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AThree-Step
Data-Mining Analysis of
Top-Ranked Higher
Education Institutions’
Communication on
Facebook
Álvaro	Figueira,	A...
2
However,	the	current	panorama	in	not	
that	clear.	This	social	media	presence	
requires	communication	departments	to	
acc...
3
4
The rankingand the dataset.
World	Rank Institution Location
National	
Rank
Quality of	
Education
Alumni	
Employment
Qual...
5
A three-step data-miningprocess.
Characterize	the	editorial	
policy	of	each	HEI
Characterize	the	audience	
of	each	HEI
P...
6
Step 1a: understanding the
communication strategy.
• Posting	frequency
• Period	of	the	day	of	the	posts
• Number	of	post...
7
8
Step 1b: understanding the
response patterns.
• Frequency	and	intensity	of	comments
• Topics	of	comments
• Distribution	...
9
10
Step 2: comparing HEI through
created metrics.
• Most	active	fans
• Feedback	from	Most	Active	Fan	(MAF)
• Number	of	pos...
11
12
Step 3: predictive analytics.
• In	the	prediction	phase,	we	built	three	models to	predict	basically	
three	things:
• th...
Predicting.
HEI Y1 Y2 Y3
Oxford
MIT
Harvard
MIT
...
13
• We	presented	a	three-step	model	to	analyze	the	social	media	
communication	of	higher-education	institutions.	The	base	fo...
THANKYOU
Álvaro	Figueira
arf@dcc.fc.up.pt
CRACS	/	INESCTEC	and	University	of	Porto
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A Three-Step Data-Mining Analysis of Top-Ranked Higher Education Institutions’ Communication on Facebook

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Track 15. Communication, Education and Social Media
Author: Alvaro Figueira

Publié dans : Formation
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A Three-Step Data-Mining Analysis of Top-Ranked Higher Education Institutions’ Communication on Facebook

  1. 1. AThree-Step Data-Mining Analysis of Top-Ranked Higher Education Institutions’ Communication on Facebook Álvaro Figueira, André Fonseca CRACS/INESCTEC and University of Porto arf@dcc.fc.up.pt , andre.fonseca@pgawind.eu
  2. 2. 2 However, the current panorama in not that clear. This social media presence requires communication departments to account for the time and efforts devoted to maintaining it and gathering the highest possible interaction among the community is key. Also, organizations strive to uncover which are the real key topics emerging from their content strategies, and which the publics use to form the mental image of the brand. Over the last few years, due to the technological evolution in what concerns web services, there has been an enormous adherence to online Social Networks. One of the most used social networks in the higher education sector is Facebook and, currently, most top-ranked Universities have an official page on it. It would be expected the posts on that page to follow an editorial guideline or strategy, developed by the respective communication/marketing department and aligned with the institution’s strategic purposes. Motivation. In our research we seek for automatic procedures to create a digital footprint of an organization on social media. In particular, to automatically uncover the publication strategy such that it can be later on analyzed with other tools and insights.
  3. 3. 3
  4. 4. 4 The rankingand the dataset. World Rank Institution Location National Rank Quality of Education Alumni Employment Quality of Faculty Publications Influence Citations Broad Impact Patents Score 1 Top 0.1% Harvard University USA 1 1 1 1 1 1 1 1 2 100.00 2 Top 0.1% Stanford University USA 2 8 2 2 5 3 2 3 7 96.86 3 Top 0.1% Massachusetts Institute of Technology USA 3 2 12 3 14 2 3 2 1 95.72 4 Top 0.1% University of Cambridge United Kingdom 1 3 10 6 10 7 17 13 52 93.14 5 Top 0.1% University of Oxford United Kingdom 2 7 14 9 6 6 4 9 19 92.20 6 Top 0.1% Columbia University USA 4 13 6 10 13 12 13 14 4 90.80 7 Top 0.1% University of California, Berkeley USA 5 6 24 5 12 4 7 7 22 88.26 8 Top 0.1% University of Chicago USA 6 11 13 8 23 17 11 16 85 87.13 9 Top 0.1% Princeton University USA 7 4 15 4 99 26 23 39 122 86.04 10 Top 0.1% Yale University USA 8 9 27 11 17 10 32 18 48 81.20 CWUR 2017 - World University Rankings. (https://cwur.org/2017.php)
  5. 5. 5 A three-step data-miningprocess. Characterize the editorial policy of each HEI Characterize the audience of each HEI Predict future KPI values and suggest future actions to improve engagement 1 2 3 Compute the ratio response/effort Compute efficiency Compare voiced topics Understand the communication strategy Understand the response pattern Create KPI. Measure and compare Institutions. Reasoning about future communication strategy
  6. 6. 6 Step 1a: understanding the communication strategy. • Posting frequency • Period of the day of the posts • Number of posts in each weekday • Frequency of posting per month • Topics of posting and the self-image • Topical words in posts • Sentiment words in posts • Distribution of posts in topic areas • Sentiment analysis • Fading patterns
  7. 7. 7
  8. 8. 8 Step 1b: understanding the response patterns. • Frequency and intensity of comments • Topics of comments • Distribution of posts in topic areas • Topics of posting and the self-image • Sentiment analysis • Aggregated sentiment in each HEI
  9. 9. 9
  10. 10. 10 Step 2: comparing HEI through created metrics. • Most active fans • Feedback from Most Active Fan (MAF) • Number of posts by a fan vs. number of users • Type of post where comments are made • Score of each HEI vs. the number of fans • Fading patterns
  11. 11. 11
  12. 12. 12 Step 3: predictive analytics. • In the prediction phase, we built three models to predict basically three things: • the engagement a post will have in the next 3 days; • the average sentiment of the response it will have in the next 4 days; • the fading of the response in the next 3 days. • For the prediction we used “Random Forests”. • To prevent overfitting, we divided the dataset into 80% for training and 20% for testing (never previously used in the training). • The results showed that the accuracy of the classifier was slightly above 80%, and that the F1-measure was of 84%, both values were computed as the average for each of the two metrics.
  13. 13. Predicting. HEI Y1 Y2 Y3 Oxford MIT Harvard MIT ... 13
  14. 14. • We presented a three-step model to analyze the social media communication of higher-education institutions. The base for our study was the top 5 high-ranked university institutions of the World, according to the QS World 2017 University Ranking. • The final stage allows the prediction of which strategies will potentially have more response and engagement; and, accordingly, which will not. • The evaluation of the model showed that the results are sufficiently accurate to recommend the use of the system, although with some care. Conclusions. 14
  15. 15. THANKYOU Álvaro Figueira arf@dcc.fc.up.pt CRACS / INESCTEC and University of Porto

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