The MuseumWeek Event : Analyzing Social Network Interactions in Cultural Fields
Antoine Courtin
Labex ”Les passés dans le présent”
University Paris Ouest Nanterre La Défense
Nanterre, France
antoine.courtin@mac.com
Brigitte Juanals, Jean-Luc Minel, Mathilde de Saint Léger
MoDyCo - UMR 7114
University Paris Ouest Nanterre La Défense - CNRS
Nanterre, France
brigitte.juanals@orange.fr
Abstract—The goal of this paper is to analyze messages sent
on the Twitter socialnetwork during the MuseumWeek event.
This analysis relies on quantitative and qualitative studies,
which were benchmarked with the MuseumWeek event.
Keywords-Cultural mediation, Twitter, Machine Learning
Categorization
I. T HE C IRCULATION OF C ULTURAL C ONTENT ON
S OCIAL N ETWORKS : AN I NDUSTRIAL AND C ULTURAL
I SSUE FOR M USEUMS
In order to apprehend the entanglement between communication industries and museums on social networks, we set
up a two-year interdisciplinary study [1] which combines
fieldwork and interviews of community managers in charge
of communication in several French social network museums. The corpora harvested in these fields were analyzed
with textual and statistical analysis tools. In the international
context of multimedia convergence, stakeholders coming
from the Internet market, computing and communications
industry have penetrated the cultural sector of museums,
relying on their platforms of goods and services. This new
configuration gives rise to the renegotiation of the control
of information dissemination channels between the two
categories of stakeholders, namely cultural organizations and
industrial companies [2]. In this respect, museums are in the
same position as cultural industries. An increasing number
of museums have generalized the use of social networks,
among other digital media and electronic artefacts [3]. Two
American companies, Facebook and Twitter, have captured
the lion’s share of this competitive international market.
Their business model is based on the attendance and the
participation of audiences and organizations.
Social networks [4] have now become editorial spaces for
cultural organizations but they are difficult to comprehend
because they are both places to attract audiences and places
to produce and share cultural content [2].
The overall goal of this paper is to analyze messages
sent on the Twitter socialnetwork during the European
communication event MuseumWeek, which was launched
in March 2014 by the American company Twitter in the
museum field, as this operation can be considered emblematic of development strategies of Internet companies in the
cultural field [2]. After briefly presenting the scope of the
international event, we will focus on the French coverage of
this event.
The outline of the paper is the following. First in Section
2, we briefly describe related work. In section 3, we remind
principles of our tool-based methodology. In Section 4,
we present the MuseumWeek event directives. In section
5, results from quantitative and qualitative analyses are
discussed. Finally, we conclude in Section 6.
II. BACKGROUND AND R ELATED WORK
Analysis of tweets has led to a great deal of research [5],
on topics such as election forecasts [6], the stock market [7],
dramatic events [8], and especially sentiment analysis [9]–
[12], but we are not aware of research in the cultural field
combining analyses of messages from professionals such as
community managers and the general public. Many studies
using machine-learning approach have been published [11],
[13]–[16], but the majority of them focus on the polarity of
messages whereas we needed to categorize the content of a
message more precisely.
III. M ETHODOLOGY
First, we would like to summarize our tool-based methodology [17] to analyze social network interaction. In the first
step, based on a Tuser taxonomy built by experts in the
studied field, all the tweet accounts are categorized. As the
size of the dataset precluded human analysis, we propose to
used NLP tools (regular expressions and more sophisticated
scripts if necessary) to automatically categorize accounts.
In the second step, a quantitative analysis of the attendance stream is carried out in order to compute temporal
trends and user practices.
In the third step, we propose to categorize the content
of tweets. First of all, it is necessary to build another
taxonomy Tcontent, based on linguistic analysis and on
lexicometric tools (lexical frequency or tf*idf score). Here
again, as the size of the dataset precluded human analysis,
we propose to use machine learning tools, especially multilabel classification. The last step is the classical step of
interpreting findings given the assumptions and theoretical
framework of the study. For this step, in order to facilitate the
understanding of study results in cultural field, we developed
heuristic visualizations tools based on JavaScript libraries
(d3js, sigmajs, etc.).
IV. T HE C ASE OF THE M USEUM W EEK
C OMMUNICATION E VENT
During 2013, several museums supported by the French
Ministry of Culture engaged in dialogue with Twitter in
order obtain a certified account1 or the creation of a new
category to improve their visibility on this media. In February 2014, Twitter invited a group of twelve French museums
(hereinafter referred to as GM 12) to a meeting in Paris to
propose the framework of the MuseumWeek communication
event. Twitter likewise organized a meeting with seventy
museums in the United Kingdom for the same purpose as
well as in other countries. Figure 1 shows participating
countries at the event and figure 2 focuses on French
institutions.
Figure 2.
Figure 3.
Figure 1.
French geographical locations
Themes and French hashtags of the MuseumWeek event
International locations
At the end of February, an International event, called
MuseumWeek (with the generic hashtag #MuseumWeek)
was designed and planned to take place during one week
(from the 24th to 30th March 2014). The organizational
principle of this event was simple: each day was dedicated
to a theme, with a specific hashtag but this specific hasthag
was different in each country. All users were encouraged
to use the hashtag of the day as well as the generic one
in their tweets. Despite the fact that this instruction was
disadvantageous to communicate within the format of 140
characters per message, 69% of French users kept to this
obligation. Figure 3 shows day-themes and their associated
French hashtags and figure 4 and 5 illustrates differences
between French and British flows of tweets.
Figure 4.
French hashtags attendances
Figure 7.
Quartile distribution of two populations of users, CI and OA
Figure 8.
Attendance and categorized curves
A. Quantitative Analysis on attendance
Figure 5.
Global analysis
English hashtags attendances in UK
31 592 French tweets were automatically harvested by
using the API provided by Twitter (twitter.com/searchadvanced). Each record contains the identification of the
sender, the text of the tweet and the day-of-issue. By
analyzing the profile of the tweet account with a combination of Natural Language Processing techniques (regular
expressions and Python scripts if need be) and institutional
knowledge, senders were categorized as shown in figure 6.
Two kinds of analysis were conducted on this corpus: a
quantitative one, which focused on the attendance, and a
qualitative one, which focused on the content of tweets
Quantitative analysis provides several important results.
First, the quartile distribution (figure 7), second the attendance curve (figure 8) which declines regularly (36%) from
the beginning, with two rebounds, one on the theme Sharing
our coup de coeur, which is also the peak of attendance,
and the other on the architecture of museum buildings. It
should be noted that these themes are those which concern
the individual accounts most. This fact is confirmed by the
categorized curve (figures 8) which shows a flat curve for
the CI and PM accounts and a two-peak curve for PIC and
CdP accounts. These results are in line with several analyses
of the commitment of users on social networks [18].
The second result concerns the distribution of the number
of tweets sent by users. Figure 9 shows a marked difference
in the practices of CI and OA accounts. CI accounts (103)
sent 6188 tweets and OA (7746) sent 25404 tweets. Most importantly, the kind of tweets are very different. CI accounts
are what we call authors, that is to say they wrote their
tweets, while OA accounts are what we call relay runners,
which means that they retweeted an original tweet. It must
be pointed out that Re-tweet is a functionality of the Twitter
interface software.
The third finding concerns the number of tweets sent by
account. 64 % of OA accounts sent one tweet, while the
same proportion of CI accounts sent 55 tweets.
In order to analyze more thoroughly the behavior of each
account, we computed three indicators as follows (where
NTW is the number of original tweets sent by a user; NRT
is the number of retweets sent by a user; NTWINIT is the
total number of original tweets; NTRT is the total number
of original retweets).
1 For
more
on
certified
accounts
on
Twitter
https://support.twitter.com/articles/269158-faq-sur-les-comptes-certifies.
IPG = (NTW+NRT) / (NTWINIT+NTRT), Indicator of
global participation;
Figure 6.
Categories of senders
V. A NALYSIS OF I NTERACTIONS
:
Figure 11. Boxplot of initial tweets and retweets sent by institutional
organizations
Specific analysis of museums’ participation
Figure 9.
Figure 10.
Distribution of number of tweets sent
Zoom on characteristics of OA accounts
IPL = NTW / NTWINIT, Indicator of author;
IPR = NRT / NTRT, Indicator of relay runner.
The ranking of all the accounts for the IPG indicator
shows that the first three are OA accounts, followed by
almost all the CI accounts. The first ten IPL are CI accounts,
and among the first ten IPR, seven are OA and three are PM.
In a nutshell, while the participation of individual users in
the event was high, their commitment was relatively weak.
As figure 4 shows, the OA population is complex but specific
categories help to identify trends. Hence, figure 10 shows
two major categories: loyalty towards the event (those who
tweeted with whole hashtags) but also the twenty most
active senders. Although often decried [19], one can note
the presence of two robots (1590 tweets, 100% of retweets,
1617 followers) which played an intermediary role between
the various countries involved in the event.
Among the 103 institutional accounts, twelve museums,
called GM12 here (see section 3), were the co-founding
members of the MuseumWeek event. Consequently, we
decided to study their communicational practices during
this event more thoroughly. We ranked the members of
GM12 with three indicators, IPG, IPL and IPR (figure 11
and 12). As regards the IPG and IPL indicators, the RIPG
(ranking of all IPG) ranking shows three partitions: a top
partition composed of 8 members of the GM12 characterized
by strong participation; a small partition of two members
located at the end of the first quartile; and a third partition,
also composed of two members, located at the bottom of the
ranking. As it was difficult to explain this result by intrinsic
characteristics of the museums themselves (historical, fine
arts, modern art, etc.), we hypothesized that the profile of the
community manager (CM) could be an explanation, and the
interviews with CMs confirmed this hypothesis. As regards
the IPR indicator, the RIPR ranking (ranking of all IPR)
shows no real pattern and no correlation with the previous
RIPG ranking. Four members of GM12 are at the top of the
RIPR ranking, but the Centre Pompidou which is a leader in
RIPG is no longer present. The Grand Palais museum which
is at the bottom of the RIPG ranking is in the first third of
the RIPR ranking.
Two conceptions of the communication policy conducted
by museums emerge. On the one hand, a selective policy of
control of the contents of the tweets sent by the museum,
which is reluctant to relay content of which it is not the
author. And, on the other hand, a policy of display or more
exactly of presence, whatever the kind of messages sent.
This division reflects different conceptions of authorship.
Figure 12.
Practices of GM12 members
Figure 13.
Set of annotations for automatic classification.
B. Content analysis of tweets
The goal of this analysis is to go beyond the quantification
of the stream of tweets (see section V.A) and to analyze their
contents. As the size of the dataset precluded human analysis, a machine learning approach was chosen. A linguistic
analysis [20] carried out on 553 tweets of the initial corpus
provided a set of classes (cf. figure 13), a set of features and
a training test for the classifier.
Finally, the 30 features used for classification are linguistic (mainly lexis), semiotic (smileys), positional and tweetspecific (interaction between accounts). For example, clitic
pronouns (vous, nous, je, moi, etc.) are used to identify
”expressing an experience”, as well as lexical markers such
as (aimer, beau, plaisir, trsor, amour, etc). Some specific
data were also used: first, metadata about accounts such
as CI or OA; second, specific marks [13] such as URL
links, the modified tweet (MT) mark and the number of
hashtags in the tweet. Note however that the annotation
assigned to the tweet by the classifier is the result of a
probability calculation. The classifier [20] combines Naive
Bayes and Vector Support Machine algorithms and performs
a multi-label classification [21]. This kind of classification
was necessary because the linguistic analysis showed that a
tweet could belong to two classes. For example a tweet such
as ”@ioster #QuestionMW On va étudier la question ! En
attendant n’hésitez pas à profiter des magnifiques collections
: @PBALille @MHNLille...” could be classified as both
”Interaction between accounts” and ”Promoting a museum”.
These two algorithms, Naive Bayes and Vector Support Machine, were applied on the corpus and their results were then
compared. If the same class was assigned by both, it was
kept, otherwise none of the classes were assigned. Figure
14 shows that this phenomenon occured very rarely.Several
bench-marks showed that this method gives the best F0.5
measure, which puts more emphasis on precision than recall
[22]. Our classifier gave an F0.5 measure which varied
between 0.67 and 0.53, depending on the partition of the
corpus being analyzed. These scores are slightly lower than
those of the state of the art (approximately 0.70) but it should
be pointed out that our set of classes is larger. The Kappa
indicator, with three jurors, was between 0.76 and 0.41,
depending on the classes compared, which can be considered
rather good. Note that several classes can be assigned to the
same tweet (they are not mutually exclusive), but rarely more
than two, which explains why the sum of percentages may
be greater than 100%.
Figure 14 shows the results obtained on the corpus
without retweets (9 511 tweets). The largest class (41%)
is Interaction between accounts. This result shows that the
social network Twitter was largely used by the users as a
way to interact with professionals and with each other, which
was one of the goals of the founders of the MuseumWeek
event.The score of the second class Promoting a museum
(39%), very close to the first one, confirms the marketing
trend evolution of some museums. By contrast, the cultural
mediation objective, which is partially measured by the class
Encouraging contributions to contents (17%) was poorly
represented.
In order to analyze the communication practices of the
community managers, the classifier was applied on three
specific partitions of the corpus (figure 14): one composed
of tweets sent by the Muse du Quai Branly, the second by
the Centre Pompidou and the third by the Cit des Sciences.
These three museums were chosen because they are the top
three leaders (see section V.A and figure 15) of the event.
The contrast with the global corpus is striking. First of all,
none of three community managers of these museums sent
a tweet to promote his/her museum. Second, a mean of 62%
(from 59% to 64%) of their tweets aimed at Expressing an
experience. Third, there was a slight difference (from 23% to
32%) in communication practices between them concerning
which was attended by around 60 community managers who
participated in the MuseumWeek event. Both representatives
from the French Ministry and Twitter expressed their satisfaction with the successful performance and announced a
second edition for 2015 with some adjustments, whereas
community managers were more cautious.
ACKNOWLEDGMENT
This research is part of the ComNum Project funded with
a Labex ”Les passes dans le present” grant.
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Figure 14.
Percentage of classes in several corpora.
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VI. C ONCLUSION
This paper has presented a framework for the analysis of a
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