Our Twitter Profiles, Our Selves: Predicting Personality with Twitter

Daniele Quercia, Michal Kosinski, David Stillwell, Jon Crowcroft

COMP4332Wong Po Yan

Introduction

▪ Significant correlation between personality and real-world behavior–Music taste–Formation of social relations

▪ Predicting the personality of users in Twitter

Why Twitter?

▪ Previous study on Facebook–The nature of online interactions does not

significantly differ from that of real world interactions

▪ A different platform–See anything of anybody unless users protect their

updates

▪ Popular

Twitter Users▪ Four types with Five measures– Listeners : follow many users–Popular: are followed by many–Highly-read: are often listed in other’s reading list– Influential: ▪ Klout score

Whether a user’s tweet is being clicked, replied or retweeted▪ TIME score

TIME magazine ranking measure that combines one’s popularity on both Tweeter and Facebook using the formula

(2a + b) / 2, where a = number of Twitter followers,b = number of Facebook social contact

Personality

▪ The Big Five Personality Test–An individual is associated with fives scores that

correspond to the five main personality traits

▪ Traits–Openness–Conscientiousness–Extraversion–Agreeableness–Neuroticism

myPersonality

▪ Facebook users are able to take a variety of personality and ability test

▪ Users can give consent to share their personality scores and profile information–40%–Only few hundreds of those have posted links to

their Twitter accounts.

▪ The Big Five Personality Test

Goal

Relationship between

▪ Personality Traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism)

▪ Two additional attributes (age, sex)

And

▪ Five user characteristics (followings, followers, listings, influential score (Klout, TIME))

Data Collection

▪ Sample users: 335– Have specified their twitter accounts on Facebook profile– Have done the Big Five Personality Test using

myPersonality in Facebook– Have shared the results and profiles on Twitter

▪ Data– Number of followers– Number of following – Number of times that the user has been listed in others’

reading list

Data Processing:Logarithm

▪ Number of followed users

▪ Number of followers

▪ Listings

▪ Two influential scores (Klout, TIME)

▪ AgeWhy?

▪ Corresponding distributions are not normal

▪ Logarithm transformation accounts for the violation of normality

Pearson Product Moment Correlation

▪ A measure of the linear relationship between two random variables

▪ Formula

▪ Range: [-1, 1]

Results

Listener & Popular▪ Extraversions– 0.13 for Listener– 0.15 for Popular– Extroverts

▪ Neuroticism– -0.17 for Listener– -0.19 for Popular– Emotionally stable

▪ Age– 0.28 for Listener– 0.37 for Popular– Tend to be older

Listener and Popular are extroverts and emotionally stable. They tend to be older.

Results

Highly-read–Openness▪ 0.17

Highly-read are people who are imaginative, spontaneous and adventurous.

Results

Influential

▪ Klout– Extraversion: 0.15– Neuroticism: -0.03

▪ TIME– Conscientiousness: 0.18– Extraversion: 0.25– Neuroticism: -0.20– Age: 0.39

Influential are people who are extroverts, emotionally stable, ambitious and resourceful. They are very likely to be older.

Model for Prediction▪ Regression analysis

▪ 10-fold cross validation using M5’ Rule– M5’ is based closely on M5– M5 (Model tree) combines a conventional decision tree with

the possibility of linear regression functions at the leaves– M5’ is the enhanced algorithm that improves with handling

missing values and enumerated attributes

▪ Root Mean Square Error–Compare the difference between predicted values and

observed values–On score scale [1,5], maximum RMSE = 0.88–Error is low Accurate

Conclusion

▪ All user types are emotionally stable

▪ Most of the users are extroverts, except Highly-read people

▪ Listener, Popular and Influential people tend to be older

▪ Influential people tend to be ambitious, but seem to be not very agreeable

▪ Highly-read people tend to be adventurous and imaginative

These inferences have long been supported informally by intuition but have been difficult to make it precise.

Suggestions

▪ Marketing–Marketing strategy is closely related to consumer personality–E.g. Select ads to which the user is likely to be most receptive

▪ User Interface Design–Match the “look and feel” of a social media site to personality

traits

▪ Recommender Systems–Product recommendation–E.g. Recommend music to users under given well-established

relationship between personality and music taste

Q & A