Proceedings ArticleDOI
Our Twitter Profiles, Our Selves: Predicting Personality with Twitter
Daniele Quercia,Michal Kosinski,David Stillwell,Jon Crowcroft +3 more
- pp 180-185
TLDR
It is argued that being able to predict user personality goes well beyond the initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social media.Abstract:
Psychological personality has been shown to affect a variety of aspects: preferences for interaction styles in the digital world and for music genres, for example Consequently, the design of personalized user interfaces and music recommender systems might benefit from understanding the relationship between personality and use of social media Since there has not been a study between personality and use of Twitter at large, we set out to analyze the relationship between personality and different types of Twitter users, including popular users and influentials For 335 users, we gather personality 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 `organized' (high in Conscientiousness) 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 active user, one can predict the user's five personality traits with a root-mean-squared error below 088 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 applications as it, for example, expands current studies on privacy in social mediaread more
Citations
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Journal ArticleDOI
Using Big Data and Machine Learning in Personality Measurement: Opportunities and Challenges:
TL;DR: It is expected that this paper will provide insights, guidance, and inspiration that helps personality researchers navigate the challenges and opportunities posed by using big data methods in personality measurement.
Proceedings ArticleDOI
Digital footprints: predicting personality from temporal patterns of technology use
Ted Grover,Gloria Mark +1 more
TL;DR: This work presents a novel form of digital traces for user modeling: temporal patterns of smartphone and personal computer activity and presents a machine learning method for binary classification of each Big Five personality trait using these temporal activity patterns of both computer and smartphones as model features.
Proceedings ArticleDOI
OLAP of the tweets: From modeling toward exploitation
TL;DR: A generic multidimensional model dedicated to the OLAP of tweets is proposed with some results and analyses for testing this multi-dimensional model on various data extracted from tweets.
Book ChapterDOI
Personality Prediction Based on All Characters of User Social Media Information
TL;DR: This paper conducted a Big-Five personality inventory test with 131 users of Chinese social network Sina Weibo, and crawled all of their Weibo texts and profile information and used machine learning method to successfully predict the Big- five personality of users.
Journal ArticleDOI
Population modeling with machine learning can enhance measures of mental health.
Kamalaker Dadi,Gaël Varoquaux,Gaël Varoquaux,Josselin Houenou,Josselin Houenou,Danilo Bzdok,Danilo Bzdok,Bertrand Thirion,Denis A. Engemann,Denis A. Engemann +9 more
TL;DR: In this article, the authors applied machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank.
References
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