=Paper=
{{Paper
|id=Vol-2723/short34
|storemode=property
|title=Quantifying Iconicity in 940K Online Circulations of 26
Iconic Photographs
|pdfUrl=https://ceur-ws.org/Vol-2723/short34.pdf
|volume=Vol-2723
|authors=Thomas Smits,Ruben Ros
|dblpUrl=https://dblp.org/rec/conf/chr/SmitsR20
}}
==Quantifying Iconicity in 940K Online Circulations of 26
Iconic Photographs==
https://ceur-ws.org/Vol-2723/short34.pdf
Quantifying Iconicity in 940K Online Circulations of 26
Iconic Photographs
Thomas Smitsa , Ruben Rosb
a
Utrecht University, 10 Trans, Utrecht, 3512JK, Utrecht, The Netherlands
a
Luxembourg Centre for Contemporary and Digital History (C2DH), 11 Porte des Sciences, Esch-sur-Alzette,
L-4366, Luxembourg
Abstract
What impact do digital media have on the creation, selection, distribution, reception and meaning of
iconic photographs? Recent studies have suggested that digital circulation, especially in a memeified
form, might lead to an ‘erosion,’ ‘fracturing,’ or ‘collapsing’ of the original context and meaning of
iconic pictures. Using a close reading methodology, these studies are necessarily based on a limited
sample – in number, period and geographic distribution – of online circulations. Introducing a
distant reading methodology to the study of iconic photographs, this paper applies the Google Cloud
Vision API to retrieve 940K online circulations of 26 iconic images between 1995 and 2020. We
operationalize the ‘loss of meaning/context’ hypothesis by using document embeddings to study the
relationship between the iconic photographs and the text surrounding them on the webpage. Based
on this distant reading, we argue that the digital circulation of iconic photographs is comprised of
similar contextual, self-referential and non-referential combinations of images and texts.
Keywords
iconic photographs, data mining, image-text analysis, top2vec, document embedding,
1. Introduction
An exploding Zeppelin; a Buddhist monk engulfed in flames; a portrait of a young Cuban
revolutionary; an astronaut taking man’s first steps on the moon; a protester blocking a tank;
President Obama in the situation room. For many readers, these textual sketches conjure up a
group of well-known iconic images: photographs that, in an often-quoted definition, are ‘widely
recognised and remembered, are understood to be representations of historically significant
events, activate strong emotional identification or response, and are reproduced across a range
of media, genres, or topics.’ [8]
Traditionally, the theoretical concept of the iconic photograph has mostly been tied to
twentieth-century top-down mass media, such as the newspaper, the illustrated magazine,
and television [23, 8]. In recent years, scholars have started to debate the effects of digital
media on the creation, selection, distribution, reception, and meaning of iconic images [3, 21,
6, 9, 17]. What happens to older iconic photographs when they are circulated online and how
do digital media impact the formation of new iconic imagery?
While answers to these questions vary, most scholars agree that digital media diminish the
power of the iconic image. The digital circulation of digitized and born-digital iconic pictures
CHR 2020: Workshop on Computational Humanities Research, November 18–20, 2020, Amsterdam, The
Netherlands
£ t.p.smits@uu.nl (T. Smits); ruben@rubenros.nl (R. Ros)
DZ 0000-0001-8579-824X (T. Smits); 0000-0002-5303-2861 (R. Ros)
© 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
375
is described as ‘trivializing’ [3], ‘decontextualizing’ [21], ‘eroding’ [6], ‘fracturing’ [17] or ‘col-
lapsing’ [16] the original context and meaning of iconic images. Meme-ification of photographic
icons is seen as the most extreme manifestation of this process. Memes collapse the ‘original
historical and biographical contexts’ of the iconic picture [16], they threaten to ‘destroy’ its
original meaning and, as a result, its ‘political and ethical significance’ [3], and memes ‘poach’
the original meaning of the icon and ‘supplement it with new interpretations that typically
deviate from the main narrative behind the famous image’ [17].
Most studies on iconic imagery, both off- and online, are based on a close reading of the
iconic image itself or a limited sample – in number, period and geographic distribution – of
circulations [7, 11, 24, 3, 10]. Introducing a combination of ‘distant reading’ [20] and ‘distant
viewing’ [2, 26] methodologies to the study of iconic images, our project uses the Google Cloud
vision Application Programming Interface (GCV API) to retrieve 940K online circulations,
remediations and appropriations of 26 iconic photographs between 1995 and 2020 (see table
1). Emphasizing how meaning is created by an interplay of images and texts, we apply several
computational techniques to test if iconic images lose their original iconic meaning in the digital
realm.
This short paper presents the first two steps of the project. First, we present the process of
data gathering and harmonization. We use the GCV API to find circulations of 26 photographs
that are widely described as being iconic in secondary literature [12]. By re-uploading identified
circulations to the API, we partly mitigate the temporal bias of the GCV API and were able
to find circulations that stretch back until the early days of the internet. The API returns
metadata of the URLs where the images can be found, as well as the titles of the page and
labels assigned to the images. Additionally, we scraped the webpages where the images can
be found and extract the data, language and full-text on the page. Upon completion of the
project, we will release a data-set that allows for further study of the online life of iconic
imagery and comparison over time.
We see the meaning of an iconic image as the product of the reciprocal, or ‘dialectical,’
interplay between image and text[18]. We are currently applying several computer vision
techniques to discover large-scale visual patterns in the 940K images in our corpus. In this
paper, however, we operationalize the ‘loss of meaning/context’ hypothesis mentioned above by
focusing on the text. Using document embedding clustering, we posit that ‘loss of context’ can
be measured by the prominence of clusters that refer to the original context. For example, a
contextual digital circulation of the famous ‘accidental napalm’ photograph will be surrounded
by textual references to the Vietnam War, while a non-contextual and/or memeified version
will lack these references.
2. Related work
Visual search engines, such as Google Image Search (since 2007) and TinyEye (since 2008) allow
users to track where images are published online. Humanities scholars have used these services
to map the online circulation of images, for example by tracking the reuse of paintings uploaded
by the British National Gallery [13]. Others have used reverse image search to track the digital
afterlife of an iconic photograph of a Swedish woman hitting a neo-Nazi with a handbag [16].
More recently, scholars have started to apply the GCV API instead of the interface to map
the digital ‘cross-platform circulation’ of images of the 2018 FIFA World Cup Final Draw [5].
Building on this last study, we found that neither Google’s interface nor the GCV API give
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a representative overview of where an image is published on the web. Being a search engine,
the results that Google shows are especially biased towards the ‘recent’ internet. Because we
are especially interested in developments over time, we developed a pipeline to circumnavigate
this problem.
Despite the observation that most modern media, such as the newspaper, the television
and the internet, are ‘multimodal’ – i.e. consist of combinations of text, images and audio –
digital and computational humanities research has mostly been applied to discover large-scale
patterns of meaning in text(s) [26]. Following the theoretical concept of image-text, Wevers
e.a have applied jointly used computer vision and NLP techniques to study the patterns of
meaning in a large corpus of advertisements [27]. We are currently applying computer vision
techniques to study the 940K images in our corpus. This short paper, however, presents only
the textual side of our project.
3. Corpus
Researchers of iconic photographs have made a distinction between national and global icons,
while also noting the possible overlap between these two sets of images [22, 4]. Based on
these kind of studies, Van der Hoeven (2019) set out to discover if some iconic photographs
are part of global visual memory: ‘a limited set of images that people all over the world have
seen and remembered’[12]. Based on a literature review, he presents a list of twenty-six iconic
photographs that are widely described as iconic. While there are other lists of photographs
that are frequently described as ‘iconic,’ such as Wikipedia’s “List of Photographs Considered
the Most Important” [14], we decided to use Van der Hoeven’s (2019) list as our corpus because
it is derived from the academic discussion of iconic photographs (see Table 1).
4. Data gathering and harmonization
Our data gathering pipeline consists of two parts. First, an image is uploaded to the GCV
API, which enables users to apply all sorts of computer vision techniques in the cloud. Our
pipeline relies on the basic functionality of the API to find full and partial circulations of the
uploaded image on the web. The API returns a list of web addresses (URLs) that contain
the iconic image. Because circulations of the iconic image on these URLs are often slightly
different than the version we uploaded, they can be used as input for the second iteration.
Using this iterative process, the pipeline not only finds more images but also less recent ones
(see figures 1 and 2). The second part of the pipeline includes several methods to scrape the
URLs returned in the first part and collects (meta)data, such as the HTML time-tags and
the language of the webpage. Although the pipeline can be used to map the dissemination of
images online, it also has some shortcomings. First, the algorithms behind the GCV API are
proprietary, which makes it impossible to know what percentage of the images are indexed and
if specific parts of the internet, social media for example, might be relatively under- or over-
represented. Furthermore, we also don’t know which percentage of indexed URLs containing
the uploaded image are returned by the API. Second, the pipeline only returns URLs that are
online, meaning that many previous circulations of iconic photographs, for example from the
early 2000s, will not be retrieved by it. As a result, our data-set carries a specific time stamp
(the date on which our pipeline scraped the URL). By releasing our data-set (upon completion
of the project), we hope to enable more sound historical comparisons in the future.
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Table 1
Iconic images in the corpus
known as photographer year historical event circulations doc2vec size
Migrant mother Dorothea Lange 1936 Great Depression 41697 5315
Falling Soldier Robert Capa 1936 Spanish Civil War 18194 2177
The Hindenburg Disaster Sam Shere 1937 Zeppelin 36683 4867
Times Square Kiss Alfred Eisenstaedt 1945 V-Day 65164 3820
Raising the Flag on Iwo Jima Joe Rosenthal 1945 Pacific War 63249 4804
Holocaust survivors Lee Miller 1945 Holocaust 18343 2954
Raising a Flag over the Reichstag Yevgeny Khaldei 1945 World War II 90344 1727
Gandhi and the Spinning Wheel Margaret Bourke-White 1946 Mohandas Gandhi 10893 3097
The Founding of the PRC Hou Bo 1949 Mao Zedong 2865 309
Assassination of Inejiro Asanuma Yasushi Nagao 1960 post-war Japan 3921 745
Guerillero heroico Alberto Korda 1960 Che Guevara 108288 3034
The Burning Monk Malcolm Browne 1963 Vietnam War 18122 4091
Saigon Execution Eddie Adams 1968 Vietnam War 18305 3437
A Man on the Moon Neil Armstrong 1969 Space Race 186921 4035
Kent State Shootings John Filo 1970 Kent State 7320 3029
Accidental Napalm Nick Ut 1972 Vietnam War 38619 2834
Allende’s Last Stand Luis Orlando 1973 South-American Coups 6997 469
Afghan Girl Steve McCurry 1984 Afghan War 47892 2682
Tank Man Jeff Widener 1989 Tiananmen Square Protest 63182 3870
The vulture and the little girl Kevin Carter 1993 Sudan famine 30121 2031
Survivor of Hutu death camp James Nachtwey 1994 Rwandan genocide 3395 696
The Falling Man Richard Drew 2001 9/11 11681 1918
Hijacked airplane unknown 2001 9/11 6938 1599
Abu Ghraib prisoner Sergeant Ivan Frederick 2003 Iraq War 3601 936
The Situation Room Pete Souza 2011 War on Terrorism 20102 4752
Alan Kurdi Nilüfer Demir 2015 Refugee crisis 24432 2251
total 947269
5. Theoretical Background
In their influential book No Caption Needed, Robert Hariman and John Louis Lucaites rely
on the work of visual culture studies scholar W.J.T. Mitchell to describe how the meaning
of iconic images is constructed [10]. Mitchell argued that the meaning of an image can only
be deduced by looking at it, or reading it, in relation to its surrounding (con)text: “The
interaction of pictures and text is constitutive of representation as such: All media are mixed
media, and all representations are heterogeneous” [19]. In an overview of his work, Mitchell
identified three main types of the intertwined ‘dialectical constellations’ between images and
texts: “‘imagetext’ (if word and image are seamlessly united), image-text (if they are distinct
but connected) and image/text (if they are in conflict or tension)” [18].
Our data-set provides a unique opportunity to study the relation between images and texts,
because the meaning of (roughly) the same 26 images is constructed over and over again, 940K
times in fact, by different texts. Using Mitchell’s concepts, we see iconic “imagetexts” as those
constellations where the text refers to original historical referent. For example, the ‘accidental
napalm’ photograph is used to say something about the Vietnam War. Iconic “image-texts”
are marked by texts that refers to concepts that fall outside what is shown on the image, but
are still connected to it. For example, an iconic image is used to say something about iconicity
itself, image manipulation, or the power of photography. Third, iconic “image/texts” display
a tension between the image and the text. Memeified versions, where the text is entirely
non-referential, would fall within this category.
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Figure 1: Circulations per iteration per year of the ‘burning monk’ photograph.
Figure 2: Circulations per iteration per year of the ‘war room’ photograph. Taken in 2011, the pipeline still
finds more circulations in 2020.
6. Methodology
We hypothesize that we can understand the three different iconic “image/-text” types by the
presence or absence of specific clusters. Several techniques could be used to gain insight into the
semantic context of the online circulations of the iconic images. We originally looked into topic
modelling, as it is widely used and has shown its value in humanities research [25]. However,
topic modelling has several downsides. For example, relying on bag-of-words representations of
documents, LDA ignores the order of words. Second, determining the optimal number of topics
remains, in most cases, educated guesswork. In order to circumvent these issues, we apply a
modified version of the recently proposed top2vec method, which uses joint document and word
semantic embedding and Hierarchical Density-Based Spatial Clustering to find topic vectors in
a corpus [1]. For preprocessing, we applied lowercasing, removed all non-alphanumeric tokens
and removed webpages with less than fifty tokens. This last step was especially important
because it removes many of the “404 not found” and “you need javascript to view this page”
.html files from our corpus. As a result of computational limits, we only trained document
embeddings on English-language URLs, roughly round 70%) of our corpus, and on samples of
15.000 webpages per iconic photograph (if the number of URL’s was larger than 15.000).
The original top2vec method clusters document embeddings (trained with the popular
doc2vec algorithm) by first reducing the number of dimensions using UMAP, and then clus-
tering the (reduced) embedding space using HDBSCAN, a density-based clustering algorithm
[15]. Because doc2vec embeds both documents and words in a vector space, the most topical
379
Table 2
Clusters that reference the original event
known as cluster words (first 10)
Raising a Flag over the Reichstag 7 troops battle germans operation surrendered stalingrad allied soviets german surrender
Kent State 0 kent students campus guardsmen shootings guard state ohio nine university
Accidental Napalm 9 phuc ut her she kim scars pain screaming waibel bang
Tank Man 3 crackdown tiananmen massacre protests beijing student chinese suppression hundreds students
words for every cluster can be found by averaging the document vectors in every cluster and
subsequently identifying the most similar words to the average document vector. In this way,
the sets of webpages are clustered and the clusters are identified by their top terms, similar to
topic modelling approaches. The advantage of the top2vec method lies in the data-driven iden-
tification of the number of clusters. The density-based clustering does not need intervention
through manual setting the number of clusters. Also, the document (and word) embeddings
offer more versatility compared to LDA topic modelling and can be used in other methods, for
example the comparison of documents across subsets through model alignment.
However, the use of HDBSCAN by the orginal top2vec paper results in “hard clustering,”
which means that every document is assigned to one cluster. Because of the heterogeneous
nature of our corpus, we decided to use a Gaussian Mixture Model (GMM) instead of HDB-
SCAN for clustering. GMM clustering superpositions clusters as Gaussian distributions. For
every document the probability of the document belonging to cluster k is calculated, which
results in a probability distribution for every document. This is important for our research
specifically, because we found that hard clustering obscures the “self-referential” language of
iconicity. In GMM soft clustering, words such as “photograph”, “iconic”, and “famous” are
grouped together, while hard clustering disperses these words over other clusters.
One issue with GMM (soft) clustering is that it involves the manual setting of the k. For
setting the number of clusters, we initially used the number of clusters returned by HDBSCAN
hard clustering. However, a more common an statistically sound way of determining the
number of clusters in GMM clustering is the use of the Bayesian Information Criterion (BIC)
and the Akaike Information Criterion (AIC). We trained GMM models with 3 to 40 clusters
and looked for the point where the BIC and AIC score were lowest.
7. Results
By comparing prominent clusters of all the 26 iconic photographs, we can discover several
large-scale patterns in the relationship between the images and the surrounding text that play
an important role in their online circulation. Most importantly, we find similar clusters for all
the 26 photographs. First of all, almost all of the photographs have a imagetext topic that
references the original historical event (see table 2). These clusters basically tells us what
is on the picture. For example, cluster 3 of the Tank Man photograph contains the words
‘crackdown, Tiananmen, massacre, protests, Beijing’ and cluster 0 of the Kent State ‘Kent,
students, camus, guardsmen, shootings.’
Next to these topics on the historical event, most iconic photographs also contain a image-
text topic that is self-referential and contains words that refer to photography, visuality and/or
iconicity. For example, cluster 5 of the Reichstag photograph contains the words ‘iconic,
Yevgeny, Khaldei (the first and last name of the photographer), photographer, camera’ The
380
Table 3
Self-referential clusters
known as cluster words (first 10)
Raising a Flag over the Reichstag 5 iconic leica yevgeny khaldei photographer camera photograph photographs picture photo
Kent State 6 photograph photographs photo photographer picture taken iconic pulitzer famous image
Accidental Napalm 3 photography verve photographers photographer famous taken exhibition photographic captured picture
Tank Man 8 photograph photographer photographs iconic prints picture photography photo photos taken
Table 4
Clusters that point to memeified circulation
known as cluster words (first 10)
Raising a Flag over the Reichstag 10 memes me don my really know listeners meme just shit
Kent State 1 meme strutting chouchou thread memes forums threads blog posts entries alexa
Accidental Napalm 11 meme memes swooty lol nbsp funny swiggity fucking booty fuck
Tank Man 9 memes memebase gifs meme wallpapers comics nbsp wallpaper funny lol
Table 5
Clusters that point to events that increased circulation
known as cluster words (first 10)
Accidental Napalm 6 facebook aftenposten hansen norwegian erna solberg zuckerberg egeland deleted
Tank Man 0 kong chinese crackdown hong china demonstrations communist government mainland
inclusion of the words ‘iconic, pulitzer and famous’ in cluster 6 of the Kent State photograph
makes clear that this is also self-referential. Clusters 3 and 8 of the Accidental Napalm and Tank
Man photographs contain many of the same words (see table 3). These self-referential clusters
also often contain references to other iconic photographs. For example, next to the words
photo, camera, image and other references to visuality, cluster 4 of the Abu Graib photograph
contains the words ‘moon, Neil, Armstrong, Capa (the famous photographer) and napalm,’
which clearly reference other iconic pictures. Cluster 5 of the accidental napalm photograph
entirely references the photograph of the drowned Syrian boy Aylan Kurdi, starting with the
words ‘Kurdi, Aylan, refugee, Syrian, boy, washed, drowned.’
Thirdly, almost all photographs also include a image/text cluster that refers to memeified
online circulations of the iconic photograph (see table 4). This kind of cluster not only contains
words like ‘meme, memes, funny and lol’ but also names of specific memes that are apparently
important for the online circulation of the iconic photograph. For example, the Kent State pho-
tograph is associated with the ‘Strutting Leo’ meme, while the Accidental Napalm photograph
is connected to the ‘Swiggity Swooty, I’m coming for that booty’ meme.
Several photographs also have a cluster that points to a specific event that increased its
online circulation. Cluster 6 of the Accidental Napalm photograph refers to the controversy
surrounding its removal from Facebook, which cited rules concerning under-aged nudity, after
a Danish newspaper posted it in 2016. Somewhat differently, cluster 0 of the Tank Man
photograph clearly indicates that it was frequently circulated in connection to the Hong Hong
protest of 2019/2020 (see table 5).
381
8. Conclusion
This paper has presented the first two steps of our project on the digital circulation of iconic
photographs. We have shown how we can use the GCV API to retrieve 940K circulations of
26 images that are frequently described as iconic in academic debates. By taking an iterative
approach of reuploading images to the API, we were able to retrieve less recent circulations of
the iconic images. Second, we have shown how document embeddings can be used to study
the relationship between the iconic photographs and the text surrounding them. We can use
this method to operationalize the ‘loss of context/meaning’ hypothesis that was put forward
by several recent studies on the digital circulation of iconic pictures. While more research
is needed, our results do not suggest an increasingly pronounced link between digital media
and decontextualized circulation, in the form of memes or otherwise, of iconic photographs.
Rather, it shows how iconic photographs are circulated in different distributions of contextual
imagetexts, self-referential image-texts and decontextualized image/text.
In the next phases of our project we will further explore how we can use computational
methods to explore the relationship between images and text in the production of meaning.
First, we want to improve the textual analysis by experimenting with different methods and
seeing how the distribution of clusters changes over time. Second, we hope to combine computer
vision and text analysis to study iconicity. This entails the detection and classification of
variation in the images, through for example crop detection and object detection. Moreover,
we hope to combine visual and textual features in embedding models that will hopefully shed
more light on the online afterlife of iconic images.
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