Do Top Higher Education Institutions' Social Media Communication

Differ Depending on Their Rank?

Alvaro Figueira

and Lirielly Vitorugo Nascimento

CRACS/INESCTEC and FCUP, University of Porto, Porto, Portugal

Department of Computer Science, FCUP, University of Porto, Porto, Portugal

Keywords: Higher Education Institutions, Social Media Communication, Twitter, Ranking Analysis, Publishing

Strategies.

Abstract: Higher Education Institutions use social media as a marketing channel to attract and engage users so that the

institution is promoted and thus a wide range of benefits can be achieved. These institutions are evaluated

globally on various success parameters, being published in rankings. In this paper, we analyze the publishing

strategies and compare the results with their overall ranking positions. The results show that there is a

tendency to find a particular strategy in the top ranked universities. We also found cases where the strategies

are less prominent and do not match the ranking positions.

1 INTRODUCTION

Year after year, there are more rankings available so

that people can make more informed decisions.

Higher Education Institutions (HEI) are no exception,

university rankings are becoming not only more

numerous as also more commonly used. The goal in

creating these rankings is to measure and evaluate

success in various areas or criteria. The metrics used

are improving, as are the methods to determine them

more accurately. Generically, HEI have been

evaluated on factors such as student success, research

volume, funding and awards, internationalization,

employment, and connections to industry, among

others.

There are several leading indexes today for HEI.

Probably the best known and most widely used are

the CWUR

, QS

, Leiden

, ARWU (also known as the

Shanghai ranking

), and URAP

. It has been shown

(Olcay, 2017) that the correlation between these

indices has been strong over the years. Therefore,

despite some small variations in the indexes, the

https://orcid.org/0000-0002-0507-7504

https://www.cwur.org

https://www.topuniversities.com/

https://www.leidenranking.com/

https://www.shanghairanking.com/

https://urapcenter.org/

overall picture given by one does not differ much

from the others.

The comparison of these rankings, the inherent

challenges and what is means for a HEI to be in a rank

have been already studied in Aguillo et al. (2010),Van

Raan (2005), and Liu (2009), to name a few. In this

article, we want to take a different approach by not

discussing the ranking itself, but by comparing the

ranking of the HEIs with their posting strategies in

Twitter in order to analyze to what extent the external

communication of HEI differs from each other. Our

motivation is that at a time when the recruitment of

new students, distinguished researchers and funding

depends heavily on the image that each HEI conveys,

external communication becomes a crucial element

for these tasks (Gajić, 2012). Since the Twitter

network (and also Facebook) is one of the most

widely used networks in academia, we believe it is

important to review the performance and strategies of

higher education institutions in this network.

Ultimately we want to understand if the rankings also

reflect some difference on the way a HEI projects its

messages.

Figueira, A. and Nascimento, L.

Do Top Higher Education Institutions’ Social Media Communication Differ Depending on Their Rank?.

DOI: 10.5220/0011592500003318

In Proceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST 2022), pages 355-362

ISBN: 978-989-758-613-2; ISSN: 2184-3252

355

There have been some studies regarding the

analysis of the social media publications in HEI

(Dumpit and Fernandez, 2017), of methods to analyse

their postings (Figueira, 2018a and 2018b) and of

inspecting the publication strategy in top-ranked HEI

(Coelho, 2021). In our approach we take a

longitudinal perspective by analysing and comparing

more HEIs and not only those close to the top of the

ranking. We want to identify and compare how their

external communication varies as we vary the ranking

position signifificatively.

In the remainder of this paper, Section 2 explains

our analysis for selecting a particular ranking and the

premises for sampling higher education institutions.

In Section 3, we conduct an analysis of the data

collected. In Section 4, we compare all HEI using a

vector space model and analyze the results. Finally,

in Section 5, we summarize the research process and

draw our final conclusions.

2 DATA RETRIEVAL

In this study we chose to use four of the most used

rankings’ pages (CWUR, Shanghai, US News and

QS). Despite acknowledging the results from (REF)

we intended to confirm that there are small variations

between the four rankings. We used the Kendall

distance and the Kendall correlation coefficient

(“Kendall’s τ”) metrics (Kendall, 1938) and (Field,

2005). Kendall Distance is 0 for identical, in the sense

of top-k, lists and 1 if completely different ones.

Kendall Tau is a measure of the correspondence

between two rankings, where values close to 1

indicate strong agreement and values close to -1

indicate strong disagreement. Another metric

frequently used in comparing ranked lists is the Rank

Biased Overlap (“RBO”), where 1 means identical

ranking and 0 means disjoint lists. The RBO is more

robust to cope with top weighted-ness (Webber,

2010).

Our goal was to test if one ranking has no

significant variations when compared with the other

ones. The results obtained for Kendall distances was

zero for all combination comparison between the

university rankings. The Kendall τ (and RBO) results

were 0.64 (0.95) for CWUR versus Shanghai, 0.63

(1.00) for CWUR versus USNews and 0.47 (0.05) for

CWUR versus QS. Despite a less strong similarity

between CWUR and QS, the general conclusion is

that there is not a significant variation in the rankings.

Therefore, we proceeded considerign just the CWUR

ranking.

We intended to collect posts from HEIs in ranking

positions 1 to 10. Then, in positions 100, 200, 300,

400 and 500. This wide-span on the ranking would

give simultaneously as a perspective on top-

performing HEI, as well as the eventual differences

on a wide extent of the ranking list. These positions

and their respective ranking in the fours indexes are

depicted in the Table 1. As it can be seen, for the

selected HEI, the differences in the ranking are not

significant for the goal of this paper.

Table 1: HEIs rankings on the four rankings.

High Education Institution CWUR Shanghai USNews QS

Harvard University 1 1 1 5

Massachusetts Institute of

Technolo

2 3 2 1

Stanford University 3 2 3 3

University of Cambridge 4 4 8 3

University of Oxford 5 7 5 2

Princeton University 6 6 16 20

University of Chicago 7 10 15 10

Columbia University 8 8 6 19

University of Pennsylvania 9 15 13 13

California Institute of

Technolo

10 9 9 6

Boston University 99 101-150 65 112

University of Lisbon 200 201-300 197 356

University at Buffalo 300 301-400 280 338

University of Porto 308 201-300 255 295

University of Oklahoma,

Norman

400 501-600 425

651-

700

Federal University of Minas

Gerais

500 401-500 456

651-

701

Some changes for the list of HEI to retrieved

tweets were made: the ranking position 99 have been

chosen instead of position 100, because Keio

University (position 100) has stopped tweeting after

April 2020. University of Porto was included in the

analysis, by curiosity, because it is the University of

the authors.

We built an in-house tweet collector for retrieving

the most recent 2500 tweets from the official Twitter

account of each HEI, setting the last possible post at

31 July 2022. Tweets were extracted in two periods at

the 5

and the 17

of August, 2022. Unfortunately,

the Twitter API did not return all the 2500 tweets for

University of Lisbon (only 1583) and for University

of Buffalo (only 1235). For the retrieval we excluded

any retweet. The reason behind this is that these two

HEI still do not have posted 2500 tweets.

As different HEI have different posting

frequencies, the time span for the 2500 tweets is also

different for each HEI. In Figure 1, we can see the

common period for the tweets posts between all the

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356

HEI. As depicted in the figure the biggest common

period is between February 2022 to July 2022.

Figure 1: Collected period for each HEI.

In the next section we will inspect the retrieved

data and perform a more in detail analysis of

publishing time and content.

3 DATA ANALYSIS

There have been some studies regarding the analysis

of the social media publications in HEI (Figueira,

2018a and 2018b) and of analysing the publication

strategy in top-ranked HEI using machine learning

methods (Coelho, 2021). In our approach we take a

longitudinal perspective by analysing a bigger set of

HEI and not only those on the top of the ranking, as

we expect to see changes as we go further in the

ranking list. We begin by analysing the number of

followers for each HEI using Figure 2.

Figure 2: Number of followers as of July 2022.

Looking at Figure 2 we can see that Harvard has

the greatest number of followers with more than 1.4

million, followed by MIT with more than 1.2 million,

Stanford with more than 900K, Cambridge with more

than 700K, Princeton with more than 400K,

Pennsylvania and Oklahoma with more than 190K

and 177K respectively, Federal University of Minas

Gerais, California Institute of Technology, Boston,

Porto and Chicago each one with more than 114K,

109K, 108K, 86K and 80K respectively, and with less

than 11K is Columbia, Lisbon and Buffalo, in this

sequence. Table 2, below, depicts the mean and

maximum number of posts for the daily tweet

frequency for all the High Education Institutions.

Table 2: Posting daily frequency (decreasing order).

Rank Higher Education Institution Mean Max

9 University of Pennsylvania 6.87 16

6 Princeton University 6.19 42

500 Federal University of Minas Gerais 6.11 20

5 University of Oxford 5.66 88

1 Harvard University 5.46 11

4 University of Cambridge 3.43 95

2 Massachusetts Institute of Technology 3.42 14

200 University of Lisbon 3.40 355

99 Boston University 3.14 31

7 University of Chicago 3.04 20

3 Stanford University 2.63 40

8 Columbia University 2.59 23

400 University of Oklahoma - Norman 2.59 14

308 University of Porto 2.32 22

300 University at Buffalo 2.26 41

10 California Institute of Technology 2.09 19

Figure 3: Boxplots of daily posting for each HEI.

Do Top Higher Education Institutions’ Social Media Communication Differ Depending on Their Rank?

357

We notice that Harvard has the smaller standard

deviation in posting frequency, and Oxford the

highest. This leads us to believe the strategy in

Harvard is more consolidated, around 5-6 posts a day.

On the other hand, we can see that Stanford,

Columbia, Oklahoma-Norman, Porto, Buffalo, and

California Institute of Technology publish between 2

to 3 posts a day.

We note the incredible number of posts (355) for

a single day in University of Lisbon on September

, 2021. Figure 3 depicts the box-plot graph for the

universities tweets daily frequency. Interpreting the

plots, it is easy to see that University of Pennsylvania

tweets daily frequency have a normal distribution

with mean of 6 tweets a day and there are no outliers.

Similarly, Harvard University has almost the same

aspect of a normal distribution with only two outliers,

one above the superior limit and one below the

inferior limit. The same behaviour happens for

Massachusetts Institute of Technology and Federal

University of Minas Gerais with a normal distribution

with outliers above the superior limit.

A common pattern can be seen in the California

Institute of Technology, Columbia, Stanford,

Buffalo, Lisbon, and Porto in which there is a very

squeezed distribution (Figure 4) with a large tail of

outliers which shows that there is not a constancy in

the tweets of those universities.

Another similar pattern can be seen at the plots of

Oxford, Princeton and, Boston where the

visualization of the mean is clear, above one post,

showing that these universities have some constancy

in the daily tweets. In Harvard, Pennsylvania, and

Minas Gerais we still have that pattern, but at a

smaller level presenting a not so balanced Gaussian

distribution.

Looking into all HEI posts, and framing into the

intersection period, we built a tweet frequency table,

crossing the weekday with the posting hour. This

results in the heat map (Figure 5) bellow. Inspecting

it, we see there is a common pattern for the

Universities of Pennsylvania and Oklahoma, in which

posts are concentrated between 2 PM to 9 PM of

weekdays.

We can also see that in Harvard, Princeton, Chicago,

and Boston, posting is a all-week activity, despite

being done on working hours only (which,

generically, all HEI do). However, we can also notice

that in MIT, Pennsylvania, Oklahoma and Minas

Gerais, high frequency posting is condensed in a short

period of time and weekdays. This situation leads us

to believe there is regular and systematic line of work

in external communication, which may be seen as an

editorial approach.

Figure 4: Distribution of posting frequencies.

Continuing the analysis, we created a set of word

clouds for each HEI in respect to all retrieved posts,

as well as for the common posting period. In Figure 6

we present the word clouds using all available

retrieved posts for each HEI.

We can notice that HEI do invest in the projection

of their image: most HEI have as the most used term

their name. Therefore, it is interesting to see that

Columbia, Boston, Lisbon, and Oklahoma differ from

this pattern. We can also see that the terms ‘student’

and ‘research’ are common on almost all HEI,

showing their concern for these topics and respective

focus on specific segments of readers.

Notably, University of Lisbon, does not present a

high relevance of these terms. University of Porto and

of Minas Gerais present the Portuguese counterparts

‘estudante’ and ‘pesquisa’. We can also observe traits

of engagement actions directed to newcomers in all

HEI, many times by congratulating them as we see

the terms ‘first’, ‘year’, and ‘new’. Finally, the terms

‘pandemic’ and ‘vaccine’ still are common in posts

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358

Figure 5: Publication weekday and time.

from Harvard and Oxford (and not in the other HEI,

which reflects an important editorial difference).

When we frame the analysis on the common

publishing period (Figure 7) we notice just two minor

changes: a) an increase of engagement actions in

Columbia when comparing to the other terms, b) a

reduction of importance of branding and projecting

the institutional image at University of Porto.

Hence, as we see it, despite different HEI

publishing at different periods and in different

frequencies, they use very small variations on their

strategies in what concerns the textual content.

Therefore, it remains unclear whether there is a

generical mapping between the ranking lists and the

strategies and publishing patterns that each HEI

adopts.

Figure 6: Word cloud for each HEI considering all retrieved

posts.

Figure 7: Word cloud for each HEI considering the

common period.

We also inspected the sentiment of the posts from

each HEI. For that we used the TextBlob library

(0.16.0), in a Python implementation, which returns

one of three possible values for each text: positive,

Do Top Higher Education Institutions’ Social Media Communication Differ Depending on Their Rank?

359

neutral, or negative sentiment. The value returned

corresponds to the result of the analysis of the text. To

better understand we present the evolution of

sentiment in the posts from Harvard in Figure 8,

where we group tweets in months.

Figure 8: Monthly evolution of sentiment form Harvard

posts. Negative sentiment in red, neutral in blue and

positive in green.

For the sake of saving space, we do not present the

graphs for all HEI in this section. However, we will

use the computed values to compare HEI in the next

section.

4 GROUPING THE STRATEGIES

To deepen our analysis, we decided to compare

quantitatively the publication strategies of HEI. As

we will be using numerical quantities, we can make

the comparisons of all at once. Our intention will be

to perform an unsupervised classification which my

group the HEI according to the metrics we will use.

As we are interested in the publication patterns,

features like employment, student success, research

funding, etc. will not be of our concern. We just want

to use metrics acquired from inspecting the retrieved

tweets, group the HEI according to these metrics, and

compare the result with the rankings.

4.1 The Feature Space Vector Model

To reflect most of the analysis we have done

previously, we choose 10 features to represent the

publishing behaviour of each HEI. Those are:

 Mean daily posting frequency

 Max daily posting frequency

 Ratio of publishing in weekends (Saturday +

Sunday)

 Ratio of publishing during night period (9pm to

7am)

 Mean positive sentiment

 Mean neutral sentiment

 Mean negative sentiment

 Mean tweet length (text)

 Length of all concatenated tweets (text)

 Total number of links used in the text

These features represent most of the analysis

described previously and now are used together to

represent a signature of each HEI posting behaviour.

4.2 Clustering the HEI

We are representing each HEI as a vector in a 10-

dimensional vector space model. In this

representation we can compute the distances between

HEIs and check which ones are closer to the others.

Then, using a grouping algorithm we are able

associate closer HEIs together. For that we use the

standard k-means algorithm. We experimented

generic k-means (MacQueen, 1967) with the Floyd

algorithm (Linde et al., 1980) and with the Hartigan-

Wong (Hartigan and Wong, 1979) algorithms, but the

results were almost identical. We tried to minimize

the inter-cluster distances using different number of

clusters while comparing them using the ‘elbow

method’. Finally, using the best results, we decided to

use 3 clusters for grouping the HEIs. In Figure 9 we

present a mapping of each HEI coloured according to

the assigned cluster. This representation uses a PCA

transformation (Abdi, 2010) in order to represent 10-

dimensional points in 2 dimensions.

Figure 9: Positioning of each HEI in a 2D projection of the

feature space. Also clustering the HEI in three groups using

colour.

We can confirm this clustering makes sense

because there is a clear distinction of the 3 groups:

HEI in blue in the second quadrant (cluster 1), HEI in

orange in the first quadrant (cluster 2), and HEI in

green (mostly around the separation between the third

and the fourth quadrants (cluster 3).

To complete the analysis, we checked the

distribution of the normalized values of the 10 features

in each cluster (Figure 10, bellow) using boxplots.

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Figure 10: Distribution of each variable in each cluster.

As can be seen, clusters 1 and 3 have a dense

distribution of the variables, and they mainly differ in

the sentiment values (positive on cluster 1 and

negative on cluster 3), on the mean posting frequency

(positive in 1 and negative in 3), and total number of

links (higher in cluster 3). We also note that in cluster

2 the variables have much more dispersion, in which

we interpret as a diffused and not-well established

strategies.

4.3 Analysis of the Results

In order to compare these results with the rankings,

we use Table 3, where we include the cluster

assignment (last column) together with the four

ranking lists. We ordered the table with respect to

column ‘cluster’, hence grouping HEIs that belong to

the same cluster.

Table 3: Cluster assignment.

As we can see, in the first cluster, apart from

University of Minas Gerais, all the other are placed in

top positions in the rank. In cluster three, we see HEIs

that are placed in a wide-span positions of the ranking

lists. We can also see that there ae only two HEIs

assigned to cluster two. These HEI are from the

middle of the list (positions 200 and 300 in CWUR).

Therefore, it seems these HEI have publishing

strategies that are not consolidated and with less clear

objectives. We may also say that HEI in cluster 1 have

a tendency to be placed in top positions of the

rankings and in cluster two they may be positioned

anywhere.

5 CONCLUSIONS

In this paper we have shown that there is a small

relation between publishing strategies and top-ranked

Higher Education Institutions. More expressive

sentiments in tweets, higher tweet length, bigger

posting frequency and smaller number embedded

links are characteristics of top ranked HEIs.

To get to these conclusions we identified a set of

HEI for which we retrieved 2500 tweets. We analysed

these tweets in respect to publishing frequency, date

and content. HEI were represented as vectors in a 10-

dimensional space we created, and then grouped

using the k-means clustering algorithm.

As for future work we intend to further analyse the

content to detect topics and check if there is a

connection between this variable and the rank of each

HEI.

ACKNOWLEDGEMENTS

This work is financed by National Funds through the

Portuguese funding agency, FCT – “Fundação

para a Ciência e a Tecnologia”, within project

LA/P/0063/2020.

REFERENCES

Abdi, H., & Williams, L. J. (2010). Principal component

analysis. Wiley interdisciplinary reviews:

computational statistics, 2(4), 433-459.

Aguillo, I., Bar-Ilan, J., Levene, M., & Ortega, J. (2010).

Comparing university rankings. Scientometrics, 85(1),

243-256.

Coelho, T., & Figueira, Á. (2021, December). Analysis of

Top-Ranked HEI Publications’ Strategy on Twitter. In

2021 IEEE International Conference on Big Data (Big

Data) (pp. 5875-5877). IEEE.

Dumpit, D. Z., & Fernandez, C. J. (2017). Analysis of the

use of social media in Higher Education Institutions

(HEIs) using the Technology Acceptance Model.

Do Top Higher Education Institutions’ Social Media Communication Differ Depending on Their Rank?

361

International Journal of Educational Technology in

Higher Education, 14(1), 1-16.

Field, A. P. (2005). Kendall's coefficient of concordance.

Encyclopedia of Statistics in Behavioral Science, 2,

1010-11.

Figueira, Á. (2018a). Uncovering social media content

strategies for worldwide top-ranked universities.

Procedia Computer Science, 138, 663-670.

Figueira, Á. (2018b). A Three-Step Data-Mining Analysis of

Top-Ranked Higher Education Institutions'

Communication on Facebook. In Proceedings of the Sixth

International Conference on Technological Ecosystems

for Enhancing Multiculturality (pp. 923-929).

Gajić, J. (2012). Importance of marketing mix in higher

education institutions. The European Journal of

Applied Economics, 9(1), 29-41.

Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136:

A k-means clustering algorithm. Journal of the royal

statistical society. series c (applied statistics), 28(1),

100-108.

Kendall, M. G. (1938). A new measure of rank correlation.

Biometrika, 30(1/2), 81-93.

Linde, Y., Buzo, A., & Gray, R. (1980). An algorithm for

vector quantizer design. IEEE Transactions on

communications, 28(1), 84-95.

Liu, N. C. (2009). The story of academic ranking of world

universities. International Higher Education, (54).

MacQueen, J. (1967). Classification and analysis of

multivariate observations. In 5th Berkeley Symp. Math.

Statist. Probability (pp. 281-297).

Olcay, G. A., & Bulu, M. (2017). Is measuring the

knowledge creation of universities possible?: A review

of university rankings. Technological Forecasting and

Social Change, 123, 153-160.

Scott, A. J., & Symons, M. J. (1971). Clustering methods

based on likelihood ratio criteria. Biometrics, 387-397.

Van Raan, A. F. (2005, June). Challenges in ranking of

universities. In Invited paper for the First International

Conference on World Class Universities, Shanghai Jaio

Tong University, Shanghai (pp. 133-143).

Webber, W., Moffat, A., & Zobel, J. (2010). A similarity

measure for indefinite rankings. ACM Transactions on

Information Systems (TOIS), 28(4), 1-38.

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

362