Self-Report Versus Web-Log: Which One is Better to Predict Personality of Website Users?

Self-Report Versus Web-Log: Which One is Better to Predict Personality of Website Users?

Ang Li (Institute of Psychology, Chinese Academy of Sciences, Beijing, China & School of Computer and Control, University of Chinese Academy of Sciences, Beijing, China), Zheng Yan (Educational Psychology and Methodology Division, University at Albany (SUNY), Albany, NY, USA) and Tingshao Zhu (Institute of Psychology, Chinese Academy of Sciences, Beijing, China)
Copyright: © 2013 |Pages: 11
DOI: 10.4018/ijcbpl.2013100103
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Abstract

Number of studies have investigated the relationship between personality and web use behaviors on Social Network Sites (SNS), and the measurement of web use behaviors relies on self-report technique. In this study, the authors investigated the correlation between self-report and web-log measures of web use behaviors, and further examined which one would be better to predict user’s personality. There are two major findings: (a) a diverse relationship exists between self-report and web-log measures of web use behaviors; (b) web-log data is a better predictor of user’s personality than self-report data. It suggests that such two sets of data (self-report and web-log) should be treated differently, and it is suitable to predict user’s personality using web-log data.
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Introduction

In recent years, Social Network Sites (SNS) (e.g., Facebook) have become increasingly popular. According to a survey by Pew Research Center in 2011, SNS users accounted for nearly half of American adults (47%), almost twice as much as the number in 2008 (26%) (Hampton, Goulet, Rainie, & Purcell, 2011). Because of its richness and availability, SNS is regarded as an ideal platform for conducting social science research, especially for psychology research (Boyd & Ellison, 2008; Centola, 2010; Lewis, Gonzalez, & Kaufman, 2012).

In order to deeply understand the user’s preference on SNS, psychologists are interested in the relationship between psychological traits (e.g., personality, depression and loneliness) and web use behaviors (e.g., time spent online and preference for web services) on SNS. According to “Lens Model” (Brunswick, 1956), indicators of personal environment might be sensitive enough to serve as measures of occupant’s psychological features (e.g., clean and clear dormitory indicates occupant’s personality trait of conscientiousness). By examining the “behavioral residue”, indicators could be formed in cyber world (Gosling, Ko, Mannarelli, & Morris, 2002). It proposes that, if a real life behavior can predict and manifest one’s psychological features, a cyber behavior can predict and manifest one’s psychological features in the cyber world as well.

With self-report technique, a large number of studies have examined the relationship between personality and web use behaviors, which implies that personality is an influencing factor toward web use behaviors (Amichai-Hamburger, 2002; Muscanell & Guadagno, 2012). For example, Amichai-Hamburger and Ben-Artzi (2000) found that, for men, high-extraverted individuals were more likely to use leisure services, and low-neurotic individuals were more likely to use information services; whereas for women, both low-extraverted and high-neurotic individuals were more likely to use social services. Orr et al (2009) found that high-shy individuals were more likely to spend much time on Facebook and were less likely to have many friends on Facebook. Mehdizadeh (2010) found that high-narcissistic individuals were more likely to spend much time on Facebook. Besides, in recent years, a few studies have begun to measure web use behaviors by artificially collecting web-log data (e.g., recording the number of online friends on personal pages). They were intended to predict user’s personality through the web-log records. Marcus et al (2006) found that web site cues (e.g., personal information, contact information directed at visitor, photographs and external links) were valid to express user’s Big-Five personality. Yee et al (2011) found that user’s Big-Five personality could be manifested on virtual behavioral metrics (e.g., virtual geographical movement) and linguistic metrics (e.g., number of chat lines and a count of all the words in the text messages sent). Gosling et al (2011) found that one’s personality impression could be formed based on observable profile information (e.g., number of photos and number of friends in local network). Kosinski et al (2013) found that the accuracy of predicting “Openness” trait (r = .43) through digital records of behavior (Facebook Likes) was close to the criterion of test-retest reliability in traditional personality test. Thus, it suggests that user’s personality could be predicted by web use behaviors.

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