This document presents a three-step process for analyzing the communication strategies of top-ranked higher education institutions on Facebook. The first step characterizes each institution's editorial policy and audience response patterns. The second step compares the institutions using created metrics. The third step uses random forests to predict future engagement, sentiment, and response based on past posts. The process was applied to the top 5 universities in the QS World University Rankings and found accuracy above 80% for predictions, allowing recommendations on effective communication strategies.

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
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

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
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
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

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

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

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. Predicting.
HEI Y1 Y2 Y3
Oxford
MIT
Harvard
MIT
...
13

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. THANKYOU
Álvaro Figueira
arf@dcc.fc.up.pt
CRACS / INESCTEC and University of Porto