Memetic Assemblages in Digital Environments - Algorithmic Body Culture on Instagram
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Memetic Assemblages in Digital Environments –
Algorithmic Body Culture on Instagram
MASTER THESIS
Submitted in Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE
in Strategic Management
Univ.-Prof. Mag. Dr. Andrea HEMETSBERGER
Department of Strategic Management, Marketing and Tourism
The University of Innsbruck School of Management
Submitted by
Nikolai HOLDER
Innsbruck, May 2020
Acknowledgments I would like to thank my supervisor Jonathan David Schöps, MSc BSc BSc, who provided all the necessary guidance to complete this work. Even though the circumstances while writing were extraordinary, he was always eager to help and give support. It was a pleasure working with you, Jonathan! Of course, I would also like to express my thanks to Univ.-Prof. Dr. Andrea Hemetsberger, who put together this interesting Master’s program that built the foundation to write such a thesis. Your support was worth its weight in gold! Last but not least, I want to acknowledge the impact of my family and friends who had encouraging words whenever times got tough and tedious. Thanks for everything!
Abstract This thesis investigates the negotiation of body ideals between human and non-human actors on Instagram. The study examines how Instagram’s explore feed algorithm is (de-)stabilizing body ideal discourse and how it is related to the reproduction of societal bias. Following an Assemblage Theory approach, visual content analysis was chosen to analyze both the expressive and material components of body ideal visual rhetoric. The study shows that the algorithm is mostly stabilizing the respective body ideology assemblages via acting as a memetic copying machine of user behavior. Nevertheless, some findings indicate that the algorithm is also destabilizing the respective assemblages. In this regard, a tendency of destabilizing anti-mainstream body ideologies was found which supports the claim that Instagram’s algorithm is reproducing societal bias.
Table of Contents
I. Index of Figures ................................................................................................................ III
1. Introduction ......................................................................................................................... 1
1.1. Problem Statement ..................................................................................................... 1
1.2. Research Aim ............................................................................................................. 3
1.3. Structure of the thesis ................................................................................................. 4
2. Theoretical Background ...................................................................................................... 4
2.1. Algorithms .................................................................................................................. 4
2.1.1. What are Algorithms? ........................................................................................................ 5
2.1.2. Big Data.............................................................................................................................. 7
2.1.3. Recommender Systems ..................................................................................................... 8
2.1.4. Algorithmic culture .......................................................................................................... 11
2.2. Body culture ............................................................................................................. 12
2.2.1. The socio-cultural body ................................................................................................... 13
2.2.2. Body Ideals....................................................................................................................... 14
2.2.3. Mainstream vs. Anti-Mainstream Body Ideals ................................................................ 16
2.3. Instagram .................................................................................................................. 18
2.3.1. What is Instagram? .......................................................................................................... 18
2.3.2. Digital Visual Rhetoric ...................................................................................................... 20
2.4. Enabling lenses ......................................................................................................... 21
2.4.1. Assemblage Theory.......................................................................................................... 21
2.4.2. Meme Theory .................................................................................................................. 23
3. Methodology ..................................................................................................................... 25
3.1. Data Collection and Sampling.................................................................................. 26
3.2. Coding ...................................................................................................................... 29
3.3. Analysis .................................................................................................................... 30
4. Results ............................................................................................................................... 32
4.1. Descriptive ............................................................................................................... 32
4.1.1. Mainstream Assemblage ................................................................................................. 32
4.1.2. Anti-Mainstream Assemblage ......................................................................................... 34
I4.2. Interpretative ............................................................................................................ 36
4.2.1. Memetic Reproduction .................................................................................................... 36
4.2.2. Destabilizing Rejection .................................................................................................... 40
4.2.3. Algorithmic (De-)stabilization .......................................................................................... 43
5. Conclusion ........................................................................................................................ 49
5.1. Discussion ................................................................................................................ 49
5.2. Limitations & Further Research ............................................................................... 57
II. References .................................................................................................................... 60
III. Appendix ...................................................................................................................... IV
IV. Affidavit ....................................................................................................................... XI
III. Index of Figures
Figure 1 - Curation Algorithm Scheme. Source: Latzer et al. (2014) ........................................ 6
Figure 2 - Scheme of Data Collection. Source: own Figure .................................................... 27
Figure 3 - Mocking of the bodybuilder theme within the mainstream explore feed ................ 44
Figure 4 - Image of Kim Kardashian obtained on the first day of data collection ................... 45
Figure 5 - Example for a #fashion related mainstream post within the anti-mainstream
explore feed .............................................................................................................................. 46
Figure 6 - Example for a #VSCO related mainstream post within the anti-mainstream
explore feed .............................................................................................................................. 47
Figure 7 - Pattern of mainstream image occurrence within the anti-mainstream
explore feed .............................................................................................................................. 49
III1. Introduction
In his book Leviathan Thomas Hobbes famously stated: “knowledge is power.” Even though
he most probably did not think about today’s digitized society, this saying is truer than ever.
With the emergence of sophisticated algorithms capable of transforming Big Data into
knowledge (Deighton, 2018), algorithmic technologies have become powerful entities of reality
construction (Beer, 2009). Algorithms are permeating everyday life by being intermingled with
many forms of communication tools and platforms (Beer, 2009). Larger online services such
as Google, Facebook, Instagram and Co. are all built around algorithms, leaving basically no
room to evade the influence of this technology (Berman & Katona, 2020, 2020; Lash, 2007).
Two main problems are associated with this omnipresence: first, algorithms are prone to
(intentional) errors and societal bias (Noble, 2018; Striphas, 2015). Second, algorithms act in
the hidden realm making them near impossible to scrutinize and opaque (Beer, 2009). What
does it mean for society when a hidden, highly complex and potentially flawed technology
exerts high amounts of power on everyday lives? Recent events like Brexit or the presidential
election of Donald Trump are linked to the intentional control of information flow on different
social media platforms (Deibert, 2019). This led to an increased skepticism towards such
services and algorithms in general. Further, many individual and personal notions are negotiated
on social media when comparing oneself to the idealized online representations of others
(Brunborg & Burdzovic, 2019). In doing so, harmful pressure is created. Roughly 90 percent
of college women and 70 percent of college men are affected by body dissatisfaction due to
idealized beauty representations consumed through different media channels (Neighbors &
Sobal, 2007). To investigate how algorithms are related to such social and cultural phenomenon
is therefore of large interest.
1.1. Problem Statement
This thesis is interested in the connection between algorithmically controlled information flow
and the construction of body ideals in digital environments. Algorithms can be described as
“encoded procedures for transforming input data into a desired output, based on specified
calculations” (Gillespie, 2014, p. 167). This means that actions of people are transformed into
information which then again influences subsequent actions. As a result, algorithms exert post-
hegemonic power of reality construction, both on the individual and the broader cultural level
(Lash, 2007; Striphas, 2015). The error sensibility of algorithms constitutes the main problem
1since it is linked to the reproduction of societal bias (Noble, 2018) which will affect individuals
and a cultural object such as the body in an arguably harmful way.
Sociologically speaking, the body is not only a physical object (Waskul & Vannini, 2012) but
also a socio-cultural object that incorporates meaning and abstract components (Turner, 2008).
In the light of those dimensions, the body becomes an interpretation and representation of
society (Adelman & Ruggi, 2016) that is constructed by several actors, both human and non-
human (Hoffman, Novak, Fischer, & Kozinets, 2018).
Instagram is a fruitful platform to investigate on the body since it is “a place where shared
understandings of contemporary body aesthetics and beauty are negotiated among market
actors” (Schöps, Kogler, & Hemetsberger, 2019, p. 196). Further, the huge user base of over
one billion active users (Instagram Press, 2020) and the algorithmically selected stream of
images qualify Instagram as an impactful medium of (algorithmic) body (de-)construction
(Cohen, Fardouly, Newton-John, & Slater, 2019).
According to DeLanda (2013) such cultural phenomena like the construction of body ideals are
best described as cultural assemblages. Assemblages are composed of components that either
are material or expressive components (Karaman, 2008). Functionally speaking, those
components can stabilize or destabilize an assemblage (Rokka & Canniford, 2016) and involve
a broad variety of objects and actors. To understand how cultural assemblages like body ideals
perpetuate within the digital realm, this thesis draws on Richard Dawkins’ Meme Theory
(2009). Memes are “replicable transmitters of cultural meaning” (Spitzberg, 2014, p. 312)
which includes face-to-face communication (Dawkins, 2009) but also digital communication
tools such as e-mails or visuals (Spitzberg, 2014). In this sense, memes act as a fundamental
unit of cultural transfer (Heylighen & Chielens, 2009).
The springboard for this thesis is Instagram’s introduction of their algorithmically curated
explore feed in 2016 (Instagram Press, 2020). This change begs the question which content is
recommended by the algorithm and how it is related to the phenomenon of body culture
construction. Even though algorithms play an increasingly powerful role in creating societal
realities (Beer, 2009) no research has been published on the actual workings of Instagram’s
recommendation algorithm with relation to body culture construction. Research on Instagram
has mainly enlightened how the visual content affects the mental health and behavior of users
regarding their own body shape (Talbot, Smith, Cass, & Griffiths, 2018; Yan & Bissell, 2014).
Other studies outlined the general social impact of algorithmic technologies with no explicit
2reference to Instagram (Beer, 2009; Berman & Katona, 2020; Cotter, 2018; Gillespie,
Boczkowski, & Foot, 2014; Lash, 2007) or with reference to other social network sites and
online services (Airoldi, Beraldo, & Gandini, 2016; Davidson et al., 2010; Hosanagar, Fleder,
Lee, & Buja, 2014; Kim & Kim, 2019; Nguyen et al., 2014, 2014; Wu, Pedersen, & Salehi,
2019). The thesis at hand aims to fuse those streams of research and narrow the present gap.
The algorithmically selected visual output of the Instagram explore feed was examined in a
systematic way by adopting an assemblage theory perspective (DeLanda, 2013). This approach
gives deep and new insights into the constructive or deconstructive nature of those
algorithmically recommended visuals. Following Rose’s (2016) suggestions to analyze the site
of the image, this research provides a Visual Content Analysis of both material and expressive
components. Further, Richard Dawkins’ (2009) Meme Theory serves as a powerful framework
to scrutinize the findings.
1.2. Research Aim
First, this research responds to Beer’s (2009) and Striphas’ (2015) call for research on the
impact of web infrastructures on individual lives. In doing so, a better understanding of
algorithmic power is reached. Building upon the work of Lash (2007), Beer (2009) and Striphas
(2015), the concept of algorithmic culture is applied to the Instagram context to understand,
how the platform is exerting power regarding the construction of cultural meanings. Second,
the author considers Novak’s and Hoffman’s (2018) suggestion of including machine
interaction in modern consumer-behavior theories by implementing a human-machine
assemblage. Identifying how the machine, more specifically algorithms, interact with humans
on Instagram will help to understand how the Instagram algorithm is related to the construction
of body ideals. Summarizing, the following questions will be investigated and should be
answered eventually:
How does the Instagram algorithm (de-)stabilize the visual rhetoric of body ideals?
How is the Instagram algorithm reproducing societal bias in the digital realm?
Body ideals and resulting body images (harmfully) influence a vast amount of people
(Neighbors & Sobal, 2007), especially by being transported and replicated through social media
(Baker, Ferszt, & Breines, 2019). Since social media and algorithms are closely interwoven
with the everyday life (Beer, 2009; Lash, 2007) the scrutinization of such platforms,
technologies and digital environments is crucial for modern society.
31.3. Structure of the thesis
To investigate on the already stated research questions, this thesis started by introducing the
main theoretical concepts and problems within the introduction. To solidify the understanding
of those topics, a more in-depth overview is given within the theoretical background chapter.
Being one of the main concepts, algorithms are explained in detail, eventually introducing the
concept of post-hegemonic power and algorithmic culture. Both of those terms are crucial for
the understanding of the overall thesis. Subsequently, the body is examined as a socio-cultural
construct that transports deep cultural meanings. Those meanings are partially condensed in
generally, widely accepted body ideals and in the antagonistic deconstruction of those ideals.
To capture those different meanings, this thesis delivers a definition of mainstream and anti-
mainstream body ideals and puts them into comparison. Since the actual research takes place
on Instagram, the third chapter defines this platform and describes its general visual rhetoric.
As a final theoretical input, the author introduces the two enabling lenses: 1) DeLanda’s (2013)
Assemblage Theory and 2) Dawkins’ (2009) Meme Theory. The former sheds light on the
highly complex assemblage of cultural components, the latter acts as a theoretical fundament
to understand how ideas spread within the society.
After the theoretical input, the author explains the used methodology including the sampling,
data collection and analysis process. Concepts embedded in the digital (Rose, 2016) are used to
code and analyze the collected visuals. As a last step, all the results are displayed in a descriptive
manner before delivering an interpretative analysis. The thesis concludes with an in-depth
discussion and a critical reflection on the limits of this research.
2. Theoretical Background
The upcoming section outlines all the relevant concepts and assumption this thesis is based on.
Further, the enabling lenses are described in detail.
2.1. Algorithms
To understand how algorithms influence today’s life, it sure is necessary to define what
algorithms are and how they fundamentally function. Nevertheless, this thesis is not aimed at
computer scientists, which is why the technological components of algorithms are held on a
4rather basic level. Large concepts like big data or recommendation systems are mostly discussed
in the light of their socio-cultural consequences (Beer, 2009; Lash, 2007) and in relation to the
idea of algorithmic culture (Striphas, 2015). Therefore, the following sections discuss
algorithms with regards to their impact on today’s society.
2.1.1. What are Algorithms?
Algorithms “are encoded procedures for transforming input data into a desired output, based
on specified calculations” (Gillespie, 2014, p. 167). While one might immediately think about
computer when reading this definition, algorithms are not limited to the digital realm (Gillespie,
2014). Sorting a fixed number of products by price while following a certain set of rules and
precisely defined steps is an algorithm - regardless if done by a human or a computer. Of course,
backend algorithms of platforms such as Facebook, YouTube or Amazon are not executed by
hand. Huge amounts of computational power are used to analyze much more sophisticated
datasets and problems in real time, eventually predicting which kind of product a consumer
might want to see or purchase at that very moment (Airoldi & Rokka, 2019). Predicting is used
in a slightly sarcastic tone, because the basic concept of making such predictions is relevance,
a rather complex and blurry parameter to evaluate accurately (Zimmer, Scheibe, Stock, &
Stock, 2019). This difficulty opens the door for prediction errors whose appearance have
already been documented (Noble, 2018).
Naturally, making accurate predictions is at the very core of advertisement driven companies
like Facebook (Fourcade & Healy, 2016) or Instagram. This objective leads such companies to
view consumers as “assemblies of data traces” (Cluley & Brown, 2014, p. 119). Whenever an
individual is using an online service or the internet in general, such traces are left. Subsequently,
advanced statistical methods can be applied which basically depersonalize humans (Airoldi
& Rokka, 2019) and reduce them to mere data. Based on those data traces, “individuals are
sorted and scored, then slotted and matched for the purpose of maximizing profit” (Fourcade
& Healy, 2016, p. 10).
Such sorting and scoring algorithms are basically static, just like the example of sorting a set of
products by price. They process one single input, create one single output and stop calculating
afterwards. On the other hand, recursive algorithms reconsider a computational output (and
related human interactions) as input for the next throughput iteration (Airoldi & Rokka, 2019;
Latzer, Hollnbuchner, Just, & Saurwein, 2014). This means that the reaction of a consumer
5towards an algorithmic output (e.g. a content recommendation) will influence the subsequent
output via a feedback loop (see figure 1). By doing so, those platforms not only analyze their
customers, they simultaneously make them key players of training and enhancing algorithmic
processes (Carah & Brodmerkel, 2019). YouTube includes the reaction of previous users
towards a recommended video into the computational process that generates a recommendation
the next time (Airoldi et al., 2016). This “closed commercial loop” (Hallinan & Striphas, 2015,
p. 122) reinforces existing consumer culture in a self-strengthening manner. Cara and
Brodmerkel call this the “recursive nature of consumer participation and surveillance” (2019,
p. 5).
Figure 1 - Curation Algorithm Scheme. Source: Latzer et al. (2014)
Even though algorithms follow arguably clear mathematical instructions their outputs are not
perfect (Airoldi & Rokka, 2019). Research shows that algorithms are prone to bias and error
(Noble, 2018) which is problematic due to their extensive influence on today’s society (Beer,
2009; Striphas, 2015). Since the computational processes are mostly hidden and opaque (Beer,
2009) consumers do not know by which standards their digital traces are evaluated (Fourcade
& Healy, 2016). Therefore, consumers continuously develop theories about those evaluation
processes (Carah & Brodmerkel, 2019) to interact with them in a beneficial way (Cotter, 2018),
also known as pleasing the algorithm. But scrutinizing recommended content while being
trapped in a near invisible computational process proves to be rather difficult for consumers
(Zimmer et al., 2019). Also, due to the acceptance of such services, some researchers question
if the will to scrutinize is even existent (Deighton, 2018). As displayed in the following
6chapters, the opaqueness (Noble, 2018), omnipresence of algorithms (Lash, 2007) and broad
acceptance (Deighton, 2018) lead to significant changes in society and completely new
structures of power (Beer, 2009).
2.1.2. Big Data
The substantial increase in computational power and the decrease of data storage cost led to the
emergence of Big Data (Gandomi & Haider, 2015). Since Big Data is a hot topic, some
confusions, misconceptions and oversimplifications revolve around this term’s definition
(Deighton, 2018). While there is no fully accepted definition (Tonidandel, King, & Cortina,
2017) this thesis defines big data with regard to the presence of three key attributes: volume,
velocity and variety, also referred to as the three V’s (Deighton, 2018; Gandomi & Haider,
2015; Steinberg, 2020). Volume describes the size of a certain dataset (Tonidandel et al., 2017)
which is an important factor but a rather blurry one. Attaching a certain threshold to data size
is difficult since the perception of what is a large volume changes over time (Gandomi
& Haider, 2015). Therefore, a dataset will be understood as large when highly sophisticated,
creative and powerful methods of analysis are necessary to extract meaningful information
(Tonidandel et al., 2017). Velocity refers to the rate of data generation and the necessary
processing speed to comprehensively analyze the data set (Simsek, Vaara, Paruchuri, Nadkarni,
& Shaw, 2019). Variety describes the amount of different data sources (e.g. structured &
unstructured data) and data types (images, text, etc.) that are combined in one dataset
(Tonidandel et al., 2017).
To illustrate those attributes as well as the magnitude of the subject, Instagram will serve as an
example. In 2015, 48,000 images were uploaded to Instagram each and every minute (Berman
& Katona, 2020). Projected onto 24 hours, 69,120,000 images were published on the platform
in one single day. Since the data is comprised of images, videos, text, hashtags and geo-data
but also of consumer behavior information (Steinberg, 2020) a big variety is given. The amount
of images certainly demands powerful analytical processes (Tonidandel et al., 2017) and the
rate of posting can definitely be described as high velocity. Also, to categorize, sort and label
such huge amount of data in real time Instagram needs to process the input with high speed.
The example shows that it is one thing to save data, but another thing to process it. Since
computational power is limited many companies collect more data than they can transform into
meaningful outputs. To a certain extent, Big Data is collected because companies can, not
7because it makes sense at that very moment (Fourcade & Healy, 2016). This turns the whole
Big Data endeavor into an ethical problem: How should consumer consent to a process of data
collection and analysis when neither themselves nor the people employing those processes
know how the data will be used eventually?
While some argue that Big Data is “only” the logical continuation of humanity’s ability to
quantify the world, Deighton (2018) describes this change as a discontinuity comparable to the
industrial revolution. Data collection occurs perpetually and reaches deep into the private
sectors of individuals (Fourcade & Healy, 2016). It has already become an invisible part of
everyday life that is intermingled with many devices and services most individuals use (Beer,
2009; Lash, 2007). By constantly enhancing the statistical models, not only actively provided
data can be analyzed (e.g. comments or likes in social media), moreover, patterns of
unconscious behaviors can be processed to segment and influence consumers (Berman
& Katona, 2020). This development has been described as “surveillance capitalism” (Zuboff,
2015, p. 81). Data and the ability to transform data into algorithmic output gives power of
reality construction to those in charge (Beer, 2009; Kitchin & Dodge, 2011). Recommendation
systems play an important role in this regard (Carah & Brodmerkel, 2019) since they govern
which content users can see on online platforms. Therefore, those systems will be explained in
the following chapter.
2.1.3. Recommender Systems
The mere amount of content provided on social media sites makes it near impossible for
consumers to navigate through without the help of algorithms. Therefore, so called curation
algorithms are broadly implement to provide meaningful and relevant content to users (Berman
& Katona, 2020). Relevance could also be described as the answer to the question what is “hot”,
“trending” or “most discussed” (Gillespie, 2014, p. 167) at the moment. The infrastructures
which automatically give answers to those questions (Latzer et al., 2014) are called
recommender systems. Those systems are the non-human actors in the human-machine
assemblage (Hoffman et al., 2018) this thesis is interested in.
Proponents see the benefit of recommender systems not only in their ability to make
macroscopic decisions on relevance but also in the microscopic selection of content for each
individual consumer (Hosanagar et al., 2014). When comparing the news feed of two individual
Facebook users they most likely are not the same. Rather, they differ according to what the
8curation algorithm of Facebook decided to be relevant on the individual level. This might seem
useful, but sceptics see a harmful narrowing of content that leaves individuals in their own little
content worlds mostly referred to as filter bubbles (Banker & Khetani, 2019; Carah
& Brodmerkel, 2019; Nguyen et al., 2014). In journalism, political polarization is often blamed
on algorithmic recommendation, but the scientific evidence on this topic is much more blurred
than public discourse might make it seem (Berman & Katona, 2020).
Arguably, filter bubbles occur when individuals are subjected to homogenous content (e.g.
single sided political agendas) through “algorithmic information filtering and results’
presentation in online services” (Zimmer et al., 2019, p. 42). Besides inbuilt errors or the
inability to debug flawed data (Noble, 2018), two factors seem to decide how strong algorithms
are involved in (de)constructing filter bubbles: 1) how is the user behaving towards the
algorithm? and 2) which kind of algorithm is used?.
Nguyen et al. (2014) found indications that individuals who followed the recommendations of
the algorithm were impacted by filter bubbles less likely. In contrary, users that refused
recommendations were captured in filter bubbles more likely. This was attributed to a natural
tendency of sticking to already known content without any non-human interference (Nguyen et
al., 2014). Further, Hosanagar et al. (2014) detected that recommender system increased the
scope of products consumers are buying, which is quite the opposite one would expect from a
filter bubble inducing infrastructure. Zimmer et al. (2019) argues that algorithms reproduce user
behavior that is lacking the necessary scrutinization which eventually leads to filter bubbles.
The following example clarifies this idea: From the perspective of a recommender system it
makes sense to label a fake news article relevant that is shared by a larger number of users.
Why should the algorithm behave differently as long as the currency of its processes is
relevance (Gillespie, 2014)? If the content is wrong, harmful or one-sided does not matter to
this specific metric of individual relevance.
The algorithm in this example is based on collaborative filter techniques, which generate
recommendations based on the purchase/consumption decisions of other users (just like
Amazon’s “other users have bought this”-section). Those algorithms seem to create filter
bubbles more often (Hosanagar et al., 2014). But again, the input data is not independent from
user behavior which makes any output a co-creation of the human-machine-assemblage
(Hoffman et al., 2018). Now, collaborative filters are not the only algorithms, in fact, there is a
whole bunch of different types (Latzer et al., 2014). Nevertheless, it will be sufficient for this
thesis to act as only two types would exist: collaborative filters and content filters. Content
9filters recommend data based on product/content attributes like color or genre. In doing so, they
seem to decrease the likelihood of recommending homogenous content/products (Hosanagar et
al., 2014).
Concluding, the mimetic reproduction of human non-human assemblage is the driver of
broadening or narrowing the scope of consumed content or products. Until now, there is no
clear consensus to which side algorithms are tending, stressing again the opacity of the
technology (Noble, 2018). Therefore, the possible outcomes found in literature are twofold: On
the one hand, recommender systems homogenize groups of individuals (Nguyen et al., 2014)
and reproduce societal bias (Noble, 2018) in the favor of mainstream ideals. On the other hand,
algorithms can heterogenize consumed content and purchased products (Hosanagar et al., 2014)
due to their proneness to user misbehavior (Zimmer et al., 2019).
Despite this ambiguity, it is a fact that algorithms are not only formed by humans, they also
form humans, their decisions and their lifestyles (Arielli, 2018). If those recommendation are
always for the good of individuals is at least questionable. Banker and Khetani (2019) speak
about an “overdependence” (Banker & Khetani, 2019, p. 500) that leads consumers to just
accept whatever the algorithm may propose. For this reason content creators are so obsessed
with understanding algorithms (Cotter, 2018), because pleasing the algorithm is necessary to
get recommended (Carah & Brodmerkel, 2019). Eventually, this creates downright
mystifications around algorithms like people have constructed around gods or other entities in
the past (Striphas, 2015). This exemplifies how powerful but also how opaque recommender
systems are. The main problem regarding such power was formulated by Wilson-Barnao
(2016): “One way of thinking about the impact of algorithmic recommendation is that it
happens in terms of those who own and operate the digital enclosure” (Wilson-Barnao, 2016,
p. 562). In this sense, not the users are the main profiteers of such technologies, the operators
are.
More data is collected than analyzed since the computational power and models to extract all
the potential information out of Big Data are still missing. Today, data is collected because it is
possible, not necessary because it is rational at the very moment (Fourcade & Healy, 2016).
Following this logic, new structures of powers emerged in today’s society (Beer, 2009) that
have implemented unknown processes of cultural construction while bearing the potential to
become even more powerful in the future. Those processes will be discussed in the following
chapter.
102.1.4. Algorithmic culture
Culture is “the attitudes, beliefs, and behaviors that, for a certain group, define their general
way of life and that they have taken over from others” (Heylighen & Chielens, 2009, p. 1).
Since it is partly algorithmically controlled what we see from others, algorithms haven an
influence on individuals (Arielli, 2018) and the culture they are embedded in (Striphas, 2015).
Now, skeptics might argue that algorithms are only present in the digital realm but consumed
online content has real-life consequences both in the psychological (Neighbors & Sobal, 2007)
and physical (Airoldi & Rokka, 2019) world. Therefore, algorithms are not only connected with
digital activities but with everyday life (Rieder, Matamoros-Fernández, & Coromina, 2018).
Users of social media sites continuously try to hack the algorithms (Cotter, 2018) and alter their
own behavior to get exposure. At this point, humans lose part of the control “since the world
starts to structure itself in the image of the capta and the code” (Kitchin & Dodge, 2011, p. 41)
and no longer vice versa (Beer, 2009). What is presented by the machine as meaningful is
suddenly thought to be a truthful representation of the actual reality (Striphas, 2015). Tech
companies like Google or Facebook become the “infrastructure that organize cultural life”
(Carah & Brodmerkel, 2019, p. 5) and therefore construct the cultural reality (Noble, 2018).
Power of reality construction has previously been executed by rather hegemonic sources like
monarchs, politicians or religious leaders. Nowadays, algorithms act as a new and additional
source of post-hegemonic, unconscious power (Lash, 2007) which is why Striphas (2015)
designated them as the “new apostles of culture” (Striphas, 2015, p. 407). But this form of
power is no longer apparently executed from above. It rather emerges from the inside, making
it hard to detect and nearly invisible. (Beer, 2009). Inside means, that information (e.g.
communication) is part of everyday life (Beer, 2009) and deeply connected with society’s
culture and habits (Noble, 2018). One example would be the smartphone which many people
always have on their body. Therefore, those algorithmic power structures intermingle most parts
of human existence, leaving basically no room to escape them (Lash, 2007). And even though
their actual processes are mostly unknown, consumers trust them due to their technical nature
(Noble, 2018). At the same time, the ongoing collection and analysis of data is thought to
“propel authoritarian practices” (Deibert, 2019, p. 31) that compare to much darker times of
history.
As already pointed out, algorithms cannot be blamed on their own for any harmful impact they
might have. Cultural transformation is much more complex and best described as an assemblage
11of human and non-human actors (Gillespie, 2014; Heylighen & Chielens, 2009). Nevertheless,
this does not implicate that both of those actors are equipollent, which is why todays culture is
not the result of a democratized negotiation between human and non-human actors (Striphas,
2015). Much more, algorithmic outputs and culture become indistinct, turning consumers into
pseudo-active bystanders (Hallinan & Striphas, 2015). Of course, people shape the output of
algorithms through their interaction with the output (Berman & Katona, 2020; Hosanagar et al.,
2014; Nguyen et al., 2014). But considering the arguments within this chapter the degree of
freedom of those decisions is at least questionable – or in other words: “the models analyze the
world and the world responds to the models” (Airoldi & Rokka, 2019, p. 30) which paints a
rather deterministic than free picture.
Now, there are several problems associated with the emergence of algorithmic culture. First,
the development of algorithmic technologies is happening fast, rendering every work out of
date right when published (Noble, 2018). This exacerbates scrutinizing all related processes
(Zimmer et al., 2019). Second, algorithms need to be trained to make predictions (Fourcade
& Healy, 2016). In turn, predictions require generalization because training data is always
specific for specific cases only. Pondering between specificity and generality bears a dilemma:
If the algorithms stay too close to the training data, no generalization is possible. If it diverges
too far, it generalizes excessively and incorporates societal bias in the process (Mackenzie,
2015). Due to the necessity of this assessment algorithms can be biased without any intent of
the developer. Nevertheless, Noble (2018) argues that such flaws are possibly intentional as
well as inextricable from the system itself, which was proven for all sorts of racial and sexism-
related issues. When giving so much power to a flawed machine, people risk their own
possibilities of shaping the world (Hallinan & Striphas, 2015), especially regarding important
cultural artifacts like the societal perception of aesthetics discussed in the next chapter (Arielli,
2018; Striphas, 2015).
2.2. Body culture
Over the last decades, the body became an increasingly important object of research in modern
sociology (Adelman & Ruggi, 2016). The understanding of the body shifted from a functional,
material approach to a socio-cultural one (Turner, 2008). This chapter describes the
construction of the socio-cultural body and relates it to contemporary body ideals and their
respective countermovement.
122.2.1. The socio-cultural body
Humans can experience their own bodies in two ways: “The body is simultaneously an object
that I can observe and a mode of being that makes that observation possible” (Turner, 2008,
p. 245). In other words, the body is the human itself but also its embodied tool to interact with
the world. Waskul and Vannini (2012) describe embodiment as “the process by which the
object-body is actively experienced, produced, sustained, and/or transformed as a subject-body”
(Waskul & Vannini, 2012, p. 3). This outlines the basic concept sociology has to deal with: the
dichotomous relationship of the natural and cultural realm and how the body connects those
sides of reality (Turner, 2008). The body is an assemblage of both sides, meaning that the nature
is influencing the culture and vice versa (Adelman & Ruggi, 2016).
The socio-cultural body is loaded with complex structures of meaning that revolve around
aesthetics but are certainly not limited to that (Waskul & Vannini, 2012). Those meanings are
expressed in a variety of “cultural beliefs, symbols and practices” (Turner, 2008, p. 55) that
differ for various cultures around the world (Chen, Yarnal, Chick, & Jablonski, 2018). For
example, removing the hair from women’s legs is a cultural practice (at least in the west) that
divides female bodies into good ones (hairless) and bad ones (with hair) (Braun, Tricklebank,
& Clarke, 2013) or into bodies that live up to the contemporary body ideals and those that fall
behind. Therefore, the body is always an interpretation of the society it is embedded in
(Adelman & Ruggi, 2016).
The construction of such ideals is complex and involves many actors. Previously, hegemons or
monarchs exerted this power (Beer, 2009), nowadays, machines play a vastly important role
(Airoldi & Rokka, 2019; Arielli, 2018). What has not changed is the fact that the power of
subordinate individuals is rather limited since the cultural construction of bodies occurs mostly
on an institutionalized level (Turner, 2008). But individuals are certainly involved in stabilizing
or destabilizing what would be considered the norm by performing all those cultural practices
(Long & Williams, 2020; Saguy & Ward, 2011). In doing so, they are also involved in
constructing what is not the norm (Turner, 2008).
It is a development of the latest decades that sociology is putting more emphasize on the body
(Turner, 2008). In part, this development can be explained by the increasing lack of external
factors in today’s society that display status: the social value of work, material possession,
religion and so forth is decreasing (Hancock, 2009). Especially the idea of possession is at
13change since accessibility is cherished over possession (Airoldi & Rokka, 2019). Such post-
modern (Hancock, 2009), personalized (Turner, 2008) and liquidized consumption patterns
destabilize existing structures of meaning (Airoldi & Rokka, 2019) pushing people to search
meaning in more internal factors. As observable today, the body proofs to be the factor of choice
in western societies (Hancock, 2009). With the evermore vanishing of physically demanding
work, bodies are not judged by their ability to perform in the workspace but by their aesthetic
performance (Hakim, 2016) and by the moral codes associated with them (Saguy & Ward,
2011). Looking back in history, this theory holds true for women since they were excluded from
most workspaces and therefore have been judged based on their bodies natural functions (e.g.
giving birth) and aesthetic potency all along (Adelman & Ruggi, 2016; Turner, 2008). Even
though the job market is gender mainstreamed for several years now, this reprehensive
reduction is consolidated in today’s western society by sexualizing women in the media (Sevic,
Ciprić, Buško, & Štulhofer, 2020). But also men are no longer predominantly judged on their
competences; physical and aesthetic components of male existence gained tremendous traction
(Hancock, 2009). The general rejection of classic masculinity in combination with the
decreasing importance of the masculine breadwinner created a void. This void gets filled with
an increased focus of society and men on the male body (Hakim, 2016).
It is incidental that the body is more important than ever as a cultural object and subject –
regardless of gender. While critical issues like self-objectification (Sevic et al., 2020), increased
self-surveillance (Hallsworth, Wade, & Tiggemann, 2005) and body shaming (Saguy & Ward,
2011) are strongly present today, the deconstruction of gender, race and body related issues
constitutes a strong countermovement. How this relates to the concept of body ideals will be
discussed in the following two chapters.
2.2.2. Body Ideals
Body ideals are a certain collection of attributes that define how an ideal body should look like
(Turner, 2008). Those attributes can and do change over time which is why in history many
different body ideals existed (Adelman & Ruggi, 2016; Hakim, 2016). But body ideals transport
more than mere codes about what is aesthetically pleasing and what not: the adherence to those
ideals is linked to moral values and personality traits (Saguy & Ward, 2011). For example,
overweight people are regarded as “stupid or ugly or lazy or selfish” (Saguy & Ward, 2011,
p. 66). This interconnection roots the body even deeper within the self-identity of people
(Hancock, 2009).
14The complex assemblage of interconnected meanings shows that body ideals, much like the
body itself, are constructed by society (Waskul & Vannini, 2012). While society imposes the
thin ideal on the female body today (Omori, Yamazaki, Aizawa, & Zoysa, 2017), more
corpulent women have been admired during the baroque times when overweight was associated
with wealth und health (Turner, 2008). Modern excesses of the thin idealizations are trends like
the thigh gap which is “the space some women have between their inner thighs when they stand
with their feet together” (Leboeuf, 2019, p. 2). A lack of thigh gap renders women too sturdy
in the eyes of this body ideal. It is obvious that a trend like the thigh gap is not about promoting
a healthy lifestyle, but about appearance, which is common for female body ideals (Simpson &
Mazzeo, 2017). Such ideals are not only dangerous, they are difficult to accomplish and for
some women out of reach due to the very anatomy of their own bodies. Considering those
points, body ideals are fluid concepts (Heylighen & Chielens, 2009) and sometimes go beyond
the natural restrictions of most bodies.
Mass media has the most important influence on today’s body ideals (Cohen et al., 2019; Fatt,
Fardouly, & Rapee, 2019; Sevic et al., 2020), but due to the decline of classic media, social
network sites like Facebook or Instagram arguably have become the strongest force in
negotiating body ideals (Leboeuf, 2019; Neighbors & Sobal, 2007; Stein, Krause, & Ohler,
2019). With the rise of powerful and widely available photo enhancing software, mostly
unattainable depictions of the human body (Hargreaves & Tiggemann, 2009) are presented by
both amateurs and professionals (Bakhshi, Shamma, & Gilbert, 2014; Hargreaves
& Tiggemann, 2009). Even though consumer understand that those visuals are altered in
unrealistic ways they feel pressured to conform (Talbot et al., 2018). In sociological terms,
people internalize (Leboeuf, 2019) such unrealistic ideals and start to judge others but also
themselves based on those. The more such content is consumed the stronger the internalization
and the stricter the judgement of others (Stein et al., 2019). But the degree of internalization is
also influencing the degree of self-objectification (Sevic et al., 2020) which describes the
process of a human viewing him- or herself through the eyes of a third person, and in doing so,
turning oneself into an object (Hallsworth et al., 2005). This results in a continuously inspection
of the body (Sevic et al., 2020) including all the associated psychological damages (Neighbors
& Sobal, 2007) like eating-disorders and others (Hallsworth et al., 2005). Such continuous
inspection is defined as self-surveillance (Sevic et al., 2020).
The next chapter sheds light on the actual body ideals that are present today. Further, the
respective countermovement is described and put into the relation of gender and race.
152.2.3. Mainstream vs. Anti-Mainstream Body Ideals
Since this thesis investigates the (de)stabilization of mainstream and anti-mainstream body
ideals, it is necessary to define both sides. To make this chapter as comprehensive as possible,
mainstream body ideals are discussed first, starting with the female ideal and followed by the
male ideal. Subsequently, the different facets of anti-mainstream movements and ideals are
explained.
As already mentioned, the female body ideal revolves strongly around being thin (Turner,
2008). This idea proofs to be very robust over time. On the other, related trends like the thigh
gap are extremely volatile and appear mostly in an narcissistic attempt to generate attention on
social media (Leboeuf, 2019). Within this thesis, those trends are not part of the body ideal.
Rather, they are viewed as outgrowths of the thin idealization culture that is much more stable
and strongly anchored in society. While being thin was sufficient for some time, the
contemporary body ideal of women additionally incorporates being muscular (Bozsik,
Whisenhunt, Hudson, Bennett, & Lundgren, 2018). Being thin and feminine while
simultaneously being muscular is borderline contradicting, rendering this body ideal hard to
reach (Simpson & Mazzeo, 2017). It combines the necessity of exhausting exercise with a
strong regulation of calory intake (Bozsik et al., 2018; Simpson & Mazzeo, 2017). Having said
this, being thin and feminine without being muscular is still present as a body ideal on its own
(Diedrichs & Lee, 2011) creating two distinct but intermingled mainstream ideals. Since the
female body ideal is defined rather narrow (Holmqvist & Frisén, 2012) also race and skin color
are part of the body ideal. Looking at social media content, the white but tanned women with
clear skin is predominantly present in western society (Chen et al., 2018). Further, body hair is
viewed as unhygienic and unpleasant on a female body (Braun et al., 2013). Concluding, the
ideal western women is thin but toned and feminine, white but tanned and hairless no matter
which part of her body.
Now, women are impacted by stricter and more homogenous body ideals than men (Diedrichs
& Lee, 2011) since their bodies have been in the focus of judgement for a longer period of time
(Fatt et al., 2019). While this claim still holds true, male body ideals are increasingly straiten in
contemporary society. Especially the idea of being muscular is omnipresent (Hallsworth et al.,
2005). But the reasoning behind muscularity differs between gender. Being muscular as a
women centers around emitting a pleasant appearance (Simpson & Mazzeo, 2017). Men, on the
other hand, are less concerned about appearance (even though it is vastly important). The male
16muscular body ideal tends towards extruding high strength and athletic performativity (Burlew
& Shurts, 2013). More recent research indicates that this nuance could shift to a more
appearance related focus, since an increasing number of men engaged in fitness show enhanced
self-surveillance behavior regarding their aesthetics (Fatt et al., 2019). Considering that men
lost part of their patriarchal power due to the general gender mainstreaming over the last
decades, it is compelling that men are filling this power void with a new focus on emitting
bodily strength (as a new form of power) (Hallsworth et al., 2005). While muscularity is a factor
man can control to some degree, other relevant genetic factors are out of their hands. The ideal
man is tall and has a clear jawline as a display of masculinity (Burlew & Shurts, 2013) which
is basically not changeable and therefore unattainable for a certain number of men.
Interestingly, while being hairless is viewed as feminine in women, an increasing number of
men is practicing hair removal (Braun et al., 2013).
The mainstream body ideal provides one common ground for both sexes: the dichotomy of
gender. This means that gender and acceptable appearances are defined by one’s sex (Luna &
Barros, 2019). For example, a man is not ‘allowed’ to wear a dress or a woman with a shaved
head is regard as non-feminine. Through insisting on the binary gender worldview, the
mainstream body ideal neglects the constructivist nature of gender that is well accepted in
sociology (Adelman & Ruggi, 2016).
Anti-mainstream movements try to dismantle the strict division between the sexes by
deconstructing gender (Adelman & Ruggi, 2016) and basically all other constructs that legislate
how a pleasant body should look like (Hakim, 2016; Hancock, 2009; Leboeuf, 2019). Since
anti-mainstream movements like body positivity include every human body (Afful &
Ricciardelli, 2015) it is difficult to define those movements by defining their body ideals. Even
though the mainstream body is included with this definition, anti-mainstream is understood as
an assemblage of resistance that predominantly brings marginalized lifestyles, body shapes and
appearances to the public. (Adelman & Ruggi, 2016). Within this thesis, anti-mainstream is
therefore defined in the light of resistance to the mainstream. This includes every human that is
not part of the norm or the body ideals described above: transgender models, queer men,
disabled people, black women, men who wear make-up, overweight humans and so on. In short
terms: the anti-mainstream subsumes the body self-presentations of the various minorities
active on Instagram.
172.3. Instagram
Visuals are ranked among the most important means of digital communication (Bakhshi et al.,
2014). Being one of the key players in this game, Instagram essentially does not need any
further introduction. Nevertheless, this chapter explains some of the main aspects and
functionalities of the social network site und links it to digital visual rhetoric.
2.3.1. What is Instagram?
Instagram is part of the so-called Social Media landscape which is defined as “a group of
Internet-based applications that build on the ideological and technological foundations of Web
2.0, and that allow the creation and exchange of User Generated Content” (Kaplan & Haenlein,
2010, p. 61). The over one billion users on Instagram (Instagram Press, 2020) create an
immense richness in content, turning Instagram into a fruitful research environment as proved
by previous studies (Abidin, 2016; Cohen et al., 2019; Cotter, 2018; Kim & Kim, 2019;
Manovich, 2017; Schöps et al., 2019; Stein et al., 2019).
In contrary to classic media like television, the consumer is simultaneously consuming,
producing and participating in content on Social Media (Zimmer et al., 2019) mostly by using
their Smartphone (Carah & Shaul, 2016). Further, consumer build their own profiles to connect
and communicate with others (Sevic et al., 2020). With respect to Instagram, this behavior is
expressed via uploading images and videos with captions or posting stories as well as liking,
saving and commenting posts of other users and following them. In contrary to other social
networking sites like Facebook, user can follow other users of Instagram without their
permission (as long as their privacy settings allow it). Further, users do not become followers
of their follower automatically. In this sense, Instagram is a asymmetric network (Hu,
Manikonda, & Kambhampati, 2014) that allows for large one-sided followings . Or as Araújo,
Corrêa, Silva, Prates, and Meira Jr (2014) are calling it: “the rich get richer phenomenon”
(Araújo et al., 2014, p. 5), meaning that users with large followings grow more easily and faster
which provides an advantage to those mainstream personas connected to “large, high-profile
corporations with strong name brands” (Scaraboto & Fischer, 2013, p. 1235).
Another important function of Instagram is the hashtag, which enables users to link their visuals
to a set of related images (e.g. tagging an image of a car with #car), making it accessible and
traceable for a potential audience. Looking from the other perspective, hashtags allow image
searching users to make sense of the huge, never ending stream of visuals on Instagram
18You can also read
