BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Our Twitter Profiles\, Our Selves: Personality and Use of Language
  - Daniele Quercia (University of Cambridge)
DTSTART:20111006T150000Z
DTEND:20111006T160000Z
UID:TALK32892@talks.cam.ac.uk
CONTACT:Eiko Yoneki
DESCRIPTION:We tested whether Twitter users can be reduced to look-alike n
 odes (as most of the spreading models would assume) or\, instead\, whether
  they show individual differences that impact their popularity and influen
 ce. One aspect that may differentiate users is their character and persona
 lity. The problem is that personality is difficult to observe and quantify
  on Twitter. It has been shown\, however\, that personality is linked to w
 hat is unobtrusively observable in tweets: the use of language. We thus ca
 rry out a study of tweets and show that popular and influential users ling
 uistically structure their tweets in specific ways. This suggests that the
  popularity and influence of a Twitter account cannot be simply traced bac
 k to the graph properties of the network within which it is embedded\, but
  also depends on the personality and emotions of the human being behind it
 . Also\, for a limited number of 335 users\, we are able to gather persona
 lity data\, analyze it\, and find that both popular users and influentials
  are extroverts and emotionally stable (low in the trait of Neuroticism). 
 Interestingly\, we also find that popular users are "imaginative" (high in
  Openness)\, while influentials tend to be "organised" (high in Conscienti
 ousness). We then show a way of accurately predicting a user's personality
  simply based on three counts publicly available on profiles: following\, 
 followers\, and listed counts. Knowing these three quantities about an act
 ive user\, one can predict the user's five personality traits with a root-
  mean-squared error below 0.88 on a [1\,5] scale. Based on these promising
  results\, we argue that being able to predict user personality goes well 
 beyond our initial goal of informing the design of new personalized applic
 ations as it\, for example\, expands current studies on privacy in social 
 media.
LOCATION:FW26\, Computer Laboratory\, William Gates Builiding
END:VEVENT
END:VCALENDAR