Presentation at WEBIST conference, 2013

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. Donnerstag, 09. Mai 13

3. Agenda
Motivations
Research Questions
Analysis
Results
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4. Social Media in Marketing and
Public Relations
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5. Social Media in Universities
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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
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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
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8. Communication behavior of first semester students at TU Graz
Social Media at Universities
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9. Selected Universities
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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
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11. From Post to User interaction
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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. 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) )
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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
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15. Statistics in Detail
Number of posts per University 09 - 11 2012
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16. Statistics in Detail
Number of comments per University 09 - 11 2012
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17. Statistics in Detail
Number of fans / talk-about count per University 09 - 11 2012
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18. Statistics in Detail
Average reaction rate per post 09 - 11 2012
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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. 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
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21. Influencers: Post Components
Negative correlation btw. UE and posts without
visual elements
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22. Influencers: Post Content
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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
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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. 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