Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions, it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Twitter profile. We will describe the type of data collected, our methods of analysis, and the machine learning techniques that allow us to successfully predict personality. We then discuss the implications this has for social media design, interface design, and broader domains.

/pdf/predicting-personality-from-twitter-2z1pyjcxa1.pdf

Predicting Personality from Twitter

Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions; it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Facebook profile. We will describe the type of data collected, our methods of analysis, and the results of predicting personality traits through machine learning. We then discuss the implications this has for social media design, interface design, and broader domains.

/pdf/predicting-personality-with-social-media-43u4lf1vpo.pdf

Predicting personality with social media

In this paper, we investigate the relationship between behavioral characteristics derived from rich smart phone data and self-reported personality traits. Our data stems from smart phones of a set of 83 individuals collected over a continuous period of 8 months. From the analysis, we show that aggregated features obtained from smart phone usage data can be indicators of the Big-Five personality traits. Additionally, we develop an automatic method to infer the personality type of a user based on cell phone usage using supervised learning. We show that our method performs significantly above chance and up to 75.9% accuracy. To our knowledge, this constitutes the first study on the analysis and classification of personality traits using smartphone data.

/pdf/who-s-who-with-big-five-analyzing-and-classifying-23iozuw7ud.pdf

Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones

In this work, we investigate the relationships between social network structure and personality; we assess the performances of different subsets of structural network features, and in particular those concerned with ego-networks, in predicting the Big-5 personality traits. In addition to traditional survey-based data, this work focuses on social networks derived from real-life data gathered through smartphones. Besides showing that the latter are superior to the former for the task at hand, our results provide a fine-grained analysis of the contribution the various feature sets are able to provide to personality classification, along with an assessment of the relative merits of the various networks exploited.

/pdf/friends-don-t-lie-inferring-personality-traits-from-social-3mp6bj5n2x.pdf

Friends don't lie: inferring personality traits from social network structure

Understanding the impact of personality traits on mobile app adoption - Insights from a large-scale field study

Motivation -- To measure computer users' personality from their use of keyboard and mouse.Research approach -- Two explorative studies were conducted. In first study a background application was executed on 20 participants' computers to record keys pressed and mouse clicks on an average of eight days. In a second Study 15 participants' completed a programming task in an hour while a background application recorded keys pressed and mouse clicks. Participants were asked to complete the short form of IPIP-NEO personality inventory afterwards. Pearson correlation analysis was done between participants' behaviour on keyboard, mouse events and personality ratings.Findings/Design -- The results suggest that some of the main traits and sub traits of personality can be measured from keyboard and mouse use. Significant correlations were found between personality main traits and sub traits and the use of keyboard and mouse. The maximum and minimum significant correlations were r (20) = 0.62, p

/pdf/measuring-personality-from-keyboard-and-mouse-use-29jbohl632.pdf

Measuring personality from keyboard and mouse use

Two key elements in the area of cognitive ergonomics are user-system performance and the user similarity. However, with the introduction of skinnable user interfaces, a technology that gives the user interface a chameleon-like ability, elements such as aesthetic, fun, and especially user individuality and identity become more important. This paper presents two explorative studies on user personality in relation to skin preferences. In the studies participants were asked to rate their preference of a set of Windows Media Player skins and to complete the BIS/BAS and the IPIP-NEO personality inventories. The results of the first study suggest colour and similarity-attraction as two possible underlying factors for the correlations found between personality traits and skin preferences. The results of the second study partly confirm these findings, however not for similar personality traits and skin types correlations.

/pdf/towards-customized-emotional-design-an-explorative-study-of-5g2g0ci4pa.pdf

Towards customized emotional design: an explorative study of user personality and user interface skin preferences

Designing domestic user interfaces for broad user populations means designing for the average user. To design for more personally intuitive interfaces detailed interactive behaviours need to be captured and described in order to better inform the design process. By utilising technologies such as interface skins, log file analysis and user interface description languages, the PROSKIN project is developing an empirical tool for quantitative capture, description and analysis of interactive behaviour in a non-invasive and situated context. The purpose of the tool is to identify user groups by distinguishing behaviour or trait which will allow designers to develop more personally relevant user interfaces. The tool itself facilitates the analyses of large datasets of users and their interactive behaviours. This will allow designers to produce interface skins for user groups of similar interactive profile and subsequently providing a less average user experience.

/pdf/avoiding-average-recording-interaction-data-to-design-for-4d1mggf9ao.pdf

Avoiding Average: Recording Interaction Data to Design for Specific User Groups

Designing user interfaces for broad user populations often means average user interfaces that do not take individual differences into consideration. The PROSKIN project is researching whether subsets of users can be identified and grouped by distinguishing interactive behaviours so that a more personally optimised interaction can be designed. This paper discusses the design and development of a research environment to support this research.

/pdf/informing-intelligent-environments-creating-profiled-user-12inivitm5.pdf

Informing intelligent environments: creating profiled user interfaces

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Papers

Measuring personality from keyboard and mouse use

Towards customized emotional design: an explorative study of user personality and user interface skin preferences

Avoiding Average: Recording Interaction Data to Design for Specific User Groups

Informing intelligent environments: creating profiled user interfaces