C4.5: programs for machine learning
C4.5: programs for machine learning
User Modeling and User-Adapted Interaction
Full Bayesian network classifiers
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A familiar face(book): profile elements as signals in an online social network
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining multiple private databases using a kNN classifier
Proceedings of the 2007 ACM symposium on Applied computing
Whose thumb is it anyway?: classifying author personality from weblog text
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Towards Inferring Sequential-Global Dimension of Learning Styles from Mouse Movement Patterns
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Personality and motivations associated with Facebook use
Computers in Human Behavior
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
The R Book
Supporting the Development of Mobile Adaptive Learning Environments: A Case Study
IEEE Transactions on Learning Technologies
All about me: Disclosure in online social networking profiles: The case of FACEBOOK
Computers in Human Behavior
Student socialization in the age of facebook
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social tagging revamped: supporting the users' need of self-promotion through persuasive techniques
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Friends only: examining a privacy-enhancing behavior in facebook
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The adaptive web
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Studying the impact of personality and group formation on learner performance
CRIWG'07 Proceedings of the 13th international conference on Groupware: design implementation, and use
Predicting personality with social media
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Extracting Emotions from Texts in E-Learning Environments
CISIS '12 Proceedings of the 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS)
Where should I go?: guiding users with cognitive limitations through mobile devices outdoors
Proceedings of the 13th International Conference on Interacción Persona-Ordenador
Personality and patterns of Facebook usage
Proceedings of the 3rd Annual ACM Web Science Conference
Sentiment analysis in Facebook and its application to e-learning
Computers in Human Behavior
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Adaptive applications may benefit from having models of users@? personality to adapt their behavior accordingly. There is a wide variety of domains in which this can be useful, i.e., assistive technologies, e-learning, e-commerce, health care or recommender systems, among others. The most commonly used procedure to obtain the user personality consists of asking the user to fill in questionnaires. However, on one hand, it would be desirable to obtain the user personality as unobtrusively as possible, yet without compromising the reliability of the model built. On the other hand, our hypothesis is that users with similar personality are expected to show common behavioral patterns when interacting through virtual social networks, and that these patterns can be mined in order to predict the tendency of a user personality. With the goal of inferring personality from the analysis of user interactions within social networks, we have developed TP2010, a Facebook application. It has been used to collect information about the personality traits of more than 20,000 users, along with their interactions within Facebook. Based on all the collected data, automatic classifiers were trained by using different machine-learning techniques, with the purpose of looking for interaction patterns that provide information about the users@? personality traits. These classifiers are able to predict user personality starting from parameters related to user interactions, such as the number of friends or the number of wall posts. The results show that the classifiers have a high level of accuracy, making the proposed approach a reliable method for predicting the user personality