Our Twitter Profiles, Our Selves: Predicting Personality with Twitter Daniele Quercia, Michal Kosinski, David Stillwell, Jon Crowcroft COMP4332 Wong 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) ▪ Age Why? ▪ 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 – for Listener – 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: ▪ TIME – Conscientiousness: 0.18 – Extraversion: 0.25 – Neuroticism: – 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