An evaluation of the potential of Web 2.0 API's for social research

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Mechant, P. & Courtois, C. (2011). An evaluation of the potential of Web 2.0 API's for social research. In: COST Action ISO906 : New challenges and methodological innovations in European Media Audience Research, Zagreb, Croatia, 2011-04-07. 43-44.

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An evaluation of the potential of Web 2.0 API's for social research

  1. 1. Cédric Courtois and Peter Mechant IBBT-MICT-Ghent University Using API’s in Web 2.0 user research: The case of networked public expectancies and feedback preferences on YouTube
  2. 2. <ul><li>Accounts for over 20% of the HTTP traffic </li></ul><ul><li>Over 50,000 new videos every day, mostly user-generated </li></ul><ul><li>RQ 1: Who do these uploaders expect to view their videos? </li></ul><ul><li>RQ 2: How do they know this public is attained? </li></ul><ul><li>RQ 3: Are these expectancies reliable? </li></ul>
  3. 3. <ul><li>Unfunded, multi-method research project </li></ul><ul><li>Consists of three phases: </li></ul><ul><ul><li>Qualitative exploration (20 face to face interviews) </li></ul></ul><ul><ul><li>Quantitative validation (two samples: N = 450 and N = 242) </li></ul></ul><ul><ul><li>Longitudinal analysis of online feedback ( N = 242) </li></ul></ul><ul><li>Combination of self-report and platform data (Google API) </li></ul><ul><li>Results in press: </li></ul><ul><ul><li>Courtois, C., Mechant, P., Ostyn, V. & De Marez, L. (in press). Uploaders' Definitions of the Networked Public on YouTube and their Feedback Preferences: A Multimethod Approach. Behaviour & Information Technology. </li></ul></ul><ul><ul><li>Courtois, C., Mechant, P. & De Marez, L. (in press). Teenage Uploaders on YouTube: Networked Public Expectancies, Online Feedback Preference and Received On-Platform Feedback. Cyberpsychology, Behavior, and Social Networking. </li></ul></ul>
  4. 4. <ul><li>Phase 1: qualitative exploration </li></ul><ul><li>Theoretical framework: </li></ul><ul><ul><li>Audiences versus publics: passive versus active, critically engaged </li></ul></ul><ul><ul><li>Concept of Networked Publics </li></ul></ul><ul><ul><li>Collective Effort Model (Social Loafing): engage in collective task? </li></ul></ul><ul><ul><li>Importance of feedback in CMC (confirmation of ‘imagined audience’) </li></ul></ul><ul><li>Analysis: combination of deductive and inductive coding </li></ul><ul><ul><li>All instances of groups: differentiation </li></ul></ul><ul><ul><li>All instances of feedback types and the attributed importance </li></ul></ul>
  5. 5. <ul><li>Phase 1: qualitative exploration (results) </li></ul><ul><li>Three types of networked publics mapped onto two dimensions </li></ul><ul><ul><li>Identified offline public: physical acquaintances, socially embedded </li></ul></ul><ul><ul><li>Identified online public: like-minded online in-group </li></ul></ul><ul><ul><li>Unidentified online public: unfamiliar online out-group </li></ul></ul>
  6. 6. <ul><li>Phase 1: qualitative exploration (results) </li></ul><ul><li>Seeming contingencies between public types and feedback </li></ul><ul><ul><li>Identified offline public: offline feedback (real-life comments/conversations) and off-platform online feedback (via IM, E-mail, SNS) </li></ul></ul><ul><ul><li>Identified online public: on and off-platform feedback (via comments, rates, views) </li></ul></ul><ul><ul><li>Unidentified online public: online on-platform feedback </li></ul></ul><ul><li>Expectancy strengths </li></ul><ul><ul><li>Identified public types > unidentified public types </li></ul></ul><ul><ul><li>Offline public type > online public types </li></ul></ul>
  7. 7. <ul><li>Phase 1: qualitative exploration (results) </li></ul><ul><li>Seeming contingencies between public types and feedback </li></ul><ul><ul><li>Identified offline public: offline feedback (real-life comments/conversations) and off-platform online feedback (via IM, E-mail, SNS) </li></ul></ul><ul><ul><li>Identified online public: on and off-platform feedback (via comments, rates, views) </li></ul></ul><ul><ul><li>Unidentified online public: online on-platform feedback </li></ul></ul><ul><li>Expectancy strengths </li></ul><ul><ul><li>Identified public types > unidentified public types </li></ul></ul><ul><ul><li>Offline public type > online public types </li></ul></ul>Subsequent quantitative phase: operationalization and testing of qualitative results
  8. 8. <ul><li>Phase 2: quantitative study (methodology) </li></ul><ul><ul><li>Step 1 : selection of potentional respondents through Google API (harvested into database) </li></ul></ul><ul><ul><li>Step 2: invitation through comments underneath videos: call to action (with hyperlink to survey regarding the uploaders’ latest video, measuring public expectancies and feedback importance; N = 450, Mean age = 23.70 ( SD = 12.32), 73% males ) </li></ul></ul><ul><ul><li>Step 3: harvesting of respondents’ public profile data </li></ul></ul>
  9. 9. Phase 2: quantitative study (results) The majority of findings in the qualitative phase are supported Hypotheses Evidence H1a: Offline public > Online publics ✓ and ✗ Within-subjects ANOVA H1b: Identified publics > Unidentified public ✓ H2a: Identified offline public expectancy  Offline and off-platform online feedback importance (+) ✓ Structural Equation Modeling H2b: Identified online public expectancy  On- and off-platform online feedback importance (+) ✓ H2c: Identified offline public expectancy  On-platform online feedback importance (+) ✗ @  2 (108, N = 450 ) = 223.29, p < .001, CFI = .98, TLI = .97, RMSEA = .05, CI 90 .04, .06
  10. 10. <ul><li>Implications </li></ul><ul><ul><li>Majority of users upload for people they have affinity with, the broader YouTube community is not important </li></ul></ul><ul><ul><li>Strong contingencies between the type of expected public and the feedback that is appreciated (lack of feedback does not imply an unsatisfied uploader) </li></ul></ul><ul><ul><li>Yet: do uploaders who seek online feedback get what they want? A second study was set up to test this … </li></ul></ul>
  11. 11. <ul><li>Second study </li></ul><ul><ul><li>Quantitative study on teenagers (N = 242, age 12-18), partial replication of previous study: all three public types and online feedback importance were included </li></ul></ul><ul><ul><li>Results show an exact replication of the first study’s results (expectancy strengths and expected public-feedback contingencies) </li></ul></ul><ul><ul><li>Additional analysis: analysis of longitudinal growth of received online on-platform feedback (comments, rates, views) </li></ul></ul>
  12. 12. <ul><li>Methodology </li></ul><ul><ul><li>Step 1-3: identical: invite respondents through API, gather self-report survey data and harvest profile and latest video information </li></ul></ul><ul><ul><li>Step 4-5: collect video information at fixed intervals of 1 month (so three measures were gathered) </li></ul></ul><ul><ul><li>Hypotheses: </li></ul></ul><ul><ul><li>H1-3: Online identified public (similar interest, activity): more views, comments and rates </li></ul></ul><ul><ul><li>H4-6: Online unidentified public (online out-group): no effect on # views, comments and rates </li></ul></ul>@
  13. 13. <ul><li>Analysis and results (latent growth modeling) </li></ul><ul><ul><li>H1-3: Online identified public (similar interest, activity): more views ( partial ✓ ) , comments ( partial ✓ ) and rates ( ✓ ) </li></ul></ul><ul><ul><li>H4-6: Online unidentified public (online out-group): no effect on views ( ✓ ), comments ( ✓ ) and rates ( ✓ ) </li></ul></ul><ul><ul><li>Hence, expectancies are accurate … </li></ul></ul>T1 = after filling out survey T2 = one month later T3 = two months later Separate models for views, comments and rates
  14. 14. <ul><li>Conclusion </li></ul><ul><ul><li>Results of first study replicate fine, even in a different context (replications are important) </li></ul></ul><ul><ul><li>Combination of multiple data sources amplifies strength of design (huge amounts of informative data are for the taking, free of cost) </li></ul></ul><ul><ul><li>For recruitment as well as dedicated analysis </li></ul></ul>
  15. 15. <ul><li>Conclusion </li></ul><ul><ul><li>Results of first study replicate fine, even in a different context (replications are important) </li></ul></ul><ul><ul><li>Combination of multiple data sources amplifies strength of design (huge amounts of informative data are for the taking, free of cost) </li></ul></ul><ul><ul><li>For recruitment as well as dedicated analysis </li></ul></ul>Thank you for listening… Any questions? Contact: cedric.courtois@ugent.be, www.mict.be

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