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Social Media Usage at Universities - How should it be done?

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Social Media Usage at Universities - How should it be done?

  1. 1. Social Media Usage at Universities How should it be done? Jennifer-Carmen Frey, Martin Ebner, Martin Schön, Behnam Taraghi Graz University of Technology Donnerstag, 09. Mai 13
  2. 2. Donnerstag, 09. Mai 13
  3. 3. Agenda Motivations Research Questions Analysis Results Donnerstag, 09. Mai 13
  4. 4. Social Media in Marketing and Public Relations Donnerstag, 09. Mai 13
  5. 5. Social Media in Universities Donnerstag, 09. Mai 13
  6. 6. Efficient presence in social web which factors have to be kept in mind when doing social media work at universities Which activity characteristics have an impact on user engagement in social network Which influencers can be identified? Social Media Goals Donnerstag, 09. Mai 13
  7. 7. Analyze the present activities of universities in social media Evaluate the success by measuring the user engagement concerning different characteristics How to Reach Goals Donnerstag, 09. Mai 13
  8. 8. Communication behavior of first semester students at TU Graz Social Media at Universities Donnerstag, 09. Mai 13
  9. 9. Selected Universities Donnerstag, 09. Mai 13
  10. 10. Analyzed Characteristics Time Time the post has been published Addressed target groups Staff, students, future students, public Post components Videos, pictures, text, hyperlinks, composition of these Post text length Number of characters of the text Post content Subject, function, time reference Frequency of postings Brinker text function model Donnerstag, 09. Mai 13
  11. 11. From Post to User interaction Donnerstag, 09. Mai 13
  12. 12. Facebook Edgerank Algorithm Selects posts to be shown on users‘ news feed: Affinity How strong is the relation btw. user and the fan page / friend? How often does user interact with the page? (interaction rate) How is the interaction rate of friends of the user? ... Weight Value to promote specific content vs. other content types Time decay Time has passed since the post has been published Possible reach factors: Number of fans Talk-about count Edgerank settings Donnerstag, 09. Mai 13
  13. 13. Measuring User Engagement Assumption: average interaction rate decreases while fan number increases A grand amount of fans influence overall interaction rate (Jochenmich, 13) Scale reaction per fan reaction per talk-about > 100000 fans 0.0015 0.0626 5000 - 100000 fans 0.0021 0.0622 < 5000 fans 0.0076 0.1207 Efficiency(P) = 100 * ( Act(P) / Est(P) ) Estimated User Engagement: Est(P) = 0.5 * ( fans(P)*fanFactor(size(P)) + talk-about(p) * talk-aboutFactor(size(P) ) Donnerstag, 09. Mai 13
  14. 14. Statistical Analysis Goal: Potential relations btw. post characteristics and efficiency Methods used: Pearson‘s Correlation Spearman‘s rank correlation Clustering methods, ... Time period: 09 - 11 2012 Donnerstag, 09. Mai 13
  15. 15. Statistics in Detail Number of posts per University 09 - 11 2012 Donnerstag, 09. Mai 13
  16. 16. Statistics in Detail Number of comments per University 09 - 11 2012 Donnerstag, 09. Mai 13
  17. 17. Statistics in Detail Number of fans / talk-about count per University 09 - 11 2012 Donnerstag, 09. Mai 13
  18. 18. Statistics in Detail Average reaction rate per post 09 - 11 2012 Donnerstag, 09. Mai 13
  19. 19. Results - Identified Influencers UE does not correlate with a single characteristic Composition of characteristics can define an efficient post Detected influencers: Time Post components Post content (subject, function, time reference) Donnerstag, 09. Mai 13
  20. 20. Influencers: Time 1: Monday ... 7: Sunday 1: 22:00 - 05:00 2: 05:00 - 08:00 3: 08:00 - 11:00 4: 11:00 - 13:00 5: 13:00 - 18:00 6: 18:00 - 22:00 Donnerstag, 09. Mai 13
  21. 21. Influencers: Post Components Negative correlation btw. UE and posts without visual elements Donnerstag, 09. Mai 13
  22. 22. Influencers: Post Content Donnerstag, 09. Mai 13
  23. 23. Results - Other Characteristics Text length: not influential Addressed target group: not influential Frequency of postings per day: 1 <= f <= 3 Comparison of university efforts: Some universities obtain higher UE rate although lower fan base / talk-about rate Best Example: Ohio State University vs. Harward Donnerstag, 09. Mai 13
  24. 24. Some Social Media Strategies for Universities Strengthen social aspects Present university as a common work place Supply opportunities to keep in contact with community Accomplishment of social conventions (Greetings etc.) Combine visual posts with text. Post on weekend at night Post some contents just for fun Avoid information about research Avoid pure announcements, use other media instead Donnerstag, 09. Mai 13
  25. 25. Graz University of Technology SOCIAL LEARNING Computer and Information Services Graz University of Technology Behnam Taraghi http://elearning.tugraz.at Slides available at: http://elearningblog.tugraz.at behi_at Graz University of Technology Donnerstag, 09. Mai 13

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