Social Media Usage atUniversitiesHow should it be done?Jennifer-Carmen Frey, Martin Ebner, Martin Schön, Behnam TaraghiGra...
Donnerstag, 09. Mai 13
AgendaMotivationsResearch QuestionsAnalysisResultsDonnerstag, 09. Mai 13
Social Media in Marketing andPublic RelationsDonnerstag, 09. Mai 13
Social Media in UniversitiesDonnerstag, 09. Mai 13
Efficient presence in social webwhich factors have to be kept in mind whendoing social media work at universitiesWhich act...
Analyze the present activities of universities insocial mediaEvaluate the success by measuring the userengagement concerni...
Communication behavior of first semester students at TU GrazSocial Media at UniversitiesDonnerstag, 09. Mai 13
Selected UniversitiesDonnerstag, 09. Mai 13
Analyzed CharacteristicsTimeTime the post has been publishedAddressed target groupsStaff, students, future students, publi...
From Post to User interactionDonnerstag, 09. Mai 13
Facebook Edgerank AlgorithmSelects posts to be shown on users‘ news feed:AffinityHow strong is the relation btw. user and ...
Measuring User EngagementAssumption:average interaction rate decreases while fan number increasesA grand amount of fans in...
Statistical AnalysisGoal:Potential relations btw. post characteristics and efficiencyMethods used:Pearson‘s CorrelationSpe...
Statistics in DetailNumber of posts per University 09 - 11 2012Donnerstag, 09. Mai 13
Statistics in DetailNumber of comments per University 09 - 11 2012Donnerstag, 09. Mai 13
Statistics in DetailNumber of fans / talk-about count per University 09 - 11 2012Donnerstag, 09. Mai 13
Statistics in DetailAverage reaction rate per post 09 - 11 2012Donnerstag, 09. Mai 13
Results - Identified InfluencersUE does not correlate with a single characteristicComposition of characteristics can defin...
Influencers: Time1: Monday...7: Sunday1: 22:00 - 05:002: 05:00 - 08:003: 08:00 - 11:004: 11:00 - 13:005: 13:00 - 18:006: 1...
Influencers: Post ComponentsNegative correlation btw. UE and posts withoutvisual elementsDonnerstag, 09. Mai 13
Influencers: Post ContentDonnerstag, 09. Mai 13
Results - Other CharacteristicsText length: not influentialAddressed target group: not influentialFrequency of postings pe...
Some Social Media Strategiesfor UniversitiesStrengthen social aspectsPresent university as a common work placeSupply oppor...
Graz University of TechnologySOCIAL LEARNINGComputer and Information ServicesGraz University of TechnologyBehnam Taraghiht...
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Social Media Usage at Universities - How should it be done?

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Presentation at WEBIST conference, 2013

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

  1. 1. Social Media Usage atUniversitiesHow should it be done?Jennifer-Carmen Frey, Martin Ebner, Martin Schön, Behnam TaraghiGraz University of TechnologyDonnerstag, 09. Mai 13
  2. 2. Donnerstag, 09. Mai 13
  3. 3. AgendaMotivationsResearch QuestionsAnalysisResultsDonnerstag, 09. Mai 13
  4. 4. Social Media in Marketing andPublic RelationsDonnerstag, 09. Mai 13
  5. 5. Social Media in UniversitiesDonnerstag, 09. Mai 13
  6. 6. Efficient presence in social webwhich factors have to be kept in mind whendoing social media work at universitiesWhich activity characteristics have an impacton user engagement in social networkWhich influencers can be identified?Social Media GoalsDonnerstag, 09. Mai 13
  7. 7. Analyze the present activities of universities insocial mediaEvaluate the success by measuring the userengagement concerning different characteristicsHow to Reach GoalsDonnerstag, 09. Mai 13
  8. 8. Communication behavior of first semester students at TU GrazSocial Media at UniversitiesDonnerstag, 09. Mai 13
  9. 9. Selected UniversitiesDonnerstag, 09. Mai 13
  10. 10. Analyzed CharacteristicsTimeTime the post has been publishedAddressed target groupsStaff, students, future students, publicPost componentsVideos, pictures, text, hyperlinks, composition of thesePost text lengthNumber of characters of the textPost contentSubject, function, time referenceFrequency of postingsBrinker text function modelDonnerstag, 09. Mai 13
  11. 11. From Post to User interactionDonnerstag, 09. Mai 13
  12. 12. Facebook Edgerank AlgorithmSelects posts to be shown on users‘ news feed:AffinityHow 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?...WeightValue to promote specific content vs. other content typesTime decayTime has passed since the post has been publishedPossible reach factors:Number of fans Talk-about count Edgerank settingsDonnerstag, 09. Mai 13
  13. 13. Measuring User EngagementAssumption:average interaction rate decreases while fan number increasesA grand amount of fans influence overall interaction rate (Jochenmich, 13)Scalereaction per fan reaction pertalk-about> 100000 fans 0.0015 0.06265000 - 100000 fans 0.0021 0.0622< 5000 fans 0.0076 0.1207Efficiency(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 AnalysisGoal:Potential relations btw. post characteristics and efficiencyMethods used:Pearson‘s CorrelationSpearman‘s rank correlationClustering methods, ...Time period: 09 - 11 2012Donnerstag, 09. Mai 13
  15. 15. Statistics in DetailNumber of posts per University 09 - 11 2012Donnerstag, 09. Mai 13
  16. 16. Statistics in DetailNumber of comments per University 09 - 11 2012Donnerstag, 09. Mai 13
  17. 17. Statistics in DetailNumber of fans / talk-about count per University 09 - 11 2012Donnerstag, 09. Mai 13
  18. 18. Statistics in DetailAverage reaction rate per post 09 - 11 2012Donnerstag, 09. Mai 13
  19. 19. Results - Identified InfluencersUE does not correlate with a single characteristicComposition of characteristics can define an efficient postDetected influencers:TimePost componentsPost content (subject, function, time reference)Donnerstag, 09. Mai 13
  20. 20. Influencers: Time1: Monday...7: Sunday1: 22:00 - 05:002: 05:00 - 08:003: 08:00 - 11:004: 11:00 - 13:005: 13:00 - 18:006: 18:00 - 22:00Donnerstag, 09. Mai 13
  21. 21. Influencers: Post ComponentsNegative correlation btw. UE and posts withoutvisual elementsDonnerstag, 09. Mai 13
  22. 22. Influencers: Post ContentDonnerstag, 09. Mai 13
  23. 23. Results - Other CharacteristicsText length: not influentialAddressed target group: not influentialFrequency of postings per day: 1 <= f <= 3Comparison of university efforts:Some universities obtain higher UE rate although lower fan base / talk-about rateBest Example: Ohio State University vs. HarwardDonnerstag, 09. Mai 13
  24. 24. Some Social Media Strategiesfor UniversitiesStrengthen social aspectsPresent university as a common work placeSupply opportunities to keep in contact with communityAccomplishment of social conventions (Greetings etc.)Combine visual posts with text.Post on weekend at nightPost some contents just for funAvoid information about researchAvoid pure announcements, use other media insteadDonnerstag, 09. Mai 13
  25. 25. Graz University of TechnologySOCIAL LEARNINGComputer and Information ServicesGraz University of TechnologyBehnam Taraghihttp://elearning.tugraz.atSlides available at: http://elearningblog.tugraz.atbehi_atGraz University of TechnologyDonnerstag, 09. Mai 13

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