The Credibility of Digital Identity Information on the Social Web: A User Study


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The Credibility of Digital Identity Information on the Social Web: A User Study

  1. 1. The Credibility of Digital Identity Information on the Social Web: A User Study<br />Matthew Rowe<br />Organisations, Information and Knowledge Group<br />University of Sheffield<br />
  2. 2. Outline<br />Problem: <br />Increased reliance on digital identity information<br />Motivation: <br />Need for assessing the credibility of digital identity information<br />Digital Identity<br />The tiers of identity information<br />Comparing Digital and Real-World Identities<br />User study of Social Web users<br />Findings from the Study<br />Conclusions<br />
  3. 3. Problem<br />The World Wide Web has evolved from a Web of anonymity into an accountable Web<br />Users are able to interact online with Web platforms<br />Publish their thoughts and musings on blogging platforms such as LiveJournal<br />Upload videos onto Youtube<br />Share photos on Flickr<br />Chat with friends on MySpace<br />All of the above allow users to construct bespoke online personas<br />So called “Digital Identities”<br />Shaping how they wish to be perceived by others in an online environment<br />
  4. 4. Motivation<br />Digital identity information from Social Web platforms is used by a range of 3rd party applications and services:<br />Dopplr uses social network information from user profiles to share travel arrangements and planned trips<br />Google’s Social Search uses identity information to affect search listings<br />Identity management services have begun using digital identity information Social Web platforms to support automated identity disambiguation techniques<br />Employers use Social Web platforms to perform lateral surveillance of potential and current employees<br />The increased reliance on digital identity information from Social Web platforms requires an assessment to be made of the credibility of this information<br />To what extent is real identity information mirrored in a digital space?<br />
  5. 5. Digital Identity<br />The Oxford dictionary defines identity as:<br />“the fact of being who or what a person is” and…<br />“the characteristics of defining this”<br />A person’s identity is comprised of a set of attributes which makes them unique<br />Digital Identity reflects this, however in an online space the these attributes can be customised and tailored by the user<br />The functionalities and feature sets provided by Social Web platforms facilitate this alteration<br />Users can construct profiles containing their biographical information (name, address, email) along with their social network information<br />
  6. 6. Digital Identity<br />Digital Identity can be divided into 3 tiers:<br />My Identity: persistent identity information such as name, date of birth and genealogical relations<br />Shared Identity: information which is susceptible to change such as social network information<br />Abstracted Identity: identity information derived from groupings and demographics<br />(Ploderer et al, 2008) states that self-promotion motivates web users to create a profile<br />(Lampe et al, 2006) found Social Web users to search for and interact with those people which they knew offline<br />The social network which a web user maintains is a powerful tool in establishing a unique identification of the person<br />This is used by identity disambiguation techniques to confirm person references on the Web <br />
  7. 7. Comparing Digital and Real-World Identities<br />Social network information from the shared identity tier provides a means of quantifying the similarity between digital and real-world identities:<br />To what extent is real identity information mirrored in a digital space?<br />To explore this question a user study was conducted using the Social Web platform Facebook<br />Given that this is the most popular Social Web platform in the UK<br />22 million users in the UK<br /><br />50 participants were gathered from the University of Sheffield<br />25 male and 25 female<br />Age range of 18-45<br />The experiment was conducted in November 2008<br />
  8. 8. Comparing Digital and Real-World Identities<br />The user study consisted of 3 stages:<br />Each participant listed their real-world social network<br />Used a web page form with 20 rows, one for each person of the network, containing their name and the relationship type<br />More rows could be added if needed<br />Contains strong-tied relationships (Donath & Boyd, 2004)<br />Each participant’s digital social network was extracted from Facebook<br />Using an application designed for this study<br />Also analysed the behaviour of the the participant: who they appear in photos with and who they share messages with<br />Each participant compared his/her real-world and digital social network<br />Selected participants which appeared in both networks<br />
  9. 9. Comparing Digital and Real-World Identities<br />To measure the extent to which digital identity information mirrors real-world identity information the following evaluation measures were used:<br />Relevance<br />Adapted from the information retrieval metric: precision<br />Measures the ratio of strong-tied to weak-tied relationships in the digital social network<br />Coverage<br />Adapted from the information retrieval metric: recall<br />The proportion of the real-world social network which appears in the digital social network<br />
  10. 10. Comparing Digital and Real-World Identities<br />Relevance<br />Average relevance measure of 0.23, indicating that 23% of a digital social network contains strong-tied relationships<br />Coverage<br />Ranges between 0.5 and 1, with an average coverage of 0.77<br />This indicates that, on average, 77% of a participant’s real-world social network appears online<br />Different from findings by (Subrahmanyam et al, 2008) which found only 49% for coverage (they define it as overlap)<br />Possibly due to differing dates when the studies were conducted and the differing countries<br />
  11. 11. Comparing Digital and Real-World Identities<br />
  12. 12. Comparing Digital and Real-World Identities<br />The covered portion of the real-world social network in the digital network was also analysed, and showed the following:<br />Majority of relationships were with friends (62%)<br />24% were with family<br />14% were with coworkers<br />For the different relationship types in the real-world social network<br />89% of friends were replicated online<br />73% of family were replicated online<br />68% of coworkers were replicated online<br />The largest age group in the social network that was covered online was <21<br />Indicative of the sample used by the study<br />
  13. 13. Comparing Digital and Real-World Identities<br />Behaviour of each participant was analysed<br />Assessing who they appeared in images with and interacted with on the platform <br />Results demonstrate a tendency to interact with only a few people frequently<br />And a large number of people rarely<br />The graph forms a power law curve<br />Denoted as the Social Longtail (>15)<br />The longtail contains 92% of strong-tied relationships which appeared in the real-world network listed by participants<br />Suggests that offline interactions and communications are continued online<br />Demonstrated by the number of messages sent and photos in which both people appear<br />
  14. 14. Conclusions<br />Credibility of digital identity information is important to many application which rely on such information<br />Findings from the study quantify the significant extent to which web users replicate their real-world identities in an online space<br />Characterised by the large coverage of real-world social networks on an example Social Web platform<br />An insight is provided into the credibility of digital identity information found on the Social Web<br />Behavioural trends demonstrate the continuation of interaction offline in a digital environment<br />The Social Longtail is mainly comprised of strong-tied relationships found offline, driven by frequent interactions<br />The results also provide findings in contrast to (Subrahmanyam et al, 2008)<br />Due to the different domains in which the studies were conducted <br />
  15. 15. Twitter: @mattroweshow<br />Web:<br />Email:<br />Questions?<br />(Donath & Boyd, 2004) - J. Donath and D. Boyd. Public displays of connection. BT Technology Journal, 22(4):71–82, October 2004.<br />(Lampe et al, 2006) - C. Lampe, N. Ellison, and C. Steinfield. A face(book) in the crowd: social searching vs. social browsing. In CSCW ’06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, pages 167–170, New York, NY, USA, 2006. ACM.<br />(Ploderer et al, 2008) - B. Ploderer, S. Howard, and P. Thomas. Being online, living offline: the influence of social ties over the appropriation of social network sites. In CSCW ’08: Proceedings of the ACM 2008 conference on Computer supported cooperative work, pages 333–342, New York, NY, USA, 2008. ACM.<br />(Subrahmanyam et al, 2008) - K. Subrahmanyam, S. Reich, N. Waechter, and G. Espinoza. Online and offline social networks: Use of social networking sites by emerging adults. Journal of Applied Developmental Psychology, 29(6):420–433, November 2008. <br />