & Mason,2015). Therefore, we opted to use online microblogs asour writing samples.

Two separate samples were recruited. Sample 1were online participants from Mainland China, andSample 2 were overseas Chinese students. The consis-tent word-personality associations found between them

allowed us to identify stable patterns of personalityexpression. In addition, we used factor analysis to iden-tify language structures associated with personality traits.We used the large sample (Sample 1) to establish thebasic factor structure, before using data from the smallsample (Sample 2) to investigate the its replicability.

METHOD

Participants

Participants in Study 1 were recruited online from main-land China. We developed a software tool and sent par-ticipation requests to 50,000 Sina Weibo users who (a)posted more than two and less than 50 microblogs everyday and (b) had been using Sina Weibo for more than 1month. The selection criteria were designed to identifyactive users and avoid accounts created for spamming pur-poses. A total of 470 Sina Weibo users (females= 292,males= 178) responded and participated in our studyfor payment of RMB30 (US$4.8) per person. This lowresponse rate was likely due to the huge amount of spamon Sina Weibo, as the frequency of solicited messagesmay desensitise people to such requests. The sample fea-tured a diverse range of ages but was unsurprisinglyskewed towards younger individuals. Many (41.5%) ofthe participants were younger than 20 years old, while afurther 38.7% of participants were between the ages of 21and 25. Participants between the ages of 26 and 30 com-prised 14.3% of the sample, while 3.2% participants werebetween the ages of 31 and 35. The remainder of the sam-ple (2.3%) were aged 36 and above.

Participants in Sample 2 were 90 Chinese studentsat a large university in Singapore (females= 67, meanage= 22.4 years, SD= 2.52). All participants wereChinese nationals currently pursuing their undergraduatedegree in Singapore. They had been in Singapore for2–4 years. They participated in our study for payment ofS$5 (US$4.03) and completed the same survey as thosewho participated in Study 1.

Measures and procedure

Participants in both Study 1 and Study 2 providedinformed consent before commencing participation.They agreed to complete a personality survey andallow us to collect their public profile information andmicroblogs on Sina Weibo. Each participant completedthe Big Five Personality Inventory (BFI; John, Donahue,& Kentle, 1991) and demographic questions (i.e., age,gender and ethnicity). The BFI is a well-establishedand widely used measure of personality. It exhibitedacceptable reliability in our study (see Table 1).

We developed a software tool and used it to downloadparticipants’ microblogs through Sina Weibo API. We


PERSONALITY AND CHINESE LANGUAGE USE 465

TABLE 1Descriptive statistics for self-ratings of personality traits in S1 and S2

S1 (n= 470) S2 (n= 90)

Mean SD Cronbach’s α Mean SD Cronbach’s α

Extraversion 3.20 .62 .69 3.11 .57 .70Agreeableness 3.63 .57 .63 3.72 .48 .63Conscientiousness 3.15 .56 .69 3.22 .58 .77Neuroticism 3.06 .63 .68 3.00 .70 .82Openness 3.61 .56 .71 3.63 .50 .76

then removed information such as reposts written by oth-ers, timestamps, geo-locations and embedded URLs, toobtain original texts written by the participants. All partic-ipants had written more than 50 microblogs. On average,each participant in Sample 1 wrote 1237.7 microblogs(SD= 841.50) within the time period of 445.61 days(SD= 161.36). Each participant in Sample 2 wrote 276.9microblogs (SD= 224.77) in a period of 565.78 days(SD= 189.46). The large difference between the averagenumber of microblogs per person is mainly due to theselection criteria in Sample 1 that required users to postmore than two microblogs every day.

We replaced commonly used emoticons with corre-sponding phrases in everyday language and used a widelyused Chinese lexical analyser ICTCLAS (Zhang, Liu,Cheng, Zhang, & Yu, 2003) to segment the writing sam-ples into words, because Chinese texts do not containword delimiters such as whitespaces. ICTCLAS uses aunified framework that includes part-of-speech tagging,disambiguation and unknown words recognition, to iden-tify individual words in a piece of Chinese text. We iden-tified 7,180,608 words in Study 1 and 392,322 words inStudy 2.

Subsequently, we used the Simplified Chinese versionof LIWC (Huang et al., 2012) to generate word frequen-cies in the two samples. Out of the 72 LIWC categories, 26categories had frequencies lower than 1% in both samplesand were removed from further analysis. All the remain-ing categories had frequencies larger than 1% in bothsamples, except one category (i.e., second singular pro-noun) had a frequency of 1.45% in Study 1 and 0.94% inStudy 2. To further ensure that the word categories that wefocused on were relatively stable, we followed the proce-dure in Qiu, Lin, Ramsay, and Yang (2012) and split eachparticipant’s writing sample into two halves by randomlyselecting half of the microblogs from the whole sampleand applied LIWC analysis to each half. We correlatedthe word frequencies and found that all the remainingcategories had an above moderate correlation coefficient(r > .3; Cohen, 1988) in Study 1, while two categories(i.e., certainty and perceptual process) had a correlationcoefficient lower than 0.3 in Study 2. We removed the twocategories from further analysis.

RESULTS

Correlations with personality

We correlated LIWC word frequencies with participants’Big Five personality traits. A total of 49 (22.27%) outof 220 correlations in Sample 1 and 19 (8.64%) out of220 correlations in Sample 2, were significant at p< .05,exceeding chance. Table 2 shows the word categoriesthat had significant correlations with at least one per-sonality trait in one sample. We added a column inthe table to show how these categories were associatedwith personality traits in previous English samples forcomparison (see the column Se in Table 2). There were43 associations between personality traits and wordcategories (21 with function word categories and 22 withcontent word categories) that were significant in at leastone sample and had the same direction in both samples.These associations suggest relatively stable personalityexpressions in two samples.

Seven (33.3%) out of the 21 associations betweenpersonality traits and function word categories repli-cated findings in English. For example, extraversion waspositively correlated with personal pronouns (includ-ing first person and second person singular pronouns),suggesting that extraverts tended to be concerned aboutpeople. This is consistent with the theoretical definitionof extraversion (Costa & McCrae, 1996) and has beenfound in English samples (Hirsh & Peterson, 2009;Oberlander & Gill, 2006; Pennebaker & King, 1999;Yarkoni, 2010). Extraversion was negatively correlatedwith impersonal pronouns, suggesting that extravertswere less likely to use language of an impersonal nature.This association had been found in English text mes-sages (Holtgraves, 2011) and tweets (Qiu et al., 2012).Extraversion was negatively associated with negations,supporting past findings (Tausczik & Pennebaker, 2010)and reflecting extraversion’s connection with reducedcognitive complexity (Costa & McCrae, 1996). Neuroti-cism negatively correlated with prepositions, replicatingpast findings in English essays (Mairesse, Walker, Mehl,& Moore, 2007).

Fourteen (66.7%) out of the 21 associations betweenpersonality traits and function word categories had not


466 QIU ET AL.

TA

BL

E2

Cor

rela

tions

betw

een

pers

onal

ityan

dLI

WC

wor

dca

tego

ries

Ext

rave

rsio

nA

gre

eab

lene

ssC

onsc

ient

ious

ness

Neu

rotic

ism

Op

enne

ssA

ge

Gen

der

Wor

dca

teg

orie

sE

xam

ple

sS

1S

2S

eS

1S

2S

eS

1S

2S

eS

1S

2S

eS

1S

2S

eS

1S

2S

1S

2

Fu

nct

ion

wo

rds

Tot

alpr

onou

ns(y

ou),

(the

y),

(it)

.09

.12

[+]5

−.0

2.0

0[+

]12−

.07

−.0

5.1

0*.0

3[+

]5.0

4.0

1[−

]12−

.31**

−.3

8**.2

1**.4

0**

Per

sona

lpro

noun

s(h

e),

(I),

(you

).1

5**.1

7[+

]4−

.03

−.0

4−

.11*

−.0

5.0

9.0

8.0

1.0

3−

.38**

−.4

1**.2

4**.3

8**

Firs

tper

son

sing

ular

(I),

(I),

(mys

elf)

.14**

.10

[+]4,

5,9,

12,1

3−

.04

−.0

2[+

]6,10

,12

−.1

4**−

.06

.08

.12

[+]1,

10,1

1,12

,13

.03

.00

[−]10

,12

−.3

0**−

.34**

.25**

.41**

Sec

ond

pers

onsi

ngul

ar(y

ou),

(you

).1

1*.2

4*[+

]12−

.05

−.0

8−

.05

.01

.08

.06

[+]13

,[−]12

.01

.08

[−]11

,12

−.3

6**−

.32**

.14**

.13

Sec

ond

pers

onpl

ural

(you

),(y

ou),

(you

)

.06

.12

−.0

7.0

3−

.15**

−.0

4.0

8−

.20

−.0

6.1

2−

.22**

−.1

4.0

8.1

2

Impe

rson

alpr

onou

ns(it

),(t

hose

)−

.11*

−.0

6[−

]4,11

.02

.14

.07

−.0

5.0

8−

.12

.06

−.0

4.0

5−

.15

.00

.27**

Spe

cial

artic

les

,,

−.0

6−

.03

.08

.17

.04

−.0

3−

.02

−.0

5−

.10*

−.0

3.1

8**.0

7−

.11*

−.0

2

Aux

iliar

yve

rbs

(may

),(s

houl

d),

(sho

uldn

’t)

−.0

8−

.09

[−]11

.01

.01

−.0

3.1

1.1

2**.0

7−

.02

−.0

1−

.11*

−.2

2*.2

7**.3

1**

Mul

tifun

ctio

nw

ords

,,

−.1

1*−

.02

.07

.17

.11*

.11

.02

−.1

6.0

7.1

1.2

0**.0

1.0

6.2

5*

Ten

sem

arke

rs(a

lread

y),

(bef

ore)

,(la

ter)

−.0

2.0

9−

.04

.22*

−.0

7.2

6*.0

5−

.11

−.0

1−

.06

−.0

9.1

0−

.01

.11

Pro

gres

sm

arke

rs(a

lread

y),

(unt

ilno

w),

(rec

ently

)−

.03

.09

−.0

8.1

9−

.10*

.23*

.09

−.0

9.0

0−

.03

−.1

1*.1

0.0

0.0

3

Pre

posi

tions

(to)

,(w

ith),

(abo

ut)

−.0

2.2

3*.1

8**.1

6.1

5**.0

9−

.08

−.2

2*[−

]5.0

8.1

2[+

]8,11

,12

.19**

.03

−.1

1*.2

2*

Pre

posi

tion

end

(in),

(abo

ve),

(unt

il)−

.01

.13

.08

.30**

.08

.14

−.0

6−

.20

−.0

8.0

4.2

4**.0

4−

.16**

−.0

7

Con

junc

tions

(and

),(b

ut),

(whe

reas

)−

.10*

.03

[+]9

.11*

−.0

1.0

7.1

4.0

4−

.02

[+]9,

11.0

9.0

5.0

7.0

1.1

0*.3

1**

Neg

atio

ns(n

o),

(not

),(n

ever

)−

.13**

−.1

8[−

]5,7,

10−

.02

.00

[−]8,

11.0

2−

.06

[−]10

,12

.03

−.0

2[+

]5,11

,12

−.0

3.0

1[−

]12−

.05

−.0

2−

.07

.13

Qua

ntifi

ers

(som

e),

(all)

,

(man

y)

−.0

5−

.09

.11*

.17

.08

−.0

6−

.03

−.0

7.0

1.0

4.3

1**.0

4−

.09*

.15

Qua

ntity

unit

,,

.07

−.0

5.1

3**.2

0.0

7.0

8−

.12*

−.1

5−

.09*

−.0

7.1

5**.4

3**−

.24**

−.1

8

Co

nte

nt

wo

rds

Com

mon

verb

s(w

alk)

,(g

o),

(see

).0

0.1

4.0

1.1

8.0

2.2

3*.0

9−

.12

[+]9

−.0

2.1

2[−

]11−

.04

−.0

2.1

6**.2

9**



TA

BL

E2

cont

inue

d

Ext

rave

rsio

nA

gre

eab

lene

ssC

onsc

ient

ious

ness

Neu

rotic

ism

Op

enne

ssA

ge

Gen

der

Wor

dca

teg

orie

sE

xam

ple

sS

1S

2S

eS

1S

2S

eS

1S

2S

eS

1S

2S

eS

1S

2S

eS

1S

2S

1S

2

Adv

erbs

(onc

e),

(ver

y),

(gra

dual

ly)

−.1

0*.0

0.0

2.0

2−

.03

.05

.15**

.08

[−]9

.03

.05

[−]11

−.0

9*−

.17

.22**

.39**

Num

bers

(one

),(h

undr

ed),

(tho

usan

d).0

4−

.04

[+]1,

7,12

.04

.07

[+]12

.10*

.09

−.0

5−

.15

[−]5

−.0

8−

.08

[−]12

.16**

.20

−.1

8**−

.21

Soc

ialp

roce

sses

(tal

k),

(acc

ept)

,(g

reet

ing)

.06

.24*

[+]3,

8,9,

10,1

1,12

.03

.07

[+]12

.04

.00

[+]2

.06

−.1

7[−

]1−

.01

.11

[−]6,

12−

.14**

−.2

9**.1

4**.2

0

Hum

ans

(adu

lt),

(bab

y),

(boy

)

−.0

7−

.08

[+]3,

8,12

.08

.16

.15**

−.0

3[+

]2,[−

]12.0

0−

.12

[+]5

,[−]8

−.0

8−

.01

[−]12

.16**

−.2

0.0

0.2

0

Affe

ctiv

epr

oces

ses

(ang

ry),

(gra

titud

e),

(dis

appo

inte

d)

.08

−.0

1[+

]1,11

−.0

5.0

2[+

]2−

.02

.00

.02

.05

−.0

3−

.12

[−]11

,12

−.2

3**−

.25*

.15**

.16

Pos

itive

emot

ion

(hap

py),

(sat

isfie

d),

(sw

eet)

.08

−.0

2[+

]5,10

,11,

12−

.03

.14

[+]2,

10,1

2.0

1.0

7[+

]10−

.03

−.1

1[−

]5,10

−.0

1−

.09

[+]8

,[−]11

,12

−.1

8**−

.23*

.14**

.19

Neg

ativ

eem

otio

n(w

orrie

d),

(ugl

y),

(ter

rible

)

.04

−.0

1[−

]10−

.06

−.1

0[−

]4,10

,12

−.0

9−

.05

[−]6,

10,1

2.1

1*.1

4[+

]3,4,

5,10

,12

−.0

7−

.15

−.2

3**−

.20

.03

.06

Cog

nitiv

epr

oces

ses

(und

erst

and)

,(c

hoos

e),

(que

stio

n)

−.1

4**.0

5.0

5.1

1.0

3.1

9[−

]12.1

0*−

.03

[+]12

−.0

2.0

6[−

]12.0

0−

.09

.10*

.30**

Insi

ght

(und

erst

and)

,(k

now

),

(rea

lise)

−.0

8.1

8−

.04

.20

−.0

4.2

8**.1

0*−

.12

.00

.05

[+]10

−.0

8.0

4.0

0.1

0

Dis

crep

ancy

(lack

),(m

ust)

,(e

xpec

t)

−.0

7−

.08

[−]8

.01

.00

[−]8

.01

.08

[−]10

,12

.11*

.10

[+]8,

12,1

3−

.03

−.0

2[−

]12−

.10*

−.2

6*.2

3**.3

5**

Ten

tativ

e(a

bout

),(u

nsur

e),

(alm

ost)

−.1

4**.0

2[−

]9,10

,12

.01

.04

.00

−.0

2[−

]12,1

3.1

2**.1

0[+

]12.0

0.0

5[+

]10−

.07

−.1

8.1

6**.3

8**

Incl

usiv

e(in

clud

e),

(nea

r),

(add

)

−.0

3.0

7[+

]5,9,

12.1

2*.1

8[+

]3,12

.00

.14

.00

−.0

4[+

]1,9,

13−

.06

.02

[+]8

.03

.04

.03

.31**

Exc

lusi

ve(c

ance

l),(b

ut),

(exc

lude

)

−.1

1*.0

3[−

]9,10

,[+

]13.0

1−

.19

[−]11

−.0

2.0

1[−

]3,10

,12

.12**

.08

[+]9,

12.0

1.0

9[+

]3,10

−.0

3−

.20

.11*

.23*


468 QIU ET AL.

TA

BL

E2

cont

inue

d

Ext

rave

rsio

nA

gre

eab

lene

ssC

onsc

ient

ious

ness

Neu

rotic

ism

Op

enne

ssA

ge

Gen

der

Wor

dca

teg

orie

sE

xam

ple

sS

1S

2S

eS

1S

2S

eS

1S

2S

eS

1S

2S

eS

1S

2S

eS

1S

2S

1S

2

Bio

logi

calp

roce

sses

(diz

zy),

(sw

eat)

,

(hug

)

.11*

.09

−.0

1.2

7*[+

]2−

.09

.13

.09

−.1

3−

.03

.10

−.0

5.0

5.2

3**.1

3

Bod

y(n

eck)

,(s

kin)

,(s

leep

).0

5.0

9[−

]5−

.07

.18

[−]3

−.1

5**.1

0[−

]3.0

8−

.13

[+]3

−.0

6.1

4−

.10*

−.0

2.1

2**.1

0

Rel

ativ

ity(p

ast)

,(c

ompa

rabl

y),

(rea

ch)

.07

.06

.10*

.34**

[+]12

.08

.19

−.0

9−

.09

−.1

0*−

.06

[−]12

.15**

.17

−.1

3**.2

2*

Mot

ion

(thr

ough

),(a

ppro

ach)

,(p

artic

ipat

e)

.05

.10

.00

.22*

[+]12

.03

.17

−.0

6−

.01

−.1

1*.0

4.0

9*.0

9−

.08

.18

Spa

ce(in

side

),(s

tree

t),

(on

stag

e)

.02

.02

.12**

.23*

[+]12

.08

.03

−.1

1*−

.09

[−]12

.01

−.0

7[−

]12.2

9**.0

7−

.17**

.09

Tim

e(p

erio

d),

(pas

t),

(aut

umn)

.06

.07

.07

.31**

[+]12

.06

.24*

[+]12

−.0

4−

.07

−.1

2*−

.04

[−]12

−.0

2.1

6−

.03

.22*

Wor

k(f

acto

ry),

(inte

rvie

w),

(sal

ary)

−.0

2.1

0[+

]2,[−

]8,12

.07

.10

.13**

.21*

[+]3

−.1

5**−

.28**

[+]8

,[−]3,

5−

.13**

.08

[−]8

.12*

.16

−.3

4**−

.20

Ach

ieve

men

t(s

kille

d),

(win

),

(mas

ter)

−.1

0*.0

7[−

]8,12

.07

.20

.22**

.30**

[+]3,

12−

.12*

−.1

9−

.04

.03

.24**

.21*

−.2

3**−

.19

Not

e:G

ende

r:1=

Mal

e,2=

Fem

ale.

Sein

dica

tes

findi

ngs

inpa

stE

nglis

hsa

mpl

es.O

nly

cate

gori

esth

atco

rrel

ate

sign

ifica

ntly

with

atle

asto

netr

aita

resh

own.

Ital

icis

edva

lues

indi

cate

corr

elat

ions

that

rem

aine

dsi

gnifi

cant

afte

rco

ntro

lling

for

age

and

gend

er.B

old

valu

esin

dica

teco

rrel

atio

nsth

atre

mai

ned

sign

ifica

ntaf

ter

cont

rolli

ngfo

rnu

mbe

rof

post

ings

.L

IWC=

Lin

guis

ticIn

quir

yan

dW

ord

Cou

nt.

*p<

.05;

**p<

.01,

two

taile

d.



been reported before. For example, conscientiousness wasnegatively associated with personal pronouns (includingfirst person singular and second person plural), suggestingthat conscientious individuals focused less on interper-sonal issues, because personal pronouns indicate atten-tional focus on people (Tausczik & Pennebaker, 2010).Conscientiousness was also associated with prepositions,a linguistic feature indicating concern with precision(Tausczik & Pennebaker, 2010), reflecting the trait defi-nition (e.g., Costa & McCrae, 1996). Openness was neg-atively related to the use of quantity unit and specialarticles. As these words are used to quantify persons orobjects, this suggests that individuals with higher degreeof openness are less likely to make quantified statements.

Seventeen (77.2%) out of the 22 associations betweenpersonality traits and content categories were consis-tent with past findings in English. Extraversion wasrelated to social processes, a relationship that has beenconsistently found in many English samples (Hirsh &Peterson, 2009; Nowson, 2006; Oberlander & Gill, 2006;Pennebaker & King, 1999; Qiu et al., 2012; Yarkoni,2010). Agreeableness was positively associated withinclusive and relativity (including space and time) words,replicating the associations found in blogs (Yarkoni,2010). Conscientiousness was associated with time,work and achievement, supporting past findings (Hirsh& Peterson, 2009; Yarkoni, 2010) and reflecting the factthat conscientious individuals are achievement-orientedand hardworking (Barrick & Mount, 1991). Neuroticismcorrelated with negative emotion words, consistent withpast findings (Hirsh & Peterson, 2009; Mairesse et al.,2007; Pennebaker & King, 1999) and reflecting thefact that individuals with higher degree of neuroticismexperience more negative emotions (McCrae & Costa,1987). Neuroticism also correlated with discrepancy,tentative and exclusive words; findings that were consis-tent with the results of previous research (e.g., Nowson,2006; Oberlander & Gill, 2006; Yarkoni, 2010; Yee,Harris, Jabon, & Bailenson, 2011). These words indicatemaking distinctions (Nowson, 2006; Pennebaker & King,1999) and suggest that emotionally unstable individualstend to make distinctions in their writings. Neuroticismnegatively correlated with work related words, support-ing the negative relationship between neuroticism andwork performance (Barrick & Mount, 1991; Nowson,2006). Openness was negatively correlated with relativityand time, replicating relations found in personal essays(Mairesse et al., 2007).

Only five (22.7%) out of the 22 associations betweenpersonality traits and content categories were new.Extraversion was correlated with biological processes,suggesting that extraverts mentioned more about topicsrelated to activities such as eating and sex than theirintroverted counterparts. Neuroticism was also nega-tively correlated with achievement words, supporting

the negative relationship between neuroticism and workperformance (Barrick & Mount, 1991).

The above results showed that while 66.7% of per-sonality expressions associated with function word cat-egories had not been found in English samples before,only 22.7% of those associated with content categorieswere new. This supported our hypothesis that personalityexpressions associated with function word categories aremore likely to be language specific than those associatedwith content categories.

Gender, age and number of postings

In our samples, females used more personal pronouns,discrepancy, tentative and filler words, consistent withprevious findings in English (Mehl & Pennebaker, 2003).In addition, older individuals used fewer social wordsand personal pronouns (including first and second sin-gular pronouns), supporting previous results and indi-cating older individuals’ more infrequent engagement insocial activities (Pennebaker & Stone, 2003). However,we found that older individuals used fewer positive andnegative emotion words. This is inconsistent with previ-ous English findings about older individuals used morepositive but fewer negative emotion words (Pennebaker& Stone, 2003).

To examine if the observed word-personality correla-tions were contingent on age and gender, we calculatedpartial correlations by controlling gender and age. Amongthe previously found correlations, 31 out of 49 (63.27%)in Sample 1, and 15 out of 19 (78.95%) in Sample 2,remained significant. This indicates that while some lin-guistic cues might reflect characteristics related to ageand gender, those remained significant were likely to bedirectly related to personality traits (see italicised corre-lations in Table 2).

We also examined if the observed word-personalitycorrelations were contingent on the number of postings.We calculated partial correlations controlling for the num-ber of postings. Among the previously found correlations,48 out of 49 in Sample 1, and 15 out of 19 in Sample 2,remained significant (see Table 2). This indicates that themajority of the observed word-personality correlationswere independent from the number of postings.

Factor analysis

Factor analysis clusters words based on their naturalco-occurrence to identify their common discourse func-tions. It has been used to reveal psychological constructsunderlying language patterns (Pennebaker et al., 2003).We conducted an exploratory factor analysis on the Sam-ple 1 data following the procedure used by Pennebakerand King (1999). First, we included word categories thathad a high correlation (r> .7) in the previous split-half


470 QIU ET AL.

TABLE 3Factor loading

Factor 1 Factor 2 Factor 3 Factor 4

CategoriesMaking

distinction ReflectionObjective

description Socialisation

Exclusive .87Conjunction .83Tentative .78Adverbs .76 .44Discrepancy .73Impersonal

pronouns.72

Tense markers .93Insight .83Interjunctions .68Assent .67 −.45Space .65Numbers .56Non-fluencies −.50Quantity unit .48Positive emotion −.47Time .45Motion .43Social processes .83Second person

singular pronouns.80

First person singularpronouns

.45

Note: Only loadings of .40 and above are shown. Negative emotion,negations and body were removed from display because they haveloadings below .40.

analysis. Second, we excluded categories that werelargely included in other categories. For example, themajority of prepositions appear in inclusive and exclusivecategories. Therefore, we removed the preposition cate-gory. Third, we removed categories that did not includespecific words (i.e., total word count). Fourth, categoriesrelated to specific topics were excluded because theyreflected personal interests rather than psychologicalprocesses. This resulted in a total of 23 word categoriesin the factor analysis. The KMO test and Bartlett’s Testof Sphericity indicated that the data were appropri-ate for factor analysis (KMO= .791, Bartlett’s Test ofSphericity p< .001). A scree analysis suggested that a4-factor solution would best fit the data. Table 3 showsthe results of factor analysis by forcing four factorswith maximum-likelihood extracted method and varimaxrotation.

The first factor (eigenvalue= 4.39) included six wordcategories with a loading of .4 or greater. They were threecognitive process categories including tentative, exclusiveand discrepancy words, and three functional categoriesincluding impersonal pronouns, adverbs and conjunc-tions. This factor was similar to the making distinctionfactor in English which included tentative, exclusive anddiscrepancy words (Pennebaker & King, 1999). There-fore, this factor was termed Making Distinction.

The second factor (eigenvalue= 3.26) had five wordcategories, including tense marker, insight, interjunction,assent and a secondary loading of adverbs. This factorsuggested thoughtful discussion of the past, present andfuture. Therefore, it was termed Reflection.

The third factor (eigenvalue= 2.44) included eightword categories. They were time, space, numbers, quan-tity unit, fewer non-fluencies, fewer positive emotions anda secondary loading of fewer assent. This factor indi-cated description of time, space and objects. It was termedObjective Description.

The fourth factor (eigenvalue= 1.95) contained socialwords, and first and second person singular pronouns.It indicated social interaction and personal attention andwas labelled Socialisation.

Factor scores were calculated using weighted sumscores of items loaded on each factor. We correlatedthe factor scores with participants’ Big Five personal-ity dimensions (see Table 4). Extraversion positivelycorrelated with Socialisation and negatively correlatedwith Making Distinction, suggesting that extraverts weremore likely to mention social activities but less likelyto indicate distinctions than introverts. Agreeablenesscorrelated with Objective Description, showing that moreagreeable individuals were more likely to mention aboutobjective matters in their writings. Conscientiousnesspositively correlated with Objective Description andnegatively correlated with Reflection, indicating thatconscientious individuals tended to talk about factualmatters rather than their own thoughts. Neuroticism waspositively associated with Making Distinction, indicatingthat higher neuroticism individuals had more attentionalfocus on differences. The negative association betweenextraversion and Making Distinction has been found inEnglish before (Pennebaker & King, 1999).

To examine the replicability of the above factorstructure, we conducted a maximum-likelihood factoranalysis with varimax rotation on Sample 2. Diagnos-tic tests indicated that a factor model was appropriatefor the data (KMO= .763, Bartlett’s Test of Sphericityp< .001). A four factor solution was indicated by thescree plot. We performed Pearson correlations betweenthe columns of responding factors in the two samples.The correlations for the four factors were: Factor 1, .83;Factor 2, .83; Factor 3, .85; Factor 4, .65 (all dfs= 23,ps< .001). Coefficients of congruence for the four factorsranged from positive .48 to .88, and off-factor coefficientsranged from −.30 to .45, with a mean of .082. Followingprocrustes rotation macro in SPSS (McCrae, Zonderman,Costa, Bond, & Paunonen, 1996), the four coefficientsof congruency were: .88 (factor 1), 0.87 (factor 2), 0.83(factor 3) and 0.74 (factor 4). These results showed thatsample 1 and sample 2 had an acceptable degree of factorcongruence.



TABLE 4Correlations between language factors and personality traits

Extraversion Agreeableness Conscientiousness Neuroticism Openness

Making distinction −.15** .06 .04 .11* .04Reflection −.01 −.06 −.09* .07 −.00Objective description −.00 .08† .10* −.08 −.06Socialisation .12* −.03 −.03 .07 −.01

†p< .10; *p< .05; **p< .01.

GENERAL DISCUSSION

The present study identified associations betweenChinese language and personality and suggests that lin-guistic expression of personality has both universal- andlanguage-specific aspects. Most fundamentally, expres-sion of personality via content words is more likely to beconsistent across languages than expression via functionwords. These findings have important implications.

Firstly, our results show that many word-personalityassociations are language independent. For example, thecorrelations between extraversion and social processes,conscientiousness and work, and neuroticism and neg-ative emotion remain consistent between Chinese andEnglish. Furthermore, the correlation coefficients allrange between 0.1 and 0.3 in both Chinese and English(e.g., Pennebaker & King, 1999), suggesting modest butconsistent effects of personality on word choice acrosslanguages. In addition, our factor analysis shows that thepsychological meaning of word categories can exhibitsome stability across languages. Although Chinese andEnglish have different grammars and vocabularies, theyboth have an underlying factor that indicates MakingDistinction.

Secondly, our study shows that there are personal-ity expressions that are specific to Chinese. In partic-ular, the majority of personality expressions associatedwith function words in Chinese have not been docu-mented in English. This is likely because function wordsdo not carry real, independent meaning and their use isstrongly influenced by the language’s grammatical rules.Due to the large linguistic difference between Chineseand English, it is unsurprising that personality expres-sions in function words would vary considerably acrossdifferent languages. In contrast, content words are imbuedwith real meaning, indicating the things people thinkand do. As universal concepts that are highly likely tobe cross-culturally applicable (Brown, 1991), the asso-ciations between content words and personality are lesslikely to manifest in a language-specific manner.

LIMITATIONS

One limitation of the present study is that these two sam-ples were drawn from different cultural contexts: Chinese

speakers in mainland China and Chinese-speaking stu-dents studying overseas. It is possible that languageusage of non-resident Chinese nationals may be influ-enced by their acculturation into the host nation, andtherefore may differ from that of Chinese individualsresiding in their home country. However, the consistentpersonality-language associations and factor structure wefound across the two samples suggest that there are per-sonality expressions in Chinese language that are stablewithout being influenced by acculturation.

Another limitation of our study pertains to the usage ofonline microblogs to study personality expression. Whileonline data provide us access to large and diverse sam-ples from different locations, they do have the limitationof being biased towards younger, more technically savvyusers, as well as possibly being affected by the uniquenorms and affordances of online communication (Gosling& Mason, 2015). In addition, the low response rate inSample 1 may indicate that our sample is not represen-tative of the entire online population. Future research isneeded to examine if our results can be generalised toother samples.

ACKNOWLEDGEMENT

This work was supported by Singapore Ministry of Edu-cation AcRF Tier 1 Grant RGT 37/13 awarded to the firstauthor.

Manuscript received April 2015Revised manuscript accepted January 2016

First published online February 2016

REFERENCES

References marked with an asterisk indicate articles included inTable 2.

Barrick, M. R., & Mount, M. K. (1991). The Big Five person-ality dimensions and job performance: A meta-analysis. Per-sonnel Psychology, 44, 1–26.

Brown, D. (1991). Human universals. New York, NY:McGraw-Hill.

Cohen, J. (1988). Statistical power analysis for the behavioralsciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Costa, P., & McCrae, R. (1996). Toward a new generation ofpersonality theories: Theoretical contexts for the five-factor


472 QIU ET AL.

model. In J. S. Wiggins (Ed.), The five factor model of per-sonality: Theoretical perspectives (pp. 51–87). New York,NY: Guilford Press.

*Gill, A., & Oberlander, J. (2003). Perception of email person-ality at zero-acquaintance. Extraversion takes care of itself;Neuroticism is a worry. Paper presented at the Proceedings ofthe 25th Annual Conference of the Cognitive Science Soci-ety. [1]

*Golbeck, J., Robles, C., & Turner, K. (2011). Predicting per-sonality with social media. In CHI’11 extended abstractson human factors in computing systems (pp. 253–262).ACM. [2]

Gosling, S., & Mason, W. (2015). Internet research in psychol-ogy. Annual Review of Psychology, 66, 877–902.

*Hirsh, J. B., & Peterson, J. B. (2009). Personality and languageuse in self-narratives. Journal of Research in Personality, 43,524–527. [3].

*Holtgraves, T. (2011). Text messaging, personality, and thesocial context. Journal of Research in Personality, 45,92–99. [4].

Huang, C.-L., Chung, C. K., Hui, N. H. H., Lin, Y.-C., Seih, Y.,Lam, B. C. P., & Pennebaker, J. W. (2012). The developmentof the Chinese linguistic inquiry and word count dictionary.Chinese Journal of Psychology, 54(2), 185–201.

John, O. P., Donahue, E. M., & Kentle, R. L. (1991). TheBig Five inventory – versions 4a and 54. Berkeley, CA:University of California.

Kashima, E. S., & Kashima, Y. (1998). Culture and languagethe case of cultural dimensions and personal pronoun use.Journal of Cross-Cultural Psychology, 29(3), 461–486.

*Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K.(2007). Using linguistic cues for the automatic recognitionof personality in conversation and text. Journal of ArtificialIntelligence Research, 30, 457–50. [5].

McCrae, R. R., & Costa, P. T. (1987). Validation of thefive-factor model of personality across instruments andobservers. Journal of Personality and Social Psychology,52(1), 81–90.

McCrae, R. R., & Costa, P. T., Jr. (1997). Personality traitstructure as a human universal. American Psychologist, 52,509–516.

McCrae, R. R., Costa, P. T., Jr., del Pilar, G. H., Rolland, J.P., & Parker, W. D. (1998). Cross-cultural assessment of thefive-factor model: The Revised NEO Personality Inventory.Journal of Cross-Cultural Psychology, 29, 171–188.

McCrae, R. R., Zonderman, A. B., Costa, P. T., Jr., Bond, M. H.,& Paunonen, S. V. (1996). Evaluating replicability of factorsin the Revised NEO Personality Inventory: Confirmatory fac-tor analysis versus procrustes rotation. Journal of Personalityand Social Psychology, 70(3), 552–566.

*Mehl, M. R., Gosling, S. D., & Pennebaker, J. W. (2006). Per-sonality in its natural habitat: Manifestations and implicitfolk theories of personality in daily life. Journal of Personal-ity and Social Psychology, 90, 862–877. [6].

Mehl, M. R., & Pennebaker, J. W. (2003). The sounds of sociallife: A psychometric analysis of students’ daily social envi-ronments and natural conversations. Journal of Personalityand Social Psychology, 84(4), 857–870.

*Nguyen, T., Phung, D., Adams, B., & Venkatesh, S. (2011).Towards discovery of influence and personality traits throughsocial link prediction. Paper presented at the 5th internationalAAAI conference on weblog and social media. [7].

*Nowson, S. (2006). The language of weblogs: A study of genreand individual differences. Unpublished doctoral disserta-tion, University of Edinburgh, Edinburgh, UK. [8].

*Oberlander, J., & Gill, A. J. (2006). Language with character:A stratified corpus comparison of individual differences ine-mail communication. Discourse Processes, 42, 239–27.[9].

Paul, L. M., Simons, G. F., & Fennig, C. D. (2015). Ethnologue:Languages of the world. Dallas, TX: SIL International.

*Pennebaker, J. W., & King, L. A. (1999). Linguis-tic styles: Language us as an individual difference.Journal of Personality and Social Psychology, 77(6),1296–1312. [10].

Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G.(2003). Psychological aspects of natural language use: Ourwords, our selves. Annual Review of Psychology, 54(1),547–577.

Pennebaker, J. W., & Stone, L. D. (2003). Words of wisdom:Language use over the life span. Journal of Personality andSocial Psychology, 85, 291–301.

*Qiu, L., Lin, H., Ramsay, J., & Yang, F. (2012). You are whatyou tweet: Personality expression and perception on twitter.Journal of Research in Personality, 46, 710–718. [11].

Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychologicalmeaning of words: LIWC and computerized text analysismethods. Journal of Language and Social Psychology, 29(1),24–54.

*Yarkoni, T. (2010). Personality in 100,000 words: A large-scaleanalysis of personality and word use among bloggers. Jour-nal of Research in Personality, 44, 363–373. [12].

*Yee, N., Harris, H., Jabon, M., & Bailenson, J. N. (2011). Theexpression of personality in virtual worlds. Social Psycho-logical and Personality Science, 2(1), 5–12. [13].

Zhang, H. P., Liu, Q., Cheng, X. Q., Zhang, H., & Yu, H. K.(2003). Chinese lexical analysis using hierarchical hiddenmarkov model. Proceedings of the second SIGHAN work-shop on Chinese language processing-Volume 17 (63–70).


Personality Expression in Chinese Language Use ... › home › linqiu › publications › ... ·...

Documents

Transcript of Personality Expression in Chinese Language Use ... › home › linqiu › publications › ... ·...