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Abstract and Figures

The mass media play a fundamental role in the formation of public opinion, either by defining the topics of discussion or by making a degree of emphasis on certain issues. Directly or indirectly, people get informed by consuming news from the media. But which is the dynamics of the agenda and how the people become interested in the different topics of the agenda? The Agenda Setting theory provides a conceptual framework in order to understand the role played by the mass media in public opinion formation, but the previous questions can not be answered without proper quantitative measures of agenda's dynamics and public attention. In this work we study the agenda of Argentinian newspapers in comparison with public's interests through a quantitative approach by performing topic detection over the news, identifying the main topics covered and their evolution over time. We measure Agenda's diversity as a function of time using Shannon's entropy and difference between Agendas using Jensen-Shannon's distance. We found that the Public Agenda is less diverse than the Media Agenda, and we are also capable to detect periods of time where coverage of certain issues are biased (coverage bias).
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A quantitative analysis of Media Agenda and
Public Opinion using time-evolving topic
Sebasti´an Pinto1,2, Federico Albanese3, Claudio O. Dorso1,2, and
Pablo Balenzuela1,2
1Departamento de F´ısica, Facultad de Ciencias Exactas y Naturales, Universidad de
Buenos Aires, Av.Cantilo s/n, Pabell´on 1, Ciudad Universitaria, 1428, Buenos Aires,
2Instituto de F´ısica de Buenos Aires (IFIBA), CONICET, Av.Cantilo s/n, Pabell´on
1, Ciudad Universitaria, 1428, Buenos Aires, Argentina.
3Instituto de Investigaci´on en Ciencias de la Computaci´on (ICC), CONICET,
Av.Cantilo s/n, Pabell´on 1, Ciudad Universitaria, 1428, Buenos Aires, Argentina.
The mass media plays a fundamental role in the formation of pub-
lic opinion, either by defining the topics of discussion or by making an
emphasis on certain issues. Directly or indirectly, people get informed
by consuming news from the media. But what are the dynamics of the
agenda and how the people become interested in their different topics?
This question cannot be answered without proper quantitative measures
of agenda dynamics and public attention. In this work we study the
agenda of newspapers in comparison with public interests by performing
topic detection over the news and identifying the main topics covered and
their evolution over time. We measure agenda diversity as a function of
time using the Shannon entropy and differences between agendas using
the Jensen-Shannon distance. We found that the Public Agenda is less
diverse than the Media Agenda, especially when there is a very attractive
topic and the audience naturally focuses only on this one. Using the same
methodology we detect coverage bias in newspapers and framing. Finally
it was possible to identify a complex agenda-setting dynamics within a
given topic where the least sold newspaper triggered a public debate via
a positive feedback mechanism with social networks discussions which in-
stall the issue in the Media Agenda.
arXiv:1807.05184v2 [physics.soc-ph] 11 Sep 2018
One of the challenges in complex social system research is to understand the
ecosystem of information flow and opinion formation. A major role within this
ecosystem is played by the mass media outlets, which are massively used as
sources of information. People get informed by the media and then interact
among them via personal discussions or through social networks, giving rise to
a complex dynamics where opinions are shaping and changing with time. In
this scenario, it becomes essential to understand the influence of mass media in
a given social group.
The influence of the media on public opinion was first explored by the social
sciences. In the seminal study performed in Chapel Hill during the US presi-
dential elections in 1968 [1], Maxwell McCombs and Donald Shaw found that
the aspects of public affairs that are prominent in the news become prominent
in the public. This work is considered the founding of the agenda-setting the-
ory. In its basic stage, known as first-level agenda-setting [2], the theory focuses
on the comparison between the topics coverage by the media and the public
agenda, i.e., the topics that the public consider as priority. For instance, within
the agenda-setting framework, it was explored how media content correlate with
audiences of different ages [3] and how people agendas differ based on the way
they consume news [4]. On the other hand, the theory hypothesizes how the
media affects the audience opinion, in particular, how political coverage and
political advertisement shape candidate knowledge among the audience [5, 6],
or how the coverage given by the media to a particular nation affects people
perception about its importance to local political interests [7]. Other works ex-
amine the differences between public and journalists preferences [8], or study the
coverage of the main newspapers on particular events related to a confrontation
scenario between government and press [9, 10].
The Chapel Hill investigation also induced other several research directions
[2]. One of them focus on detecting media bias, either by taking into account
the number of mentions related to a preferred political party [11, 12] or by
identifying the ideology through the position of the media regards to certain
issues or actors [13, 14]. This research line can be linked with the theory of
framing [15, 16], which focus on the way the media emphasizes some attributes of
an object, while understating others. On the other hand, several investigations
pay attention to the sources of the media agenda, theory known as intermedia
agenda-setting [17, 18, 19], where the competition and the mutual influence
between different media are observed.
Since the irruption of internet, a quantitative analysis based on the access
to big data became available, as for instance, those who takes into account
temporal dependence of the media and public attention. In [20] it is shown
that the newspapers and Twitter have an opposite reaction to the changes of
the unemployment rates; in [15], the competition of frames about gun control
is explored; in [21], the authors show how fluctuations of Twitter activity in
different regions depend on the location of terrorist attacks; and in [22], the
complex interplay between the social media and the traditional one is followed
over time on a set of predefined, but general, issues. However the works cited
above have performed a dynamical analysis of agenda-setting based on a single
issue or on a set of predefined issues. These are usually selected by the researcher
and use to reflect general subjects, such as “health” or “gun control”.
However, a data driven selection of issues can be performed using a tool
frequently employed in the analysis of large document corpus: Unsupervised
topic modeling. It is an alternative to the dictionary-based analysis, which is
the most popular automated analysis approach [23], and allows to work with
a corpus without a prior knowledge, letting the topics emerge from the data.
Although many works employ unsupervised topic modeling on news corpus,
much of them emphasize the performance of the topic model over a labeled
corpus, focusing on the proper detection of the topics [24, 25, 26]. In general,
issues about the temporal profile of topics are embedded in the context of topic
tracking [27, 28], or in the recognition of emerging topics in real-time [29], mostly
applied to social media.
In this work we propose a novel method in order to study the dynamics
of the mass media agenda, which consists of performing an unsupervised topic
model on newspapers articles, and studying how the emerging topics evolve with
time. We look also at their correspondence with the audience agenda, by looking
both the Google searches and Twitter activity in the studied period. Rather
than focus on a single issue or on a set of independent topics, this method
allows us to define the agendas (both the media and the public) as an object
which evolves with time. Our work focuses on a quantitative approach which
complements the agenda-setting theory describe above, which mainly stands
within the framework of first-level agenda setting, but also allows us to face
agenda bias and framing. We apply this method to study the dynamics of the
Argentinian media agenda due to our familiarity with the political background,
but as can be seen throughout the work, the methodology implemented is far
general and can be easily extended to other datasets.
On the other hand, new approaches to study social dynamics coming from
statistical physics have proposed mathematical models to explore the interplay
between the mass media and society [30, 31, 32, 33, 34]. However, much of
them lack in being contrasted with real data. In this work we are intended
to get a closer insight on the complex interaction between the media and the
public, and provide a quantitative research in order to construct better and
more data-driven models.
Materials and Methods
The Media Agenda
We analyze a three-month period of the Argentinian media agenda composed
by a corpus of news articles that were published between July 31st, 2017 and
November 5th, 2017. The articles come from the political section of the online
editions of the Argentinian newspapers Clar´ın,La Naci´on,agina12, and the
news portal, Infobae. The first two lead the sale of printed editions in Buenos
Aires city, but Clar´ın reaches roughly two times the readers of La Naci´on, and
ten times the readers of agina 12 [35], who was chosen because of its left
political orientation. On the other hand, Infobae has the most visited website,
much more than Clar´ın and La Naci´on [36]. The corpus analyzed is made
up by 2908 politics articles of Clar´ın, 3565 of La Naci´on, 3324 of agina 12,
and 2018 of Infobae. Except agina 12, all articles were taken from the section
Pol´ıtica (Politics) of the respective news portals, while the articles which belong
to agina 12 were taken from the section El pa´ıs (The country).
The articles are described as numerical vectors through the term frequency -
inverse document frequency (tf-idf) representation [37]. Given the set of terms
contained in the corpus words, after removing non-informative ones such as
prepositions and conjunctions, the tf-idf algorithm represents the i-document
as a vector vi= [xi1, xi2, ..., xit], where the component xij is computed by the
eq.(1), where tfij is the number of times the j-term appears in the i-document;
dis the number of documents in the corpus; and njis the number of documents
where the j-term appears. Each vector is then normalized to unit Euclidean
length. Once the document vectors are constructed, we put them together in a
document-term matrix (M), which has dimensions of number of documents in
the corpus (d) by number of terms (t).
xij = tfij ·idfj= tfij ·[1 + log( 1 + d
1 + nj
)] (1)
In order to detect the main topics in the corpus, we perform non-negative
matrix factorization (NMF) [37, 38] on the document-term matrix (M). A topic
is defined as a group of similar articles which roughly talks about the same
subject. NMF is an unsupervised topic model which factorizes the matrix M
into two matrices Wand Hwith the property that all three matrices have no
negative elements (see eq.(2)). This non-negativity makes the resulting matrices
easier to inspect, and very suitable for topic detection 1.
Such as the resulting matrix Hhas dimensions of number of documents by kand
matrix Whas dimensions of kper number of terms, the number kis therefore
interpreted as the number of topics in the documents and it is a parameter that
must be set before the factorization. In this work, we arbitrarily set k= 10,
based on our knowledge of the corpus. Since the factorization of eq.(2) usually
can not be made exactly, it is approximated by minimizing the reconstruction
error, i.e. the distance between matrix Mand its approximated form ˜
The NMF factorization was made through the python module scikit-learn [39].
The matrix His the representation of the documents in the topic space.
We normalized its rows to unit l1-norm in order to view their components as a
degree of membership of a given document in the set of topics. In particular,
the index of the largest component tells us which is the most representative
topic of the document. On the other hand, Wgives the topics representation
in the original term space. The largest components of a row give the most
representative words of each topic, which we call keywords, and therefore an
insight of what the topic is talking about.
After performing NMF, we represent the time-dependent Media Agenda as
a time-evolving distribution of topics. We define Wi(day) to be the daily weight
of the topic i, which is calculated following the eq.(3), where l(j) is the number
of words of the document j;hji is the degree of membership of document jon
topic i;djis the date of document j; and δis the Kronecker delta. Providing
by the fact that each document vector can have all non-zero components, it is
allowed that a document contributes to more than one topic weights. In order to
reduce noise, we apply a linear filter with a three day wide sliding window, and
finally we normalize the temporal profiles in order to describe each newspaper
agenda as a distribution over the topic space, which evolves over time.
Wi(day) = X
l(j)·hji ·δdj,day (3)
The Google and Twitter Agendas
Besides the construction of the Media Agenda it is important to have some mea-
sure of the public interests and construct what we call the Public Agenda. To
achieve this goal, we take Google and Twitter as proxies of the public interests
by looking for the same topics in the same period of time. We take advantage of
the topic keywords in order to make queries into the Google Trends tool and into
the advance search tool of Twitter, and therefore we get the relative weight of
searches and tweets in each respective platform. In this way, the Public Agenda
is described, in an independent way, by the Google and Twitter Agendas. In sec-
tion Results we will give a more detailed description of the keywords involved
in their construction.
Normalized Shannon Entropy (H)
In fact, one of the key of our work is the representation of the Agendas as time
evolving topic distributions, and the association between the different measures
that we can derive from them with the social events observed during the period.
In order to do that, we focus in the concept of diversity of the attention.Diver-
sity is a very important variable that must be taken into account when dealing
with multiple issues [40], due to the fact that it tells us how the attention is
distributed across the different topics of discussion. As was proposed in [40],
we use the normalized Shannon entropy to quantify the diversity within our
The normalized Shannon entropy H[p] referred in eq.(4) gives us a measure
of how spread is a -discrete- distribution, taking the maximum value of 1 when
all outcomes are equally probable (as in the case of having a diverse agenda),
and 0 when there is just one possible outcome (when one topic of discussion
dominates the agenda).
H[p] = PN
i=1 p(xi)ln(p(xi))
Jensen-Shannon distance
While the diversity is a property of each distribution, a natural question that
arises when comparing different distributions is how similar they are. For in-
stance, we will be particularly interested in measuring the similarity between
the Media and Public Agendas, because lower values would indicate distant in-
terests between the media and its audience. We measure the similarity between
distributions via the Jensen-Shannon distance (JSD). When the similarity of
the distributions is low, the distance between them is high.
The Jensen-Shannon distance (JSD) is a metric between distributions based
on the Jensen-Shannon divergence (JSD iv) [41], which is in turn a symmetric
version of the well-known Kullback-Leibler divergence DKL (eq.(5)). We recall
that the JSD has the advantage of being symmetric and also a well-defined
distance, which makes it conceptually easier to deal with. As can be seen in
eq.(6), the JSD between the distributions Pand Qis simply the square root
of the Jensen-Shannon divergence, where M=P+Q
DKL (P||Q) = XP(i)log(Q(i)
P(i)) (5)
JSD(P, Q) = pJ SDiv (P, Q) = r1
2[DKL (P||M) + DKL (Q||M)] (6)
Outliers identification
As was mentioned before, once the concepts of diversity and distance between
distributions were defined, we are particularly interested in those dates when
these measures take extreme values: Lower values of diversity indicate a topic
which is absorbing much of the attention of any of the Agendas; on the other
hand, when the distance between two distributions is high, we can conclude that
they have distant interests. However, we still lack the definition of what a low
diversity or a high distance are. We face this problem by treating the measures
of each observable as random samples from a population with unknown distribu-
tion, and identifying those extreme values as outliers of the distribution. In order
to detect these outliers we follow the popular box-plot construction proposed
by Tukey [42], which is a simple data-driven method and has the advantage of
making no prior assumption about the distribution of the data. However, it is
important to remark that the constants involved in the outliers definition (see
eq.(7)) is taken from applying this method on a normal distribution.
In the box-plot construction a quartile division of the Nobservations is
proposed. We name Q1 as the lower quartile, Q2 the median of the distribution,
and Q3 the upper quartile. Recall that Q1 (Q3) is defined to be the division
where the 25th (75th) percent of the observations lies below (by definition,
the median Q2 separates the distribution in two equal parts). On the other
hand, the inter-quartile range IQ is defined to be IQ =Q3Q1. This is
the range where the bulk of the data lies inside. We are not interesting in the
visualization of the box-plot in its own but instead in its procedure to identify
outliers. Therefore, from the identification of the quartiles, new quantities called
fences are defined in eq.(7): The lower inner fence (LI F ), the upper inner fence
(U IF ), the lower outer fence (LO F ), and the upper outer fence (U OF ). The
fences can be interpreted as the limits of the distribution.
LIF =Q11.5IQ
UIF =Q3+1.5IQ
UOF =Q3+3I Q (7)
We then have all the ingredients to label a point as an outlier: A point which
lies above the upper inner fence is considered a mild outlier, while a point that
lies above the upper outer fence is considered an extreme outlier. The same
holds for the lower fences, i.e. if a point lies below the lower inner (outer) fences
is considered as a mild (extreme) outlier [43]. We will indicate the proper fences
in each figure either when the diversity or the distance is being analyzed. We
will pay attention not only to those values labeled as outliers, but also to those
that are next to any of the fences despite not being strictly defined as that.
We initially focus on the ten most important issues from the three-month pe-
riod corpus of news reported above. The election of factorizing the corpus in ten
topics was based on having a low dimensional representation of the corpus and
a clear interpretation of the topics due to our prior knowledge of the political
background. We found that this factorization allowed us to draw useful con-
clusions. However, more sophisticated methodologies to estimate the number
of topics in a corpus can be taken into account in future researches. The ten
topics are represented in the word clouds of figure 1. Given our interpretation
of the keywords found in three of them, we joined these topics as being part of
the same macro-topic which we called Elections. On the other hand, the same
holds for other two topics which were classified as part of a macro-topic called
Missing person. Therefore, the ten original topics were reduced to seven, which
are pointed out in the radar plot of figure 1. The meaning of the topics or
macro-topics is contextualized in the Supplementary Material.
Finally, by following the procedure described in the previous section, we
construct the Media Agenda (MA) and the Public Agenda (PA), in both
its Google and Twitter derivations, as time-evolving distributions in the space
of seven topics.
Figure 1: Radar plots of the Media and Public Agendas represented
by the ten topic (then collapsed to seven) distribution and their cor-
responding word clouds. The Public Agenda is represented both by Google
Trends (GT) and Twitter (Tw). Topic names are introduced together with the
word clouds containing the most important keywords involved in the definition
of each topic. In green color we show the keywords used to define the topics in
the Google Trends and Twitter queries (see tables 1 and 2) and therefore in our
construction of the Public Agenda.
The Media and Public Agendas
In figure 1 we show a seven topic decomposition of the whole corpus using radar
plots for the Media MA and Public agendas PA discriminated by GT (Google
Trends) and Tw (Twitter). In this figure we also show the word clouds of the
keywords that define each of the ten original topics, where the size of the word
reflects its importance in the topic definition. In green color, we point out the
words involved in the Google Trends and Twitter queries in order to construct
the Public Agenda. The queries employed are also specified in tables 1 and 2.
On the other hand, in table 3 we show the linear correlation between the topic
temporal profiles from the Public Agenda and their counterparts in the Media
We can see that both GT and Tw look similar in this representation, but
they show specific differences with the Media Agenda. For instance, a greater
interest of the audience in the topic Missing person than the media is observed,
or inversely, a lower interest in the topic Prosecutor’s death takes place. How-
ever, this static representation is not able to show the complex dynamics of the
agendas evolution and the importance of punctual and specific facts which can
erase or amplify their differences.
The time evolution of the topics is shown in a bump chart of the Agendas
by figure 2. The bump chart provides a clear visualization of the relative weight
of the topics at the same time with their ranking. In figure 2, we also highlight
some important events related to the dynamics of the topics. It is possible
to appreciate how the main topic changes with time and have a glance of the
qualitative differences between the agendas. In particular, it can be seen some
differences between the Public Agendas that were not observed in figure 1, as
for instance, the persistence of main topics is longer in Twitter than in Google
Trends. This is more evident at the end of the analyzed period, where the topics
discussed in Google Trends show more response to change in Media Agenda than
in Twitter, maybe due to the existence of a different pattern of interaction in
the social network, to which a deeper analysis could be devoted in future works.
The linear correlations between the same topics of MA and PA were also
calculated. In all cases, we found that the correlations are positive and statis-
tically significantly, as it is shown in table 3. We interpret this as a validation
of the topics found in the corpus and the keywords that describe it. Even
though we are particularly interested in those periods where the Agendas dif-
fer, it is expected that the media and public interests should generally follow a
similar a pattern, mainly driven by external events A non positively (or a non
significantly) correlation may imply, besides the obvious conclusion of agendas
disengagement, that we can be eventually failing to properly detect the key-
words or features that describe a particular topic, and therefore the comparison
of the Google Trends or Twitter patterns with their counterpart in the Media
Agenda would be wrong.
Table 1: Queries used in Google Trends in order to build the Public Agenda.
Topic name Google Trends query
Elections elecciones + cambiemos + cristina kirchner +
massa + randazzo
Missing person santiago maldonado + juez otranto + patricia
bullrich + gendarmer´ıa + desaparici´on forzada
Former Planning minister de vido + desafuero + ministro de planificaci´on
+ minnicelli + baratta
Current President mauricio macri + cgt + reforma laboral + pe˜na
+ triaca
Social leader milagro sala + cidh + tupac amaru + pullen ller-
manos + morales
Prosecutor’s death nisman + amia + memor´andum con ir´an + timer-
man + juez bonadio
Former Vice-President amado boudou + ciccone + ariel lijo + vanden-
broele + n´nez carmona
Table 2: Queries used in Twitter: Due to different characteristics in the search
tool of Twitter, we adapted the queries employed in Google Trends, but pre-
serving, at least we can, the most important keywords.
Topic name Twitter query
Elections elecciones + cambiemos + kirchner + massa +
Missing person maldonado + otranto + gendarmer´ıa + de-
Former Planning minister vido + desafuero + minnicelli + baratta
Current President macri + cgt + laboral + triaca
Social leader sala + cidh + tupac + amaru + pullen + ller-
manos + morales
Prosecutor’s death nisman + amia + memorandum + timerman +
Former Vice-President boudou + ciccone + lijo + vandenbroele + car-
Table 3: Correlation between the topic temporal profiles of the Public Agenda
and their counterpart in Media Agenda. All correlation values are statistically
significant (p < 109), except (*) which is significant with p < 0.05.
Topic name Correlation MA and GT MA and Tw GT and Tw
Elections 0.81 0.59 0.75
Missing person 0.68 0.76 0.89
Former Planning minister 0.92 0.82 0.87
Current President 0.77 0.75 0.63
Social leader 0.49 0.25(*) 0.57
Prosecutor’s death 0.56 0.59 0.75
Former Vice-President 0.90 0.92 0.97
A quantitative description of the Agendas
Agenda diversity
How dominant is a main topic? Is the degree of dominance of a given topic in
the Media Agenda reflected in the Public Agenda? In order to answer such kind
of questions we quantify the diversity of the agendas through the normalized
Shannon entropy H, which was introduced in section Material and Methods.
In figure 3 we can see the value of Has a function of time for the three
agendas. It is important to pay attention to those periods of time when the
diversity is lower than usual. This effect is notoriously more pronounced in the
Public Agenda giving by GT, and in particular in four specific days when four
local minimums of the Shannon entropy can be detected. Three of them are
outliers as defined in section Material and Methods, two of them from GT
Elections Missing
(A) (B) (C)
(G) (I)
Aug 06 Aug 13 Aug 20 Aug 27 Sep 03 Sep 10 Sep 17 Sep 24 Oct 01 Oct 08 Oct 15 Oct 22 Oct 29Jul 31 Nov 5
Aug 06 Aug 13 Aug 20 Aug 27 Sep 03 Sep 10 Sep 17 Sep 24 Oct 01 Oct 08 Oct 15 Oct 22 Oct 29Jul 31 Nov 5
Aug 06 Aug 13 Aug 20 Aug 27 Sep 03 Sep 10 Sep 17 Sep 24 Oct 01 Oct 08 Oct 15 Oct 22 Oct 29Jul 31 Nov 5
Figure 2: Bump graph of the time-dependent Media (MA) and Pub-
lic Agendas extracted from Google Trends (GT) and Twitter (Tw).
Widths and rankings of the curves encode topic’s relative weight. Also, some
important events related to the topics are pointed out: A: First news about
Santiago Maldonado’s disappearance (Missing person); B: Primary elections;
C: March one month after Santiago Maldonado’s disappearance; D: March two
months after Santiago Maldonado’s disappearance; E: Appearance of Santiago
Maldonado’s body; F: General elections; G: Julio De Vido’s detention; H: De-
bates on labor reform; I: Amado Boudou’s detention (Former vice-president).
A more detailed explanation is given in section Supplementary Material.
and one from Tw. The other one has been not identified as an outlier but it is
a pronounced minimum and therefore a point of interest in our description.
A lower value in the agenda diversity is due to the fact that the most im-
portant topic attracts practically all the attention of the public and the media,
collapsing the agenda to one of the issues involved. In the radar plots included
in figure 3 we can see how two of these outliers (aand d) belong to the topic
Elections. They are related to the primary and general legislative elections that
took place in August 13th and October 22nd respectively. In all the agendas
these points were detected as outliers except point (d) in Twitter Agenda. Why
is that? The radar plot of the Twitter agenda for this day displays an asso-
ciation between the topic Elections and the Current President, decreasing the
importance of this topic. Discussions in Twitter about elections appear also in
point (c), when the other agendas seem to be more diverse. On the other hand,
and despite not being classified as outlier, we also focus on point (b) because
the Shannon Entropy in the Google Agenda displays a minimum (collapsing
agenda) which is not corresponded neither in the Media nor in the Twitter
Agendas. Crawling in the context, we see that it belongs to the topic Missing
person and this date corresponds to the rally that took place one month after
the disappearance of Santiago Maldonado (see Supplementary Material).
We would like to emphasize the discussion about this topic (Missing person)
because its dynamics show interesting features, as we will show below.
From the measure of Hwe have also observed that the median of the Public
Agenda diversity is statistical significant lower than the median of the Media
Agenda. Specifically HGT = 0.73 and HT w = 0.74 are statistically significantly
lower than HMA = 0.85 with p < 1018, while there is no significant difference
between the first two. However, from figure 3 we can see that GT shows more
abrupt dropouts in the diversity in response to specific events. From all this
analysis we can conclude that given a finite set of topics, the Public Agenda
is less diverse than the Media Agenda, because the public seems to focus
on the most important topics than the media can do, maybe due to editorial
Distance between Media and Public Agendas
Given our descriptions of the agendas as time-evolving distributions, we can
compare them by computing the Jensen-Shannon distance. In this context,
outliers in selected dates will correspond to divergences between the Media and
Public Agenda: Specific events when the public interests do not match with
media offer. In figure 4 we show the Jensen-Shannon distance between Media
and Public Agendas as a function of time. We focus on three points that seem to
be relevant enough. In all cases, the topic distributions at these days displayed
show that the increment in the distance between agendas is due to a greater
interest of public opinion in the Missing person topic.
Points (c) and (d) show that both the public and the media highlight this
topic, but the media do not disregard other topics, so the corresponding dis-
tance between them can be interpreted as lack of diversity in Public Agenda as
discussed in the last section.
On the other hand, points (a) (we take this point due to be a local maximum
despite not being an outlier) and (b) show a major interest of the public in the
topic Missing person which it is not reflected in the Media. In figure 2 we can
see that this topic becomes the most important in public interests (both in GT
Figure 3: Shannon entropy (H) as a measure of agenda diversity. The
Public Agenda shows a less diverse behavior than the Media Agenda as can be
seen in the top figure. The horizontal lines correspond to the lower inner fences
of each signal in order to identify outliers. The related radar plots show the
agenda at the selected days where the time series exhibit dropouts (points a-d),
indicating that the most important topic catches most of the public’s attention.
E: Elections; FPm: Former Planning minister; FVP: Former Vice-President;
Sl: Social leader; Pd: Prosecutor’s death; Mp: Missing person; CP: Current
and Tw) days before that it happens in the Media Agenda. This fact can be
associated with a social networks (like Facebook and Twitter) campaign in favor
of the appearance of Santiago Maldonado (“The missing person”) that took
place on August 26th. This campaign was massive and initially underestimated
by the main media outlets in Argentina (see Supplementary Material).
Finally, it is important to say that the Jensen-Shannon distance, together
with the measurement of agenda diversity given by the Shannon entropy, give
an insight of independent behavior, in certain particular dates, of the public and
the media. Its identification can be a starting point to study the media reaction
to a change in audience interests.
Agenda bias in different media outlets
In this section we leave aside the Public Agenda as an unified corpus and we
study the composition and evolution of the Media Agenda on each media out-
let. In figure 5 we show the bump charts corresponding to each of the analyzed
newspapers analogously to figure 2. The topics are the same which were intro-
duced in the word clouds of figure 1, but when computing the topic weights, the
articles are discriminated by newspaper. We also show the radar plots with the
average distribution, as made in figure 1.
In figure 5 we can qualitative look at the differences between the newspaper
agendas. For instance, we can see how the newspaper called agina 12 gave
more importance to the topics Missing person and Social leader, while it reduces
to minimum the coverage of the topic Former Planning minister as the others
In order to detect significant bias coverage of a given newspaper, we again
calculate the Jensen-Shannon distance, but between the individual newspapers
agenda and the Media Agenda. Note that this is the distance between the
distributions of figure 5 and the top panel of figure 2. In figure 6 we show
the Jensen-Shannon distance as a function of time. We detect three points as
outliers, although we finally disregarded point (b) due to a lack of informa-
tion of newspaper Infobae in that period. The other two points correspond
to differences between agina 12 and the other newspapers and correspond to
differences in the coverage of the topic Missing person.
Point (a) corresponds to the first news of the disappearance of Santiago
Maldonado, reported by agina 12 before the primary elections, and point (c)
corresponds to the two months rally after the disappearance (see Supplemen-
tary Material). Another singularity of point (c) corresponds to a greater
coverage of agina 12 in the topic Social leader while the other media outlets
seem to be more interested in the topic Former Vice-President.
The greater coverage in the topic Missing person by agina 12 is even more
clear if we inspect the temporal profile of the topic and compare the coverage
given by each newspaper. Differences in the coverage are due to coverage bias
[44]. In figure 7 we show the temporal profile of the topic Missing person (panel
(a)) and the topic Former Planning minister in panel (b), as an example where
the behavior is the opposite, as can be seen below.
From panel (a) of figure 7, we can see the larger coverage of agina 12 in
comparison to other newspapers at the beginning of the period. For example, we
Figure 4: Jensen-Shannon distance between the Media and Public
Agendas as a function of time (with upper inner fences pointed out). Larger
distances are due to a greater interest of the audience in the topic Missing per-
son which decreases the interest in other topics. On the other side, the Media
Agenda still keeps certain degree of diversity. E: Elections; FPm: Former Plan-
ning minister; FVP: Former Vice-President; Sl: Social leader; Pd: Prosecutor’s
death; Mp: Missing person; CP: Current President.
can quantify this difference calculating the median of the signals. If we focus on
the period between July 31st and August 27th, the median of the topic relative
weight in agina 12 agenda is roughly 0.14 and this is statistically significantly
larger (p < 107) than other medians, which are lower than 0.05. Analyzing the
same period, but in panel (b), we can show again that the median in agina 12
agenda, which is roughly 0.01, is lower than the others, which oscillate around
0.05 (p < 103). This quantification is proposed as a method of studying
coverage bias in the context of the methodology implemented in our work.
Finally, in figure 7 we also show the topic keywords word clouds, highlighting
the most frequently mentioned in each newspaper and filtering the common
words to all newspapers. Although most of the words are not relevant enough,
some of them are quite interesting, for instance the word represi´on (repression)
when agina 12 talks about the topic Missing Person and the word Cristina
(Fern´andez de Kirchner, former president) which is employed by all newspapers
except agina 12 when they talk about the topic Former Planning minister
(see Supplementary Material).
We think that a deeper study of the topic keywords could be a first ap-
proximation in the study of framing, which will constitute the core of futures
A brief discussion about agenda-setting
In a world where social media exists and the feedback between the media and
audience is common currency, nowadays the idea that the media sets the agenda
and the audience blindly follows it (as it’s seemed to be suggested in the original
work of McCombs) is too naive. Based on the data analyzed above, the behavior
of the Media and Public Agendas, either by looking at Google Trends or Twitter,
shows periods of strong similarity, for instance in the presence of an unexpected
event, and periods of disengagement. Therefore, it is not trivial to establish a
causal relationship between the agendas, specially when they are represented as
time.evolving topic distributions as we did in this work. However, it is possible
to discuss agenda-setting if we focus on a single topic. We think that the Missing
person topic is the most adequate topic to be discussed because:
It caused a great impact in both the media and the audience;
its coverage fully deploys along the time lapse analyzed in this manuscript
(see Supplementary Material).
In figure 8 panel (a), we show the Missing Person relative weight from both
the Media and Public Agendas. After the initial coverage, the agendas seem
to differentiate around August 15th, when the topic starts to become more
important in the Public Agendas than in the Media one. Around August 24th,
the topic abruptly increases in the audience interests while the reaction in the
media is slower, showing a significant peak in the plot of the discrete difference
of the temporal profiles (panel (b)). This date is very close to August 26th, when
a campaign in social media took place. After that event, the media increases
its coverage about the topic.
In order to understand this behaviour we calculate the cumulative coverage
of the Missing Person topic, which is displayed in figure 8 panel (c). The
cumulative coverage of the Missing Person topic is defined to be the numerical
Elections Missing
Former Vice-
Aug 06 Aug 13 Aug 20 Aug 27 Sep 03 Sep 10 Sep 17 Sep 24 Oct 01 Oct 08 Oct 15 Oct 22 Oct 29
Jul 31 Nov 5
Aug 06 Aug 13 Aug 20 Aug 27 Sep 03 Sep 10 Sep 17 Sep 24 Oct 01 Oct 08 Oct 15 Oct 22 Oct 29Jul 31 Nov 5
Aug 06 Aug 13 Aug 20 Aug 27 Sep 03 Sep 10 Sep 17 Sep 24 Oct 01 Oct 08 Oct 15 Oct 22 Oct 29Jul 31 Nov 5
Aug 06 Aug 13 Aug 20 Aug 27 Sep 03 Sep 10 Sep 17 Sep 24 Oct 01 Oct 08 Oct 15 Oct 22 Oct 29Jul 31 Nov 5
Figure 5: Bump charts of newspaper agendas and radar plot of the
average distributions. The figure shows, in a qualitative way, the bias in the
different newspaper agendas. For instance, the greater interest of agina 12
(P12) in the Missing person topic and its slightly lower coverage in the Former
Planning minister respect to the other newspapers.
(a) (c)
2017-08-12 2017-10-02
Figure 6: Jensen-Shannon distance between the newspaper agendas
and the Media Agenda as a function of time.agina 12 shows the most
different behavior, motivated again by its interest in the Missing person and
Social leader topics, as can be seen in the radar plots which belong to points
(a) and (c). The anomalous behavior of Infobae at point (b) is due to few
articles around that date in our database, therefore we ignore its radar plot.
E: Elections; FPm: Former Planning minister; FVP: Former Vice-President;
Sl: Social leader; Pd: Prosecutor’s death; Mp: Missing person; CP: Current
integration between the initial date and the current date, of the topic temporal
profile in each media outlets (figure 7) and in the Google and Twitter Agendas
(which can be seen in figure 8 panel (a)). This quantity shows us how the media
and public attention have been accumulated since the first events.
Figure 7: Relative weight of the topics (a) Missing person, and (b)
Former Planning Minister, and their corresponding word clouds of
frequent newspaper keywords. We interpreted the differences shown in
given periods as an indicator of coverage bias. For instance, in figure (a) agina
12 pays a greater attention in the first days. In the word-clouds, we show
which of the defining words are more frequently used by the corresponding
newspaper. Most of them are less informative, but others seem to represent a
first approximation in the study of framing.
The figure 8 panel (c) shows a complex agenda-setting dynamics within
the Missing Person topic: The least sold newspaper (agina 12 ) triggered
a public debate via a positive feedback mechanism reinforced by reiterative
Google searches and discussions in social networks. Due to this increasing public
interest, the rest of the media were forced to pay attention to this subject and
finally, the topic becomes also prominent to the Media Agenda.
Beside the analysis performed above, there are two important facts that
must be mentioned about the Missing Person topic: First, the disappearance of
a person is a very sensitive issue in the Argentinian society, which can explain
why this particular topic triggered the audience interest; and second, there
were political reasons why agina 12 was particularly interested in covering
this topic since the beginning, while the rest of the media did not until the
topic was prominent to the Public Agenda (see Supplementary Material).
The analysis of the cumulative coverage allows us to face the question about
causality at least in a qualitative way. However, it was possible to highlight the
complex feedback dynamics that take place between public and media agendas.
Aug 06 Aug 13 Aug 20 Aug 27 Sep 03 S ep 10Jul 31
P12 MA
Cummulative coverage
Figure 8: Non-trivial agenda interplay in the Missing person topic. The
temporal profiles of figure (a) show that the Public and Media agenda seem to
differentiate around August 15th (vertical grey line (1)) and the Public increases
abruptly its interest in the topic around August 24th (grey line (2)). It can be
seen also in figure (b), where the discrete differences were computed. With the
computing of the cumulative coverage of figure 7 and figure (a), represented as
a bump chart in figure (c), we suggest that the topic was first set by agina 12
and then the audience interest causes the coverage of the rest of the media.
The mass media plays a fundamental role in opinion formation and therefore,
it is of vital importance to have an accurate quantitative description of the
Media and Public Agenda and their relationship in the framework of agenda-
setting theory. In this work, through the implementation of a topic detection
algorithm we describe the Media Agenda as a distribution which evolves with
time and which is defined in a topic space which emerges intrinsically from the
corpus. This gave us an insight of how we can construct and follow the audience
interests, i.e the Public Agenda, in order to compare with the media interests.
This approach with newspaper, Twitter and Google trends data, let us un-
derstand that the Public Agenda is usually less diverse than the Media Agenda.
Specifically, it is shown that when there is a very attractive topic, the audience
naturally focuses only on this one. On the other hand, the media keeps a certain
degree of diversity and a wider range of topics.
Interestingly, we show how the measurement of distances between agendas
can be employed to rapidly detect those periods when the public may have an
independent behaviour respect to the media and also when it does not. The
“Missing person” topic analysis, presented in figure 8, shows concrete evidence
of a complex dynamics agenda-setting case. Therefore, this work depicts a very
general methodology which can be used in order to understand who sets the
agenda: If it is the media or the society. The generality of our method allows
to easily face up the agenda-setting issue in other datasets.
Moreover, the methodology implemented here also allowed us to detect cov-
erage bias in newspapers and gave us a first approximation in the theory of
framing. The implementation of the word clouds and topic detection algo-
rithms, which were described in this work, allow us to clearly observe the huge
differences between any pair of newspapers. In particular, it is shown in fig-
ures 5 and 7 how Clar´ın,La Naci´on and Infobae, that share a similar political
view with the current government, focused on the former president Cristina
Fern´andez de Kirchner, whereas agina 12, the newspaper that criticizes and
condemns the government, focused on the denounced repression that Santiago
Maldonado suffered from the police.
We hope that some of the elements studied here will give us insights at
the time of proposing a mathematical model about the interaction between
mass media and audience. Future works may include a more systematic study
and its extension to international media, a deeper study of framing through
topic detection and sentiment analysis, and a more quantitative analysis about
Supplementary Material
Here we provide the context of the the discussed topics within this work. The
news belong to the period between July 31th and November 5th, 2017. The bias
of the different media outlets reflect the highly polarized political climate ob-
served in Argentinian society. During the administration of Cristina Fern´andez
de Kirchner (2007-2015), the government confront with several news organiza-
tions. It led to media outlets such as Clar´ın,La Naci´on and the news portal
Infobae to be very critical of the Fern´andez ’s administration, emphasizing the
allegations of corruption related to it, as can be seen in the importance given
to the topics Former Planning minister and Former Vice-President. On the
other hand, agina 12 has an opposite ideological bias, supporting the former
administration of Cristina Kirchner and therefore being very critical with the
current Mauricio Macri’s administration, doing special emphasis on issues re-
lated to human rights, as can be again observed in the coverage given to the
topics Social leader and Missing person.
Two legislative elections were celebrated during the period in great part of
Argentina: Primary elections on August 13th and the general elections on Oc-
tober 22nd, 2017. A special focus was put on the elections in the Buenos Aires
province, where the former President Cristina Fern´andez de Kirchner partici-
pated as a senator candidate representing the alliance Unidad Ciudadana, con-
fronting Cambiemos, which is the alliance of the current President Mauricio
Macri and the current governor of Buenos Aires province Maria Eugenia Vidal.
On the other hand, two other candidates that also participated in the election
were Sergio Massa and Florencio Randazzo.
Current President
Mauricio Macri is the current Argentinian President since December 2015 and
this topic is mainly composed of articles related to his administration, specially
after the general elections of October 22nd, 2017, when a labour reform pro-
moted by the government was being discussed.
Missing Person
Santiago Maldonado dissapear on August 1st, 2017 after a minor clash between
the Gendarmerie (Border Guards) and a group of Mapuches (Patagonian native
population), which recognize themselves as the original population of an area
in the Patagonia. Since that event, the Mauricio Macri’s administration was
accused by several people as the responsible for a forced disappearance.
A very massive campaign in social media took place on August 26th, 2017
under the motto “Where is Santiago Maldonado?”, followed by two massive
protest marches to the Plaza de Mayo that took place on September 1st and
October 1st, of which the first one had a great repercussion due to several
incidents that took place during the march.
The body of Santiago Maldonado was found dead on October 17th, 2017 in
the Chubut river, near the place where he was seen for the last time, and the
autopsy report told that Santiago Maldonado had died from “asphyxia after
being submerged”, with no injuries on his body. However, due to the fact that
this topic is a very sensitive one for the Argentinian society, the causes of the
Maldonado’s death are still being investigated.
Former Planning minister and Former Vice-President (Corruption
of former administration)
Julio de Vido was the Planning minister during the administration of estor
Kirchner and Cristina Fern´andez de Kirchner (2003-2015). In 2015, he was
elected to integrate the Chamber of Deputies, which finally voted to strip De
Vido of his congressional immunity over corruption allegations and was imme-
diatly jailed on October 27th, 2017.
Amado Boudou was the Vice-President of the Cristina Kirchner’s admin-
istration. Boudou was arrested on November 3rd, 2017 on charges including
money-laundering and hiding undeclared assets.
Social leader
Milagro Sala is an indigenous leader. She has been incarcerated under pre-trial
detention ever since she was first detained in January 2016. She faces allegations
of embezzlement related to government funding for housing projects managed
by T´upac Amaru, her social organization. Sala accused the government of
“violating her human rights”, and several people think that she is a political
Prosecutor’s death
Alberto Nisman was a special prosecutor who were investigating the 1994 terror
attack on the Argentine Israeli Mutual Association (AMIA), until his suspicious
death in January 2015. During the period analyzed in this work, a team of
experts led by the Gendarmerie (Border Guard) concluded that late prosecutor’s
death may have been a case of murder, not suicide.
Comparison between NMF and LDA
In this section we compare the results of applying a different topic model to our
corpus. The reference is Latent Dirichlet Allocation [45] (LDA) which is one of
the more used topic models in last years.
Even though the topics found with LDA may not coincide with the NMF
ones, it is expected that the corpus under study displays some degree of ro-
bustness when considering different topic models. On the other hand, as was
discussed in [46], NMF can be a more suitable topic modeling method in certain
domains, in the way that it produces more coherent topics, while LDA tends to
return higher levels of generality and redundancy. Topic coherence is defined as
the semantic interpretability of the terms used to describe a particular topic,
although the coherence of a topic may depend on the user’s expectations.
We define a simple coherence measure defined in eq. (8), where dij is the
number of documents where the term iand term jappear simultaneously, and
dxis the number of documents where appears the term x. The summation is
over the Ntop terms of the topic. It’s important to note that if two terms have
no co-occurrences, the contribution to the summation is zero, and if these ones
appear only together the contribution is one. A topic with higher coherence is
a topic where the terms that define it co-occur frequently.
T C =
We perform a decomposition into 10 topics using LDA with the python mod-
ule gensim [47], which allows us to modify the number of times the corpus is
read, improving the coherence of the topics. Unlike to what we see with NMF,
the LDA’s performance depends strongly on the initial condition of the algo-
rithm. After 10 iterations, we chose the one with highest mean topic coherence,
and compared this with the NMF results.
In figure 9 we show the temporal profiles of topics Elections and Missing
Person for both NMF and LDA. The association between topic models was
simply made by looking at the topics which share common keywords. As can be
seen from figure 9 and table 4, those LDA topics which can be linked to NMF
ones or to a combination of these, show a temporal profile highly correlated.
The seven main topics obtained with LDA are practically the same as those
found with NMF. Only minor differences, related to other topics composed of
very general words were found. This comparison shows us that the Agendas
obtained following both methods are similar.
Table 4: Correlation between the temporal profiles of the topics found in NMF
and associated topics in LDA.
Topic name Correlation between NMF and LDA
Elections 0.98
Missing person 0.99
Former Planning minister + Former Vice-President 0.89
Current President 0.94
Social leader 0.94
Prosecutor’s death 0.83
Topic relative weight
Topic relative weight
Figure 9: Temporal profiles of topics Elections (left) and Missing Person (right)
for both LDA and NMF. All the topics found by applying NMF have a highly
correlated counterpart in LDA.
Media Agenda (MA). Public Agenda (PA). Google Trends (GT). Twitter (Tw).
Clar´ın (Cl). La Naci´on (LN). P´agina 12 (P12). Infobae (Inf). Non-negative
matrix factorization (NMF). Latent Dirichlet Allocation (LDA).
Availability of data and material
The data that support the findings of this study are available from Clar´ın at, La Naci´on at, P´agina
12 at, and Infobae at,
but restrictions apply to the availability of these data and so are not publicly
available. Google Trends and Twitter data are respectively openly available at and
Competing interests
The authors declare that they have no competing interests.
P. Balenzuela was supported by grants PICT 201-0215 from Agencia Nacional
de Promoci´on Cient´ıfica y Tecnol´ogica and UBACyT 20020170100356BA from
University of Buenos Aires. F. Albanese and S. Pinto were supported by Conicet
Author’s contributions
PB conceived the study. SP collected and analyzed most of the data. FA
collected Twitter data. FA, PB, and SP interpreted the data and prepared the
manuscript. PB and COD give the final approval of the version to be published.
We thank Dr. A. Chernomoretz, Dr. M. Otero, Dra. V. Semeshenko, and Dr.
M. Trevis´an for bringing us a critical revision of the article.
1Even though there are other techniques in topic detection, as for instance LDA (Latent
Dirichlet Allocation), the NMF decomposition suites perfect for the kind of corpus we have
analyzed in this work (A detailed comparison of both, NMF and LDA methods could be found
in Supplementary Material).
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