IconOf.com logoKostas MavropaliasMy thoughts on Software Engineering, Deep Learning and Cyberpsychology.

Introducing Social Bits

April 26, 2011, 9 min read

I recently concluded my research project towards the MSc in Cyberpsychology, where I investigated if and how personality characteristics and learning styles of individuals can be predicted by analysing their social bits. In this article I will give the definition of social bits and present the relevant theoretical background.

The social web

Over the past few years, the Internet ecosystem has been characterised by the evolution and tremendous growth of the social web, which gave rise to hundreds of Social Networking Sites (SNSs) that cater to a wide range of interests and target audiences. The most popular SNS, Facebook, now serves more than 500 million international users that spend over 700 billion minutes per month on it, while 50% of its active users log on in any given day (Facebook, 2011). Even though Facebook holds the reins of the social web, it is not the only place where people go online to interact socially. There are alternatives similar to Facebook that are popular in certain countries (e.g. Orkut, Bebo, Hi5), and more niche websites that focus on specific aspects of online social activity. Such niche websites include Last.FM, where users can listen, share and discover music, Foursquare that allows its users to publicise their geographical activity to the web, Linkedin that acts as a communication platform for professionals, Flickr that lets its members share their photos online and Twitter, which provides a platform for the exchange of short messages.

The popularity of social networking sites has attracted the interest of scholars and researchers, making the social web a thriving environment for research and study. Examples of such research include the study of Zywica and Danowski (2008) who investigated the social enhancement and social compensation theories that describe the perceived popularity of Facebook users and the study of Cheung and Lee (2010) who found that collective intention to use a social networking site is determined by both subjective norm and social identity.

As people grow and age, they develop unique and evolving personalities (McCrae, 2000). Over time, they create new connections with other people and discontinue others. They gain new interests that can grow or diminish over time. They discover new things, communicate, interact and visit new places. A good deal of this is reflected in their online social activity, where they share, directly or indirectly, much of what is going on in their lives (Kim, Jeong & Lee, 2010). In Facebook alone, there are 900 million objects that people interact with (pages, groups, events and community pages) and the average user is connected to 80 community pages, groups and events, creating 90 pieces of content each month (Facebook, 2011). Overall, Facebook users share 30.000.000.000 content objects (web links, news stories, blog posts, notes, photo albums and more) each month, install 20.000.000 applications every day, while there are 250 million active users that engage with Facebook on external websites (Facebook, 2011). In the case of Twitter, its user-base of more than 105.000.000 million users (Watters, 2010) publishes more than one billion tweets per week (Twitter, 2011). Similar usage trends can be observed in most of the popular SNSs, highlighting the significance of the social web. A research area of particular interest is how people use different SNSs and how that activity relates to their individual personality characteristics and identities.

Social networks and personality

Most psychological research on the social web has been focused on Facebook, mainly due to its immense popularity and the richness of information that it provides for each user, followed by Twitter. Little to no psychological research on other SNSs exists. When it comes to measures of personality factors in relation to SNS use and activity, no research outside of Facebook exists to-date. Only a few studies so far have shed light on how personality factors relate with the way people use Facebook. Back et al. (2010) found out that people are not using their Facebook profiles to promote an idealised virtual identity, but to communicate their real personality. This finding is quite significant since it opens the door to further research in this area, by increasing the level of confidence on data acquired from user profiles. A follow up study done by Gosling, Augustine, Vazire, Holtzman and Gaddis (2011) observed supporting results that were in line with Back et al.’s and suggest that, rather than escaping from or compensating for their offline personality, Facebook users appear to extend their offline personalities into Facebook.

Ross et al. (2009) investigated how the Big Five personality factors relate to Facebook use, using a convenience sample of 97 college students. Overall, Ross and his colleagues found indications of personality connections with some aspects of Facebook use, but not as many as they were expecting. They state, “personality defined by the Big Five factor approach may be too broad and not be the best way to understand specific Internet behaviours”. However, their methodology consisted of a long questionnaire that queried participants on various aspects of their Facebook profiles and activity. This limits the profile aspects that one can investigate, while self-reporting might not provide an accurate and complete representation of a person’s social activity.

Amichai-Hamburger and Vinitzky (2010) did a study based on that of Ross et al. (2009), where instead of collecting self-reports, they used more objective criteria and measured user information as it was found on the Facebook profiles of a convenience sample of 237 college students. In contrast to the findings of Ross and his colleagues that found only a few significant links between personality and Facebook behaviour, Amichai-Hamburger and Vinitzky found a strong connection between the two. While Amichai-Hamburger and Vinitzky (2010) were able to find that personality is strongly connected to Facebook use, they only investigated eight aspects of the latter, namely: number of friends, number of pictures, gender, college, self-picture, birthday, number of groups and number of photo albums. The low number of aspects might have affected the (low) number of predictors of Facebook use that they found.

As the previous studies suggested, people communicate their real personality on Facebook and there is a connection between Facebook use and personality. However, the studies of Ross et al. (2009) and Amichai-Hamburger and Vinitzky (2010) were somewhat limited in certain aspects of their methodology and on the number of Facebook facets explored. Ross et al. (2009) used self-reports to collect data on a limited number of Facebook facets, while Amichai-Hamburger and Vinitzky (2010) observed only eight facets of their participants’ profile pages. Such limitations can be considered practical necessities as the researchers were pioneering research in an unexplored area. A more fine-grained and in-depth analysis of significantly more Facebook facets might show even higher levels of correlation between personality and Facebook use, along with a greater number of psychological predictors of Facebook use. Furthermore, as a previous study has suggested, the aggregation of profile data from different SNSs revealed “significantly more facets about the individual users than one can deduce from the separated profile” (Abel, Henze, Herder & Krause, 2010). Consequently, an exploration of additional social networks in relation to personality might give researchers a better understanding on how people use the social web.

Introducing social bits

As people share and publish content online they are leaving their digital traces in a number of social networking sites. My MSc thesis defined these digital traces as social bits. The term social bit has a two-fold meaning. It borrows the word bit from computer science to indicate a basic, singular piece of information and to imply its relation to the digital world. The word social highlights, emphasises and puts the word bit in the context of human society, its members and their communal activity and interactions. When put together, the definition of a social bit is as follows:

A social bit is a single, atomic data unit acquired from a person’s online social activity and indicates the presence or absence of a single feature.

Social bits can be assembled into small sets or groups to represent a single unit of information.

A unit of information that consists of a collection of social bits is called a social byte.

To reiterate, a social bit represents the smallest, basic piece of data that can be acquired from a person’s online social activity, while a social byte is a small set of social bits and represents a unit of information. Social bits and social bytes form the social data of a person’s online activity across various social networking sites. In order to help understand, clarify and distinguish the terms social data, social byte and social bit, some examples of these terms are shown in Table 1.

Table 1

Examples of social data, social bytes and social bits.

Social data Social bytes Social bits
All user activity on social networking sites like Facebook, Twitter and LinkedIn. A YouTube video shared on Facebook. type of video, ratings, number of likes, number of comments
A tweet published on Twitter. link(s) included in the message, hash-tag(s) included in the message, @ mentions, number of retweets
A check-in on Foursquare. location of check-in, date, time, links/tags in the message
A song marked as favourite on Last.FM. release date, artist, genre, mood, album

It is evident that the online social activity of people contains very many social bytes that when gathered and analysed can contain even more social bits. An important consideration for researchers is what kind of information lies within that data, and as previous studies (Amichai-Hamburger and Vinitzky, 2010; Back et al., 2010; Ross et al., 2009) suggested, various aspects of people’s personalities and identities can be encoded in that seemingly abstract data set.

My study’s investigation into social bits and social bytes across 6 SNSs (Facebook, Twitter, LinkedIn, Foursquare, Flickr, Last.FM) illustrated that personality traits and learning styles are significantly connected to SNS use, making the practical implications for intelligent and adaptive systems highly significant. My study further suggested that the newly introduced concepts of social bits and social bytes are a plausible and insightful means of approaching psychological SNS research and can provide an understanding of the inner nature of SNS use in unprecedented detail.

I will post more articles on the subject, along with more details and findings from my study, in the near future.

References

  1. Abel, F., Henze, N., Herder, E., & Krause, D. (2010). Interweaving public user profiles on the web. User Modeling, Adaptation, and Personalization, 16–27. Springer.
  2. Amichai-Hamburger, Y., & Vinitzky, G. (2010). Social network use and personality. Computers in Human Behavior, 26(6), 1289-1295. Elsevier.
  3. Back, M. D., Stopfer, J. M., Vazire, S., Gaddis, S., Schmukle, S. C., Egloff, B., et al. (2010). Facebook profiles reflect actual personality, not self-idealization. Psychological science : a journal of the American Psychological Society / APS, 21(3), 372-4.
  4. Cheung, C. M. K., & Lee, M. K. O. (2010). A theoretical model of intentional social action in online social networks. Decision Support Systems, 49(1), 24-30.
  5. Facebook (2011a). Statistics. In Facebook. Retrieved March, 10, 2011, from http://www.facebook.com/press/info.php?statistics.
  6. Gosling, S. D., Augustine, A. A., Vazire, S., Holtzman, N., & Gaddis, S. (2011). Manifestations of Personality in Online Social Networks: Self-Reported Facebook-Related Behaviors and Observable Profile Information. Cyberpsychology, Behavior, and Social Networking. Mary Ann Liebert, Inc.
  7. Kim, W., Jeong, O.-R., & Lee, S.-W. (2010). On social Web sites. Information Systems, 35(2), 215-236.
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  9. Ross, C., Orr, E. S., Sisic, M., Arseneault, J. M., Simmering, M. G., & Orr, R. R. (2009). Personality and motivations associated with Facebook use. Computers in Human Behavior, 25(2), 578-586. Elsevier.
  10. Twitter (2011). #numbers. In Twitter. Retrieved March, 17, 2011, from http://blog.twitter.com/2011/03/numbers.html.
  11. Watters, A. (2010). Just the Facts: Statistics from Twitter Chirp. In ReadWriteWeb. Retrieved March, 10, 2011, from http://www.readwriteweb.com/archives/just_the_facts_statistics_from_twitter_chirp.php.
  12. Zywica, J., & Danowski, J. (2008). The Faces of Facebookers: Investigating Social Enhancement and Social Compensation Hypotheses; Predicting Facebook™ and Offline Popularity from Sociability and Self-Esteem, and Mapping the Meanings of Popularity with Semantic Networks. Journal of Computer-Mediated Communication, 14(1), 1-34.