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My Dissertation Defense

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ATPI Doctoral Dissertation Defense of Laura A. Pasquini
Department of Learning Technologies, College of Information
University of North Texas
June 12, 2014

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My Dissertation Defense

  1. 1. 1 Organizational Identity and Community Values: Determining Meaning in Post-Secondary Education Social Media Guideline and Policy Documents ATPI Dissertation of Laura A. Pasquini Department of Learning Technologies College of Information, University of North Texas June 12, 2014 Co-Major Professors: Dr. Jeff M. Allen & Dr. Nick Evangelopoulos; Dissertation Committee: Dr. Kim Nimon & Dr. Mark Davis
  2. 2. Research Study To examine and define the semantic structure of a corpus-creating community of practice and to establish a common reference point for post-secondary education (PSE) social media guideline and policy documents. 2 pp. 2-3
  3. 3. How is Social Media Being “Guided” in Higher Ed? •Mergel et al. (2012) Create a social media policy before using social media or experimentation with social media within the organization to generate and apply guidance. •Wandel (2009) and Joosten et al. (2013) Security and privacy are two of the primary concerns •Rodriguez (2011) Deal with challenges related to privacy, ownership of intellectual property, legal use, identity management, and literacy development pp. 31-32 3
  4. 4. Background • Social media use has increased in higher education (Brenner & Smith, 2013); however guideline and policy documents have rarely been examined (Joosten, 2012; Joosten et al., 2013; Reed, 2013) • Institutions direct & moderate how students, staff, faculty & administrators use social media on campus (Blankenship, 2011; Moran, Seaman, & Tinti-Kane, 2011) pp. 4-54
  5. 5. Research Questions R1. What content related factors are relevant to structuring the body of textual data in retrieved electronic social media guideline and policy documents from the PSE sector? R2. Does the distribution of topics analyzed in the corpus differ by PSE institution geographic location? pp. 9-85
  6. 6. Theoretical Development The cycle of Wenger’s (1998) participation and reification in the community of practice is assessed through a distributed repository of documents, which for the purpose of this study is called a corpus. pp. 8-16 6
  7. 7. Participation pp. 10-11 7
  8. 8. Reification pp. 11-13 8
  9. 9. Theory Building Following Evangelopoulos & Polyakov (2014) this research focused on a special kind of community of practice, the corpus-creating community, where the body of social media guideline and policy documents is a distributed corpus. Specifically this corpus contains meaning, values and identity. 9 pp. 14-16
  10. 10. Assumption 1. The community of social media guideline and policy administrators in PSE is a community of practice. 10 p. 9
  11. 11. Assumption 2. The community of practice, social media guideline and policy administrators in PSE, have built a semantic structure with a shared understanding of how social media guidelines and policies should be. 11 p. 10
  12. 12. Assumption 3. Published and accessible social media guideline and policy documents are artifacts that reify the ideas from the community of practice. 12 p. 11
  13. 13. Assumption 4. Analysis of the collection of social media guideline and policy documents by an appropriate text analytic method uncovers the components of the semantic structure of meaning. 13 p. 13
  14. 14. Theoretical Framework 14 p. 15
  15. 15. Limitations The research method LSA is: •Dimension reduction of the dataset •Orthogonal (Lee, Song & Kim, 2010) •Polysemy issues (Li & Joshi, 2012) pp. 19-20 15
  16. 16. Delimitations • Published, online accessible • Text only in artifacts • Organizational focus: PSE sector • English-speaking countries • No indicates bound of the study controlled might influence validity • Follow LSA methodological recommendations for this type of text mining procedures (Evangelopoulos, Zhang, & Prybutok, 2012) pp. 20-2116
  17. 17. Methodology The research design for this study was a semi-automatic approach to reviewing the semantic structure and terms in the social media guideline and policy documents. This particular text mining procedure required a large matrix of term-document data to construct a semantic space in which the closely associated terms and documents were place near one another. (Deerester, Dumais, Landauer, & Harshman, 1990) p. 3617
  18. 18. Research Methods Latent Semantic Analysis (LSA) • a computational research method that simulates human like analysis with language (Landauer, 2011) • originally used for information retrieval query optimization (Deerwester, Dumais, Furnas, Landauer, & Harshman, 1990; Dumais, 2004) • topic extraction using LSA (Sidorova, Evangelopoulos, Valacich & Ramakrishnan, 2008; Li & Joshi, 2012) • rotated LSA (Evangelopoulos & Polyakov, 2014) pp. 41-4518
  19. 19. 19 Research Design p. 46
  20. 20. Step 1: Establish the Corpus
  21. 21. Data Collection www.socialmediaguidance.wordpress.com 21 pp. 55-56
  22. 22. Sample: 250 PSE Institutions from 10 Countries pp. 53-54 & Appendix A (p.103) 22 24,243 atomic social media guideline & policy documents
  23. 23. pp. 53-54 & Appendix A (p.103) 23 17,429 1071 13 3771 314 1121 189 109 160 62 # of atomic social media documents
  24. 24. Step 2: Pre-Process the Data
  25. 25. Text Document Preparation for LSA pp. 47-48 & Appendix C 25 PassageID PassageText SMP00001 Social Networking/Social Utilities SMP00002 The following recommendations were discussed in the context of the social media that are most popular now, mainly Facebook, LinkedIn, and Twitter, but were drafted to be fluid enough to apply to social networks and utilities that will emerge in the future. The IMG recommends the following best practices guidelines. SMP00003 Do: SMP00004 Use social media to stay in touch with friends and make new ones. SMP00005 Use social media to create your best image, since your page is likely visible to more people than just your selected friends, followers, or subscribers. SMP00006 Type your name into a search engine (i.e., Google, Bing, Facebook, YouTube) every once in a while to check on your public image. SMP00007 Use social media to get involved with the campus community and learn what's happening. SMP00008 Use social media to advertise your organization's events. SMP00009 Make sure you understand and use the privacy settings on your social media accounts to monitor who can look at your profile.
  26. 26. LSA Input Data: Term Frequency Matrix Input data for LSA is the term frequency matrix X. This matrix quantifies the collection of documents by recording the occurrence of each term in each document. documents terms X pp. 42-4426 PassageID PassageText SMP00001 Social Networking/Social Utilities SMP00002 The following recommendations were discussed in the context of the social media that are most popular now, mainly Facebook, LinkedIn, and Twitter, but were drafted to be fluid enough to apply to social networks and utilities that will emerge in the future. The IMG recommends the following best practices guidelines. SMP00003 Do: SMP00004 Use social media to stay in touch with friends and make new ones. SMP00005 Use social media to create your best image, since your page is likely visible to more people than just your selected friends, followers, or subscribers.
  27. 27. Step 3: Extract Knowledge
  28. 28. LSA Step 1: Singular Value Decomposition Latent Semantic Analysis (LSA) starts with the Singular Value Decomposition (SVD) of matrix X: X = U  Σ  VT where U is the term eigenvector matrix, V is the document eigenvector matrix, and Σ is the diagonal matrix of singular values (square roots of eigenvalues). SVD performs a semantic decomposition of the discourse in X. documents terms X dimensions terms dimensionsdimensions = documents dimensions · · VT ΣU pp. 42-4428
  29. 29. LSA Step 2: Truncated SVD The truncated term frequency matrix is obtained by retaining the first k SVD dimensions: Xk = Uk  Σk  Vk T The truncation of the SVD components corresponds to a semantic abstraction of the discourse in X. documents terms Xk dimensions terms dimensions dimensions= documents dimensions · · Vk T ΣkUk pp. 44-4529 24,24 3 664
  30. 30. How Many Dimensions? pp. 63-6430 Eigenvalues obtained by squaring the singular values in matrix and using iterative methods to obtain the scree plot elbow, and the profile likelihood test (Zhu & Ghodsi, 2006).
  31. 31. Contractual vs. Promotional View pp. 73-79 31
  32. 32. Appendix E (p. 129) and pp. 68-71 32 Document Count Topics Label Contractual View: 36 Universal Topics for 250 PSE Institutions
  33. 33. 33 Document Count Topics Label Appendix E (p. 129) and pp. 68-71 Contractual View: Universal Topics for 250 PSE Institutions
  34. 34. Appendix E (p. 131) 34 Factors Labels High-Loading Terms with TF-IDF F36.1 Facebook facbook (15.3), page (0.8) F36.2 Twitter twitter (15), account (0.83), tweet (0.76) F36.3 Engagement engag (6.68), share (3.32), convers (2.61), onlin (2.15), user (2.14), peopl (2.13), more (2.09), audienc (2.04), help (1.93), social (1.91), inform (1.87), network (1.81), don (1.66), creat (1.65), commun (1.6), follow (1.57), activ , (1.46), group (1.4), tool (1.37), keep (1.37),post (1.34), facebook (1.34), us (1.22), content, 1.21), comment, (1.17), presenc (1.16), photo (1.16), event (1.13), profession (1.11), provid (1.08), blog (1.08), on (1.05), connect (1.03), encourag (1.03), public (1.02), platform (1.02), twitter (1.01), new (0.99), allow (0.98), tweet (0.94), re (0.92), build (0.91), discuss (0.86), respond (0.86), learn (0.84), valu (0.83), organ (0.82), effect (0.82), fan (0.82), respect (0.81), channel (0.81), site (0.81), promot (0.8), page (0.8), person (0.77), particip (0.77), friend (0.77), linkedin (0.77), relev (0.76), student (0.76), not (0.75), best (0.75) F36.4 Best Practices practic (9.79), best (9.51) F36.5 Content content (12.9), share (1.54), creat (1.27), web (1.1), comment (0.93), manag (0.88), copyright (0.76) F36.6 YouTube youtub (12.8), channel (1.33), photo (1.16), video (0.9) For Example – F36.3: Engagement
  35. 35. Appendix E (p. 131) 35 F36.3: Engagement The high-loading terms for F36.2 include:
  36. 36. Promotional View: Common Topics Among 250 PSE Institutions Appendix G (pp. 136-137)36 Topic Very High Clarity By Region Observed Topic Label Topic Non-USUS Coln 1 Coln 2 Total Facebook F36.1 21 68 22.372 66.628 89 Twitter F36.2 20 61 20.361 60.639 81 Best Practices F36.4 12 59 17.847 53.153 71 Content F36.5 9 41 12.569 37.431 50 YouTube F36.6 18 53 17.847 53.153 71 Posting F36.8 12 43 13.825 41.175 55 Comments F36.9 5 21 6.536 19.464 26 Institutional Users F36.12 12 6 4.525 13.475 18 Account Management F36.13 4 19 5.782 17.218 23 Use of Platforms F36.14 13 21 8.547 25.453 34 Respect F36.15 19 57 19.104 56.896 76 Blogs F36.16 10 33 10.809 32.191 43 Copyright & Fair Use F36.17 7 22 7.290 21.710 29 Social Networking F36.19 6 14 5.027 14.973 20 Audience F36.21 6 23 7.290 21.710 29 Site Maintenance F36.22 5 8 3.268 9.732 13 Link F36.27 7 15 5.530 16.470 22 Privacy F36.28 5 18 5.782 17.218 23 Naming Conventions F36.29 5 17 5.530 16.470 22 Flickr F36.33 11 31 10.558 31.442 42 LinkedIn F36.34 10 33 10.809 32.191 43 Responsibility F36.35 12 19 7.793 23.207 31 229 682 911 Expected DESCRIPTION VALUE c2 * 30.919727 p-value 0.075004 Critical value 32.670573 a 0.05 df 21 Calculation of the Chi-Square Test
  37. 37. Promotional View: Most Diverging Topics Between the US & Non-US PSE Institutions pp. 79-82 37 Step Topic Chi-sq Value P-value Most Diverging Topics Factor Chi-sq 1 36 324.41 0.000 Institutional Users F36.12 81.74 2 35 242.39 2.76E-33 Page & Group Administration F36.10 55.17 3 34 182.82 1.15E-22 Information Management F36.7 27.21 4 33 155.6 3.55E-18 Privacy F36.28 25.74 5 32 130.07 4.26E-14 Facebook F36.1 17.48 6 31 111.17 2.96E-11 Social Networking F36.19 13.84 7 30 97.37 2.56E-09 Audience F36.21 12.38 8 29 84.18 1.57E-07 Posting F36.8 10.72 9 28 72.52 4.87E-06 Personal Use F36.18 8.05 10 27 63.8 5.06E-05 Support at Institution F36.11 6.1 11 26 56.99 0.000268 Time & Resource Management F36.25 5.73 12 25 50.83 0.00111 Followers F36.24 6.04 13 24 44.32 0.00481 Institutional Identity F36.23 5.21 14 23 38.64 0.01551 Flickr F36.33 4.95
  38. 38. Promotional View: Correspondence Analysis p. 8838
  39. 39. Promotional View: Canada p. 8839 F28 see SMP01268 passage example from Brock University : ”Brock University protects your privacy and your personal information. The personal information requested on this form is collected under the authority of The Brock University Act, 1964, and in accordance with the Freedom of Information and Protection of Privacy Act (FIPPA) for the administration of the University and its programs and services. Direct any questions about this collection to the Social Media coordinator in University Marketing and Communications.” F28 = Privacy F07 = Information Management F07 see SMP16534 passage example from Thompson River University : “The first, and probably most important, privacy tool or protocol you can engage is to prepare or provide a brief privacy seminar for students that informs them about existing privacy legislation in BC and Canada and highlights the importance of fundamental privacy principles, such as knowledge, notice and informed consent. Most younger students have grown up in a culture of mass information-- sharing and are not yet old enough-or simply have been fortunate enough-to never have suffered the serious negative consequences for sharing too much of their or other people's personal information.”
  40. 40. Discussion: My Interpretation 40
  41. 41. Recommendations for Social Media Guideline and Policy Development 41 Appendix H (pp. 138-169)
  42. 42. Bonus: Q-Sort Method 42 Conducted a Q-Sort Method for Categories
  43. 43. 43 Q-Sort Inter Rater Reliability Matrix Inter-Rater Reliability Coefficients and Associated Standard Errors Condition al/Rater Sample Condition al/Subject Sample Unconditi onal Method Estimate Std Error 95%C.I. Std Error 95% C.I. Std Error 95% C.I. AC1 0.679709 0.057206 (0.564 : 0.796) 0.049043 (0.58 : 0.779) 0.064524 (0.549 : 0.811) AC1C 0.680354 0.056997 (0.565 : 0.796) 0.048832 (0.581 : 0.779) N/A N/A Kappa 0.6726 0.057481 (0.556 : 0.789) 0.049882 (0.571 : 0.774) 0.065012 (0.541 : 0.805) KappaC 0.671924 0.058116 (0.554 : 0.79) 0.0501 (0.57 : 0.774) N/A N/A BP 0.679012 0.057226 (0.563 : 0.795) 0.049125 (0.579 : 0.779) 0.064564 (0.548 : 0.81) Conger 0.672937 0.057863 (0.555 : 0.79) 0.049738 (0.572 : 0.774) N/A N/A (Landis & Koch, 1997; Gwet, 2008)
  44. 44. Updated Categories from Q-Sort 44
  45. 45. Social Media Guideline and Policy Document Database 45 Appendix B (pp. 116-117) Pasquini, Laura A. (2014). Appendix B: Social media guideline and policy document database. figshare. http://dx.doi.org/10.6084/m 9.figshare.1050571
  46. 46. Future Research 46 pp. 101-102
  47. 47. Summary of Research 47 • Compiled recommendations for developing social media guidelines and policies from 250 PSE institutions representing 10 countries after extracting key topics. • Developed a common reference for social media guideline and policy documents research to inform the PSE sector. • Compared the distribution of the 36 topics (factors) across two geographic regions to determine importance. • Theorized the semantic structure created by an organization in relation to Wenger’s (1998) Community of Practice framework with corpus-creating community.
  48. 48. 48 Questions?

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