Associative Framing
A unified method for measuring media frames and the media agenda∗
Wouter van Atteveldt†, Nel Ruigrok‡, Jan Kleinnijenhuis†
Submitted to ICA2006 November 1, 2005
Abstract
The study of framing is made difficult by the ongoing discussion on
definition, theoretical grounding, and measurement methodology. This
paper proposes a method to automatically measure and visualize associative media frames.
Associative frames are concept networks or maps and can be situated between the object attributes of second level agenda setting and
the complex frames often proposed in the literature. These networks
can be interpreted as the media equivalent of the association networks
found in schemata theory, allowing firm psychological grounding for a
theory of associative frame setting.
As a case study, the measurement method is applied to the Dutch
news coverage on Immigration, Islam, and Terror in the past five years.
The 9/11 attacks caused a structural increase in media attention to
these issues, and set the associative frames for the rest of the studied
period. The murder on Theo van Gogh by a fundamentalist muslim
also caused an attention peak and strong associations of the other
issues with the Islam, but this was followed by a return to their post9/11 levels.
∗
The authors would like to thank Dirk Oegema for allowing us to use his data and
Rens Vliegenthart for his useful comments
†
Free University Amsterdam ([email protected]; [email protected])
‡
University of Amsterdam ([email protected])
1
Introduction
Framing and agenda setting are two influential theories dealing with media
effects on the audience. Framing, however, is plagued by fierce disagreements concerning its definition, theoretical grounding, and measurement
methodology.
In this paper we will discuss a type of frames called associative frames.
These frames consist of relations between concepts and together form an associative network of the concepts reported in the media. They are broader
than the object attributes of second level agenda setting but narrower than
many frames that are proposed in the literature. Associative frames can
be interpreted as the media equivalent of association networks such as in
schemata theory, giving associative frame setting firm grounding in psychological theory.
Subsequently, this paper presents a way to automatically measure and visualize the network formed by these associative frames. We operationalise
associative frames as co-occurrence patterns. Since visibility is generally
operationalised as term occurrence, this provides a uniform way to measure
media agenda and associative frames.
As a case study, this method is applied to the Dutch news coverage of the
Immigration, Islam, and Terror issues in the past five years, providing face
validity for the method.
Theoretical Framework
Agenda Setting as transfer of Salience
Agenda Setting was a first strong challenge to the ‘limited effects’ claim.
The core of this research was stated years earlier already by Bernard Cohen
stating that the mass media ‘may not be successful much of the time in
telling people what to think, but it is stunningly successful in telling its
readers what to think about’ (Cohen, 1963, p.13). In their seminal Chapel
Hill study McCombs and Shaw (1972) introduced the term Agenda Setting
to describe this influence after finding a near perfect correlation between the
public agenda and issue visibility in the media. For Agenda Setting to occur,
people must come to believe the issue is more important after exposure to
mass media than before.
2
In the years since this first study, Agenda Setting has turned out a robust
and conceptually clear theory, with numerous studies reproducing these effects and elaborating on the theory (see for example Dearing and Rogers,
1996; McCombs and Bell, 1996; Rogers et al., 1993). Dearing and Rogers
(1996, p.22) formulated a three-component model of the agenda setting process, consisting of (a) the media agenda, which influences (b) the public
agenda, which in turn may influence (c) the policy agenda. Expanding the
original model with influences on the media agenda, researchers divided the
theory into Agenda Building and Agenda Setting processes, with the media
agenda being the dependent variable in the building phase and the audience
as the dependent variable in the setting phase (see Scheufele, 1999). However, the transfer of salience is the common theoretical base underlying the
large variety of Agenda building and Agenda Settings studies. Interpreting
salience as the “degree to which an issue on the agenda is perceived as relatively important” (Dearing and Rogers, 1996, p.22), agenda setting can be
seen as transfer of salience from the media agenda to the public agenda.
In sum, we can state that Agenda Setting is conceptually clear and based on
strong empirical evidence. However, the ability to influence how or what to
think is beyond the scope of the initial Agenda Setting theory. Rather than
a ‘limited effects’ claim, Agenda Setting in its original form can be called a
limited claim of effects.
Framing
The idea of framing in a message first appeared in Goffman (1974) research
in sociology. He argues that the organization of a message influences the
audience in their thoughts and actions ‘we actively classify and organize our
life experiences to make sense of them.’ These ‘schemata of interpretation’
are labelled frames; they enable individuals to ”locate, perceive, identify,
and label.”
During the last decades, the study framing gained an important place in
the field of communication research. In one of the key-studies on this topic,
Entman (1993) defined framing as selecting “some aspects of a perceived
reality and make them more salient in a communicating text, in such a way
as to promote a particular problem definition, causal interpretation, moral
evaluation, and/or treatment recommendation for the item described.”
The definition shows already the multi-facet aspect of framing research.
It is about selection, salience, and recommendation, including not only the
3
communicator but also the audience. As Entman (1993) points out there are
at least four locations of framing that can be studied: the communicator,
the text, the receiver and the culture. Moreover, framing research over
years expanded by not only concentrating on the frames as found in the
different locations, but also focussing on the process of transfer of frames
from one location to another. In the same line as agenda building and agenda
setting, researchers distinguish a frame building process with the media as
the dependent variable and a frame setting process where the audience is
the dependent variable (Scheufele, 1999; De Vreese, 2002).
Aggrevating this, there are many different definitions of what constitutes a
frame. We have already seen Entman’s definition; Gitlin (1980, p.7), in his
study into the relationships between news media and the Student New Left
movement, defines frames as ‘persistent patterns of cognition, interpretation,
and presentation, of selection, emphasis, and exclusion, by which symbolhandlers routinely organize discourse’. Gamson and Modigliani (1987) define
a frame as a ‘central organizing idea or story line that provides meaning’
and are signified by ‘symbolic devices’ such as metaphors, exemplars, catchphrases, depictions, and visual images. De Vreese (2005, p. 53) offers a
broad definition of a frame as ‘an emphasis in salience of different aspects
of a topic’. Many researchers define a frame as a central organizing theme,
idea, or topic (Tankard et al., 1991; Pan and Kosicki, 1993). Other definitions differentiate between physical framing (such as position and frequency;
McCombs and Mauro, 1977; Ghanem, 1996) or symbolic framing (using pictures, catchphrases and quotes; Ghanem, 1996; Gitlin, 1980) and content
frames, such as the affective and cognitive frames of Ghanem (1996) or the
reasoning devices of Gamson and Modigliani (1989).
In sum, framing goes beyond the limited claim of agenda setting by also
claiming an effect on the way or context within which the public thinks
about something. However, the many theoretical complexities attached to
framing cause Entman (1993) to complain about a lack of structure and
paradigmatic unity. Starting from Rosengren (1993) and Beniger (1993)
arguments stating that three paradigms infuse the communication research
(constructionist, critical and cognitive approach), D’Angelo (2002) takes
a more optimistic view and sees framing as a multiparadigmatic research
program. Due to the inherent link between salience and cognition, we focus
here on the cognitive perspective, focussing on the earlier described framing
process.
4
The cognitive paradigm
The focus on the receiver in the communication chain is central within
the cognitive approach to framing. This approach presumes that news can
‘encourage particular trains of thoughts about political phenomena’ (Price
et al., 1997, p.483). Grounded in cognitive psychology, the approach uses the
associative network model of human memory (Collins and Quillian, 1969),
proposing that the concepts in semantic memory are represented as nodes in
a complex hierarchical network. Each concept in the network is directly related to other related concept. Collins and Loftus (1975) refined this model
in introducing important changes regarding the processing of information in
the network. They talk about the automatic spreading of activation. According to them a processing of a concept is manifested in the network as the
activation of the appropriate node that represents it. When the proper concept node is activated, activation continues automatically to all connected
nodes.
Minsky (1975) connected this view to framing when he defined a frame as
a structure containing various pieces of information. These discursive or
mental structures are closely related to the description of schema’s which is
‘a cognitive structure that represents knowledge about a concept or type of
stimulus, including its attributes and the relation among those attributes’
(Fiske and Taylor, 1991, p.98). Benford and Snow (2000) distinguish three
core elements in social movement framing: ‘diagnostic framing’ (problem
identification and attributions), ‘prognostic framing’ (articulation of the
proposed solution) and ‘motivational framing’ (directed to reach collective
action). These cognitive structures are based on prior knowledge (Fiske and
Linville, 1980). According to Graber (1988), people use schematic thinking
to handle information. They extract only those limited amounts of information from news stories that they consider important for incorporation into
their schemata. She added that the media make major contributions to this
schema formation.
Combining Agenda Setting and Framing
With research within both the Agenda Setting and framing tradition progressing, a new debate commenced, arguing for a convergence of the two
concepts. However, opinions in this respect differ and no common accepted
model emerged so far. Scheufele (2000, p.309) for example suggests a distinction between Agenda Setting and framing since ‘salience’ forms the theoret5
ical premises of agenda setting while attribution is the theoretical premises
of framing: “[Agenda setting] increase the salience of issues or the ease with
which these considerations can be retrieved from memory if individuals have
to make political judgments about political actors [...] framing influences
how audiences think about issues, not by making aspects of the issue more
salient, but by invoking interpretive schemas that influence the interpretation of incoming information.”
Other researchers, however, suggest that not only are agenda setting and
framing effects related, framing is, in fact, an extension of agenda setting.
As McCombs and Estrada (1997, p.240) explain: “How news frames affect
public opinion is the emerging second-level of agenda setting. The first level
is the transmission of object salience. The second level is the transmission
of attribute salience.” Attribute salience refers to the ways in which any
object can be described or characterized. McCombs and Ghanem (2001,
p.68) state in this respect “Beyond the agenda of objects there is another
aspect to consider. Each of these objects has numerous attributes, those
characteristics and properties that fill out the picture of each object”. The
attributes connected to the objects form the central part of this second-level
Agenda Setting, or attribute agenda setting.
Measuring Media Frames and the Media Agenda
In this paper, we will study what could be called ‘associative frames’. These
frames, which overlap with the object-attributes in second level agenda setting, consist of associations between objects and other objects. We believe
that this is consistent with the definitions of Entman (1993) and De Vreese
(2005). Taking the cognitive perspective as described above, these frames
refer to the earlier described schemata’s of interpretation of Goffman (1974).
The corresponding audience frames are seen as associative networks as described by Collins and Quillian (1969).
Analogous to agenda setting as a transfer of salience of concepts, this allows
us to see associative frame setting as a transfer of salience of the links between concepts. Where these concepts are the attributes of other concepts,
this is identical to second level agenda setting, but we believe that it is fruitful to look at the associative network as one large interconnected network
rather than as the attributes of individual concepts.
Thus, this model is related to the audience frames or schemata in which individuals form strong associations between different mental concepts. The
6
relationships between these different concepts can be triggered through outside cues, such as news messages, consistent with second level of Agenda
Setting of McCombs and Estrada (1997) and the strong media effect postulated by Graber (1988).
It is our hypothesis that a strong textual association between issues, as found
in news messages, will lead to a strong mental association of these issues in
the audience’s mind. However, in this paper we will only take the first
step towards testing this hypothesis by presenting a method to measure the
media frames based on cooccurrence. Since the media agenda is generally
equated with the occurrence of issues or actors, this proposal provides a
uniform method to measure media agenda and associative frames.
Association as Co-occurrence:
An information theoretic interpretation
Taking an information theoretic point of view, we state that the media send
messages, some of which are received by the consumer. In our model, we
assume that the media exposure can be divided into well defined parts, which
we shall call documents. For example, a document could be a topic in the
evening news, a day of newspaper coverage, a single newspaper article, or
even a paragraph or sentence within such an article.
In what could be called a ‘bag of messages’ approach, we further assume that
each message only contains one atomic concept, and moreover, that there
is no relation between the the messages except for the document (or bag)
in which they occur. This can be interpreted as stating that a document is
about a certain concept1 according to a consumer if that consumer receives
the message corresponding to that concept.
Within this framework, we operationalise textual association between two
concepts as the co-occurrence of the messages corresponding to these concepts within one document. Thus, a media consumer receiving two messages
within the same document will cause him or her to perceive the relayed concepts as being associated in that document.
1
In this paper, we use the word ‘concept’ to refer to entities that are the subject or
objects of associtations. Concepts thus can be actors, issues, and also characteristics of
actors or issues
7
Fictive Example
Let us consider the small fictive example consisting of three articles about
the hurricane Katrina, a factual report on the front page; a description
of the situation in the emergency shelters on page 4 and an analysis on
page 5 on the performance of the Bush administration. In this example, we
are interested in three concepts, Katrina, the Bush administration, and the
hurricane Victims, and we have a list of keywords indicative of both concets.
Table 1 summarizes this example and lists the (fictive) keyword counts of
these concepts in the articles.
Table 1: Fictive example
art1 art2
Page number
1
4
Word count: Katrina
Word count: Bush adm.
Word count: Victims
5
0
1
2
2
5
art3
5
5
8
1
Counts to probabilities
In the bag-of-messages approach, we assume that each concept-message is
sent at most once in each article, and is sent with a certain probability.
Thus, we need a function to determine the probability of a message based
on the keyword count corresponding to the concept.
Similar to the motivating problem behind logistic regression, we need to
transform a count, which has range [0, ∞i, to a probability, with range [0, 1].
p
is used, but this transforms
In logistic regression, the log-odds ratio log 1−p
h−∞, ∞i to the desired range. We could use the function without the logarithm to overcome this, but this lacks a straightforward interpretation.
Instead, we take a more axiomatic approach. Following the intuitions behind the ‘local weighting’ function in Deerwester et al. (1990), we want our
transformation function f (·) to follow the following constraints: (i) it must
be monotonically increasing [f (c1 ) > f (c2 ) ⇔ c1 > c2 ]; (ii) it must be sublinear [f 0 (c1 ) < f 0 (c2 ) ⇔ c1 > c2 ]; (iii) it must be zero if and only if the
count is zero [f (c) = 0 ⇔ c = 0]; (iv) it must go to one as the count goes to
lim
infinity [ c→∞
f (c) = 1]; and (v) we would like the the probability of [oi or
8
oj ] to be equal to the probability of an ok with the count of the other two
combined [f (ci ) + (1 − f (ci )) · f (cj ) = f (ci + cj )].
A set of functions satisfying these constraints is the following:
µ
¶
1 count(o,a)
p(o|a) = 1 − 1 −
b
(1)
What remains is the choice of b, where 1b is the probability of an article
being about an object if it is mentioned once. Again, ideally this would be
modelled empirically, but a commonsense definition based on the average
length of articles could also be used, reflecting the intuition that one mention
in ten words should count heavier than one in a hundred words. If the length
of documents is variable, for example when full newspaper articles are used,
it might be desirable to use the density rather than the count.
To return to our example, we can now calculate the probabilities based on
the word counts. Taking b = 4, we arrive at the probabilities in table 2
Table 2: Fictive example
art1 art2 art3
Page number
1
4
5
p(Katrina)
p(Bush)
p(Victims)
0.76
0.00
0.25
0.44
0.44
0.76
0.76
0.90
0.25
Phyisical framing, Visibility and reading chance
As discussed above, an important frame that lies outside the scope of associative framing is the physical framing of actors or issues by the location in
the paper, appearance of photograps, and by the location of the mention in
the article. The information theoretic framework defined above, however,
also gives us a natural way to interpret this physical framing: the importance of a document determines the relative reading chance of that document
[p(art)], and the prominence of an actor or issue in the document determines
the chance of receiving the message corresponding to that meaning object
given that that document is actually read [p(o|art)].
This also leads to a straightforward definition of visibility: the visibility of
a concept o is the average chance over all documents of reading about that
9
concept, weighted by the chance of reading that documens. In other words,
the visibility is the chance of reading about that object if one is to read only
one article in the given period.
V is(o) = p(o) =
X
p(arti )p(o|arti )
(2)
i
Again returning to the example above, we can now calculate the relative
visibility of our three meaning objects. First, we need to compute the reading chance of each article. Let us assume that front page articles have twice
as much chance as normal articles to be read, in other words p(art1 ) = 0.5
and p(art2 ) = p(art3 ) = 0.25. Then, we arrive at the following relative visibilities: Katrina 0.68, Bush 0.33, and Victims 0.38. Katrina received about
twice as much media attention as the other two meaning objects, mainly
due to the prominent front page article.
From occurrence to co-occurrence
In its most general form, the step from occurrence to co-occurrence means
transforming a term-document matrix2 into a term-term matrix, where the
cell for each term is a measure of the association between these terms based
on the two rows in the term document matrix containing the occurrence
scores for each document.
This seems fairly straightforward, but there are an infinite number of possible functions doing this and the choice of function will have a strong effect on
the outcome of the analysis. As Goodman and Kruskal convincingly argue
in their series of landmark papers on the subject (bundled in Goodman and
Kruskal, 1979), the choice of association measure is highly important and
should be based on ‘operational meaning within the context of the particular
problem’ rather than on ‘tradition and convention’. Taking their advice to
heart, we shall review a number of such measures that are used in similar
situations, and finally propose our own measure based on the interpretation
of (co-)occurrence in a information theoretic/probabilistic framework.
2
A term-document matrix is essentially the same as a standard SPSS case-variable
datasheet.
10
Linguistic collocation patterns: χ2 tests
In linguistics, a collocation is defined as a pair (or more) of words frequently occurring together and generally having a non-compositional semantics. These pairs can be automatically extracted from large corpora by
looking at words that occur next to each other significantly more often than
chance would predict. This condition, necessary to avoid biasing frequent
words, is operationalised using χ2 statistics.
For our problem, however, this is not satisfactory. First, from theory we see
no reason to exclude chance co-occurrence from triggering mental association. If all the news in a period is about two events, it seems likely that
people will start associating these events. Moreover, since we will generally
use a corpus of text selected on the basis of certain keywords, we have no
good way of establishing the absolute a priori probabilities.
Finally, the χ2 measure is symmetric. We assume that, if all news about
A is also about B but not the other way around, A will be more strongly
associated with B than the other way around. This is intuitively plausible (at
the time of writing, Afghanistan is strongly associated with natural disaster;
but other disasters such as Katrina cause the opposite association to be
weaker) and consistent with Collins and Loftus (1975, p.408) assymetric
definition of the mental association between concepts.
Information Extraction: cosine measure
In information extraction, the association between two terms or documents
is generally computed by using the cosine angle between the vectors (the
occurrence rows) in n-dimensional space; this is also the measure used in
much research based on latent semantics (Deerwester et al., 1990; Bestgen,
2002). This measure has the advantage of being easily computable and not
biasing in favour of similar pure frequencies, as Euclidian distance does.
However, it is still a symmetric measure, as argued against above. A related
problem is that the cosine is normalised by vector length, in our case the term
frequencies. This makes it very difficult for any concept to be associated with
a concept with high overall frequency, which is theoretically dubious.
Correlation
Pearson correlation between to vectors is actually similar to the cosine measure, since the variance (the normalisation factor in correlation) equals the
11
vector length if the vectors are mean centered. Thus, the objections against
cosine as listed above also hold for correlation, and moreover there seems no
good reason to center the occurrence data around the mean.
Information Value
Interpreting the two rows of occurrences as the probabilities of two possibly
related messages arriving in a set of trials, Theil (1972, pp 110–132) define
the concept of expected mutual information. This concept, which is used in
the uncertainty coefficient in SAS (SAS, 1999), is the amount of information
about B that we expect to gain by knowing whether A occurred. Thus, if
we are certain that B will occur after having seen A, this information gain
will be maximal (and the uncertainty coefficient will be 1).
This is a very elegant measure with a natural interpretation in an information theoretic context, and it is also asymmetric in the way argued for above.
However, it has one strong disadvantage: it does not distinguish between
confidently expecting A after seeing B and being certain A will not occur
after seeing B. It only measures the fact that our uncertainty is reduced, not
in which way this reduction is.
Conditional Probability as Association Measure
Within the framework defined above, we shall define the association between
A and B as the proportion of articles that are about A that are also about
B, or in other words: P (B|A), the chance of receiving an A-message if one
receives a B-message in any given document.
Applying bayes’ rule and iterating over all the articles, we can calculate this
as:
p(A, B)
=
ass(A → B) = p(B|A) =
p(A)
P
art p(art) · p(A, B|art)
P
art p(art) · p(A|art)
Since we assume messages to be independent of each other wihtin a document (I(A; B|Art)), and using the definition of visibility from equation
eq:vis, this equals:
P
p(art) · p(A|art) · p(B|art)
ass(A → B) = art
(3)
V is(A)
12
This measure is a combination of the measures discussed above. Like the
correlation and cosine measures, it is basically the inner product of the
document vectors. However, the normalisation is only based on the total
frequency of one of the two concepts, creating the desired asymmetry. The
theoretical foundations are identical to the ones behind the information value
or uncertainty coefficient, only the resulting measure is based on actual a
posteriori probability rather than the (undirected) uncertainty reduction.
In the fictive example, the pair of associations between the Bush administration with the hurricane in these articles is:
Ass(B → K) = P (K|B) =
0.5·0.76·0+0.25·0.44·0.44+0.25·0.76·0.9
0.33
Ass(K → B) = P (K|B) =
.22
= 0.32
.68
=
.22
= 0.66
.33
Thus, Bush is more strongly associated with the hurricane that the other
way around, which is in agreement with the intuition formulated above.
Deriving association networks from text
Now, we have all the building blocks needed to automatically derive association networks from a collection of text. First, we need to determine the
document size or unit of measurement. Then, we need to determine the
(relative) reading chance of each document, presumably based on physical
characteristics of the article. Then, for each document and concept, we
need to determine the probability that that document ‘about’ that conecept, which can be based on the frequency of the mention of keywords for
that meaning concept and possibly the location of their mention, and using
equation (1) to transform this into probabilities. Finally, we can compute
the visibility of each concept using equation (2), and the association between
each pair of concepts using the formula in equation (3).
This results in a list of visibility scores, and a matrix of association strengths,
which can serve as input for a number of analyses or effects studies. A nice
visualisation is possible by drawing a graph, using the concepts as nodes
and the associations as edges. The size of the nodes and the widths of the
edges can then represent the visibility and association strength.
13
Case study: Mohammed in the Polder
As a demonstration of the feasibility of the framework defined above, we
applied it to the media coverage about immigrants, Islam, and terror in the
Netherlands. We retrieved all documents containing (a synonym of) one of
those concepts from the five Dutch national daily newspapers in the period
from January 2000 until September 2005. This resulted in a total of 130,819
documents.
Unit of Measurement
We decided to use paragraphs rather that documents as our basic textual
unit or document. The reason for this is that it is probable that two meaning objects appearing in the same paragraph are actually related, while
this seems less feasible for meaning objects with a number of paragraphs
in between. Moreover, the shorter the textual unit, the less heavily the assumptions of independence within the document and one message per object
per document weigh. Including the headline as a paragraph, this resulted
in a total n of 395,034.
Reading chance
We assume that we can approximate the chance of receiving a message
without taking the content of the message or the internal state of the reader
into account. In the domain of newspapers, this means that we can model
the reading chance of a proposition based on the structural properties of the
message such as circulation of the newspaper, position of the article in the
newspaper, and position of the proposition in the text.
The ideal way to model the reading chance of an article is presumably experimentally. Here, however, we take a simpler approach by looking at
the circulation and the advertising cost.3 The circulation of newspapers is
known, and we can simply assume that the reading chance increases proportionally with circulation. Then, we use the premium charge based on
3
It turned out that the more ‘prestigious’ quality newspapers are more expensive than
would be predicted by comparing circulation; this is probably due to the fact that advertising in these newspapers reaches a subpopulation with more buying power. As we
are not interested in buying power, we take the circulation rather than the base cost per
square milimeter for the comparison between newspapers.
14
position and day of appearance as multiplier for the reading chance of an
article on that day and position (see table 3).
Since our unit of analysis is the paragraph, we also need to determine the
reading chance per paragraph. Lacking real empirical evidence, we assumed
the reading chance to be inversely proportional to the paragraph number,
numbering the headline as 1. Taking the average of the article position
premiums and this inverse proportionality, we arrive at the following formula
for reading chance of an article:
p(art) ∝ circ · pos · weekday ·
1
parnr
(4)
In this equation, circ is the circulation of the newspaper; pos is 2.4 for the
headline, 1.3 for pages 3 and 5, and 1 for all other positions; weekday is 1.15
for Saturday and 1 for all other days; and parnr is the paragraph number.
The normalisation factor required to turn the proportionality into equality
is the sum of this formula for all newspaper articles, including the articles
not in our selection. Although not required for the association networks or
for determining the relative visibility, we determine this factor based on a
15-day sample containing all the news in the papers studied.4
Table 3: Circulation and advertisement cost per newspaper
Circulation
Reach
Front page*
Page 2*
Page 3*
Page 5*
Saturday*
Base cost/reach**
Telegraaf
752,751
2,334,000
2.00
1.00
1.33
1.33
1.13
0,24
AD
640,000
2,100,000
3.50
1.10
1.10
1.25
1.06
0,26
Volkskrant
308,454
705,000
2.00
1.00
1.33
1.33
1.19
0,47
NRC
256,721
451,000
2.00
1.00
1.33
1.33
1.22
0,80
Trouw
108,531
278,000
2.50
1.00
1.50
1.50
1.16
0,49
*) multiplier for an advertisement on that position
**) in euros per square meter per person reached
Content of articles
The content of an article consists of a set of messages, one for each meaning
object, with the probability attached that that message will be received by
4
Note that our article population included all the newspaper sections such as sport and
culture, causing the ‘absolute’ visibility of concepts will be relatively small. To obtain more
reasonable numbers it might be advisable to define a more strict population of interest.
15
a reader, meaning that the reader will consider that object to be included
in the topic of the text.
In this study, we measured three concepts, Immigration, Islam, and Terror,
using keyword lists for each concepts. These keywords were counted in each
of the documents. The equation in formula 1 was applied to these counts ,
resulting in a ‘term-document’ matrix containing the probabilities per article
per meaning object.
Results
Now, we can calculate visibility and association scores using the formulas in
equation 2 and 3. This results in a list of visibility scores and a term-term
matrix of association strenghts.
Issue Visibility
Applying the visibility formula to the document-message probability matrix
yields a table of visibility scores per issue. These scores are the total percentage of the news that is about each issue.5 Figure 1 shows these visibility
scores per month for the investigated period.
In this graph, there are four obvious peaks: 9/11, the bombings in Madrid
and London, and the murder on Theo van Gogh by the Muslim fundamentalist ‘Mohammed B.’ The three terrorist attacks appear as strong peaks in
the terror issue, with a small peak in the attention paid to Islam, while the
murder on Van Gogh is mainly a surge in attention to Islam topics. Overall,
the attention to Terror increased structurally after the 9/11 bombings. Terror remained the highest ranking of the three issues except for the month
after the murder of Van Gogh and in the beginning of 2004, when a political discussion on restricting immigration and the deportation of refused
immigrants exceeded the attention to terrorism.
Associative Frames
The term-term matrix containing the association strenghts between the
three concepts were visualised in figure 2 using a cutoff threshold of 4%
for the associations. The size and percentages in the nodes represent the
5
Note that since some articles are about more than one topic, the total is not the
addition of the underlying scores; see the section on aggregation, above
16
4%
2%
1%
Total
Immigration
Islam
Terror
2005.07
2005.01
2004.07
2004.01
2003.07
2003.01
2002.07
2002.01
2001.07
2001.01
2000.07
0%
2000.01
Visibility (% of total newspaper content)
3%
Date
Figure 1: Issue visibility
visibility of that concept as the proportion of the total news that was about
that concept. The percentage on the arrows is the association strength,
defined as the percentage of articles about the source concept that is also
about the target concept.
The picture of the initial period only shows two weak associations going
from Terror and Islam to Migrants. No direct association is made between
Terror and Islam.
This changes after the 9/11 attacks. Not only does the amount of news about
terror increase sharply, relatively strong associations are found between Islam, Migrants and Terror. When journalists mention Islam, in 12% of the
articles they also mention terrorism. The association between Migrants and
terrorism is also strong (9%), but Terrorism is not associated strongly with
either concept. Probably, terrorism is seen as something happening abroad,
and not strongly connected with ‘our’ Migrants or Muslims, which are still
minor topics in the news.
17
Islam
3‰
Islam
2‰
Migrants
3‰
4%
12%
9%
5%
4%
4%
Terror
2‰
Terror
8‰
(a) 1 Jan. 2000 - 11 Sep. 2001
(b) 11 Sep. 2001 - 2 Nov. 2004
5%
12%
Islam
13‰
Migrants
3‰
5%
Migrants
5‰
5%
4%
11%
8%
Migrants
3‰
Islam 3 ‰
9%
11%
6%
6%
12%
4%
Terror
6‰
Terror
9‰
(c) 2 Nov. 2004 - 2 Dec. 2004
(d) 2 Dec. 2004 - 1 Oct. 2005
Figure 2: Association Networks
No so, however, in the month after the murder on Theo van Gogh. Islam
suddenly becomes a top issue with which Terror is strongly associated (12%).
The associations of Islam and migrants with Terror do not change much, but
suddenly Migrants are highly associated with the Islamic religion. In the
relative calm after that hectic month, the media mostly revert to the situation before the murder, with only a slightly increased association between
Migrants and Islam.
Since this is a fairly simple case study rather than an exhaustive investigation
of this topic, we should be reluctant with concluding much from these figures.
What is interesting, however, is that 9/11 seems to have caused the greatest
structural change in both issue attention and the associative employed in
the media. During the peak month of the murder on Theo van Gogh there
was a surge in attention for the Islam and Migrants and Terror were strongly
associated with this concept. Except for a slight increase in the association
18
between Migrants and the Islam, however, things returned to ‘post-9/11
normality’ after this event.
Discussion and Conclusions
Framing and Agenda Setting are two influential theories of media effects, and
a number of studies have been looking at a possible convergence of the two
theories. We have argued that ‘second level’ agenda setting is comparable
to a subset of the framing effects as described in the framing literature.
This subset, which we labelled associative frames, consist of association
networks in text (media frames) and in memory (audience frames). We
regard associative frame setting as a transfer of salience from these textual
networks to the associative memory.
Taking an information theoretic framework, we have presented a uniform
way to measure the media agenda and associative media frames. Associative
frames are measured based on the co-occurrence of messages, while the media
agenda is measured using the occurrence of these messages.
We applied this method to the Dutch newspaper coverage of Immigrants, the
Islam, and Terrorism over the past five years. The visibility of the Islam and
Terrorism increased structurally after 9/11, while Islam had a strong peak
after the assassination of the film producer Theo van Gogh by a fundamental
Muslim of Moroccan descent.
Looking at the associative networks, we see that initially these issues are
mainly seen as distinct topics. After 9/11, there is a surge in media attention
for Terror and both immigrants and the Islam become moderately associated
with it. In the turbulent month after the assassination of Van Gogh there
was a relatively strong association between all three concepts. After this
month, the association network returned to the earlier state, except for an
increased link between immigrants and terrorism.
Limitations and Challenges
As we have seen in our study, information theory provides an intimate
link between association patterns, conditional probabilities, and expectations/salience. This opens up a way to experimentally validate associative
frame setting by using reaction times: if a subject has a strong association of
Bombing with Al Qaeda, we expect a subject to read a sentence containing
19
bombing more quickly after reading a sentence containing Al Qaeda; and
to a lesser degree the other way around. Thus, we can use reaction time as
an alternative to survey questions to measure the effect of media exposure,
possibly making experimental settings using manipulated media exposure
more valid due to the more subtle effect measure.
It should be noted that not all frames that have been proposed in the literature will be measurable using co-occurrence. What we labelled associative frames probably covers the attributive or associative ‘content’ frames
expressed in terms of salience or importance of certain aspects of issues
(Entman, 1993; Scheufele, 2000; De Vreese, 2005), the affective and cognitive frames of Ghanem (1996) or the reasoning devices of Gamson and
Modigliani (1989).
The ‘central organizing theme’ as proposed by Tankard et al. (1991) and Pan
and Kosicki (1993) will probably surface as a strong association between the
main actors or issues of a story, although it might be more implicit. Symbolic
frames, such as described in Ghanem (1996) and Gitlin (1980) will be more
difficult as symbolic images or catchphrases will not appear in statistical
cooccurrence.
Less superficial frames, such as frames relying on evaluative charge or causal
direction will simply require more information, such as the evaluative charge
or directionality of the association. For these frames, knowing that there is
an association is not enough: we also need to know what the association is.
This is still consistent with Collins theory, however, as they also state that
there are multiple possible links between concepts. However, which types of
media associations can be transferred to the audience, and what effect they
will have on decisions, is an open question.
Finally, a very interesting open question is that of determining the right
document size. Using paragraphs, such as in the case study, probably underestimates the associations. If the first paragraphs mentions Muslims,
and the second mentions Terrorism, it seems plausible to infer an associative effect. Content analysis looking at media frames generally restrict these
frames to occur within an article. It might well be the case, however, that
the audience will also associating the concepts in two articles that are printed
side by side or simply in the same newspaper. Since the method proposed
here is general enough to deal with all those document sizes, this provides
an opportunity to see whether restricting oneself to articles is plausible in
investigating media effects.
20
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