=Paper=
{{Paper
|id=Vol-3878/53_main_long
|storemode=property
|title=Divergent Discourses: A Comparative Examination of Blackout Tuesday and \#BlackLivesMatter on Instagram
|pdfUrl=https://ceur-ws.org/Vol-3878/53_main_long.pdf
|volume=Vol-3878
|authors=Aenne Knierim,Michael Achmann-Denkler,Ulrich Heid,Christian Wolff
|dblpUrl=https://dblp.org/rec/conf/clic-it/KnierimAH024
}}
==Divergent Discourses: A Comparative Examination of Blackout Tuesday and \#BlackLivesMatter on Instagram==
https://ceur-ws.org/Vol-3878/53_main_long.pdf
Divergent Discourses: A Comparative Examination of
Blackout Tuesday and #BlackLivesMatter on Instagram
Knierim, Aenne1,*,† , Michael Achmann2,**,‡ , Ulrich Heid1,*,† and Christian Wolff2,*,†
1
Universität Hildesheim, Universitätsplatz 1, 31134 Hildesheim
2
Universität Regensburg, Universitätsstraße 31, 93035 Regensburg
Abstract
On May 25th, 2020, a viral eleven-minute clip showing the murder of George Floyd sparked international outrage and
solidarity, leading to the digital memorial event Blackout Tuesday on Instagram. We analyzed posts to compare Blackout
Tuesday discourse with #blacklivesmatter movement conversations. Using topic modeling, we identified dominant themes and
counter-narratives in Blackout Tuesday and #blacklivesmatter captions. Using hashtag co-occurrence analysis, we investigate
hashtag networks to situate the discourses within spheres of Instagram activism. Our findings indicate that both corpora
share themes like ”calls to action”, but Blackout Tuesday posts are shorter and solidarity-focused, while #blacklivesmatter
posts are longer and address white privilege more explicitly. #blacklivesmatter is linked to anti-racist activism hashtags, while
Blackout Tuesday connects more with popular culture and #Alllivesmatter. This supports qualitative research on Blackout
Tuesday’s performative allyship, adding a quantitative perspective to existing research.
Keywords
Blackout Tuesday, #blacklivesmatter, Instagram, Cultural Analytics
1. Introduction paid to BT to date. Most existing research on BT has
been conducted using interview studies and hermeneutic
The number of posts from the #blacklivesmatter move- methodologies. In addition, there is one quantitative
ment (#blm) is estimated to be 28 million, which exempli- study by Chang et al. [7] which examines the contents
fies the movement’s impact on society [1, 2]. However, of images, focusing on visual and geographic analyses.
the popularity of the movement reached a peak when Because a large share of the posts related to BT feature
“Blackout Tuesday” (BT) took place, a digital memorial a black tile rather than an image, it is also important to
day on Instagram [1, 2]. BT was caused by a wave of examine the text in the post, the caption.
outrage about the murder of George Floyd, an African To fill this gap, this study used text mining to investi-
American who was killed on May 25, 2020 by two white gate the content of posts on BT. We examined the interre-
police officers in Minneapolis. The outrage was sparked lations between BT and #blm by applying topic modeling
by an 11-minute clip of the murder which went viral in and hashtag co-occurrence analysis to both discourses
social media. The video was posted in the context of the within the same period of time. Our aim was to under-
#blm movement and in a cultural setting where African stand how BT has impacted the #blm discourse. We also
Americans perceived law enforcement as agents of bru- wanted to compare the different types of discourse, as BT
tality [3]. To postulate solidarity with African Americans is a digital memorial day, whereas #blm is a new social
in their fight for racial justice, social media users posted movement.
a black square and added a post caption with the hash-
tag #blackouttuesday. Given the cultural context, the
video supported the perception of white police brutality, 2. BT and #blm on Instagram
white supremacy, and systemic injustice against African
Americans. While #blm needed years to gain a large #blm is a movement started by the three African Amer-
international audience, BT reached millions within a day. ican women Alicia Garza, Patrisse Cullors, and Opal
The #blm movement has received significant attention Tomet. It is described as: ”an ideological and political in-
in research and has had a strong impact on discussions tervention in a world where Black lives are systematically
in mainstream media; however, little attention has been and intentionally targeted for demise. It is an affirmation
of Black folks’ contributions to this society, our human-
ity, and our resilience in the face of deadly oppression”
CLiC-it 2024: Tenth Italian Conference on Computational Linguistics,
[4]. The number of posts from the #blm movement is
Dec 04 — 06, 2024, Pisa, Italy
$ knierim@uni-hildesheim.de (K. Aenne); estimated to be 28 million, which exemplifies the move-
michael.achmann@informatik.uni-regensburg.de (M. Achmann); ment’s impact on society [1, 2]. #blm is a new-new social
heidul@uni-hildesheim.de (U. Heid); christian.wolff@ur.de movement: hierarchical, participatory and decentralized,
(C. Wolff) deeply mediated, accommodating both online and offline
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
repertoires, combining connective with collective action 4. Digital memorial day versus new social
[5, 6]. In the summer of 2020, 20 million people joined movement: BT and #BlackLivesMatter consti-
protests to address racial injustice and demand account- tute different forms of political participation in
ability for the murder of countless Black people at the social media. Is this visible in the posts, topical
hands of law enforcement [7, 8]. clusters and networks of hashtags? If so, can we
BT was part of the campaign #TheShowMustBePaused, draw conclusions about how discussions are led
spreading from the music industry to social media [9, 10]. differently depending on the type of participation,
It was started by Jamila Thomas and Brianna Agyemang, in this case a memorial day versus a new social
both female music executives at major record companies movement?
in the U.S. [10]. The campaign was opened in response to
the murders of George Floyd, Breonna Taylor, Ahmaud
Arbery and other Black citizens at the hands of police 3. Data Collection and Description
[11]. Thomas and Agyemang encouraged music industry
Building on the framework of Cultural Analytics, we base
professionals to halt business operations for the duration
our analysis on cultural sampling [16]. Because hashtags
of June 2, 2020 to prompt conversation about financial em-
structure online discourse [12], we used the hashtags
powerment of Blacks in the music industry [10, 11]. The
#blackouttuesday and #blm to create our cultural sample.
campaign involved posting a black square on Instagram,
Manovich highlights that current humanities research
while refraining other social media activity that day [10].
follows ”Close Reading” [17] as the dominant paradigm
Soon, the hashtag morphed and circulated, with posts
of textual analysis which puts single artifacts by profes-
being different than intended [9, 10]: the hashtag was
sional authors at the center of research [16, 18]. Using
used to postulate solidarity with George Floyd, instead of
cultural samples allows to investigate nonprofessional
encouraging financial empowerment of Blacks in the mu-
vernacular created by ”regular” people [16]. We add a
sic industry. This also impacted the #blm conversation
distant reading perspective to existing research on racial
on Instagram.
justice movements by extracting a cultural sample of
Often, users posted the hashtag #blm in #blackouttues-
#blm and BT from Instagram posts.
day posts. Hashtags allow users to find specific infor-
We collected our corpus retrospectively using Crowd-
mation or to monitor a situation because they serve to
Tangle1 . We used the two search terms #blacklivesmat-
structure discourse centered around a specific topic in
ter and #blackouttuesday to find public Instagram posts
social media platforms like Instagram [12, 13]. In table 2,
published within a three month period following the
we show that nearly 10% of #blackouttuesday posts were
death of George Floyd. Our corpus comprises posts from
tagged with the hashtag #blm. Using #blm in BT posts
05/24/2020 11:59 pm to 08/24/2020 12:00 am. We ex-
concealed #blm posts with social justice information, hin-
ported the data in two batches, as CrowdTangle’s search
dering critical collective organization [10, 13]. Therefore,
limits exports to a maximum of 300,000 posts. Using
#blm called not to use the hashtag along with BT posts.
this method, we collected 548,249 posts for #blm, and
Next to concealing #blm posts, BT critics called it “white
305,344 posts for #blackouttuesday. The CrowdTangle
guilt day” [14]. Posts were considered as empty gestures
dataframes contain posts as a link to one image per post
[15, 10]. This accusation was not made in the context of
and the ”description” column that contains the caption
#blm.
of the post. In addition, the exported data includes meta-
With a bird’s eye view on hashtag relations and topics,
data for each post: The timestamp when the post was
we hope to gain insight into differences between BT posts
published, the username and name of the creator, and
and #blm. Thus, we investigated the following research
interaction metrics for likes and comments. Our analysis
questions:
concentrates on captions, the textual part of Instagram
1. Topic prevalence and significance: What are posts. Additionally, we use the timestamps for a descrip-
the dominant themes and topics in the #black- tive analysis of trends within our corpus.
outtuesday and #blm Instagram captions? Com- We collected the top hashtags and sorted them by us-
paring the two, what are the core concerns? age in Table 2. Expectedly, #blm posts referenced BT, with
2. Counter speech: Which counter narratives ap- #georgefloyd, #blackouttuesday and #icantbreathe in the
pear? top twenty hashtags. Unlike #blackouttuesday, #blm con-
3. Hashtag co-occurences Which hashtags co- tains no references to the original campaign #theshow-
occurring in the #blackouttuesday and #blm feeds mustbepaused in the top hashtags. #blackouttuesday also
are most prevalent, and do their interconnections includes #alllivesmatter, a hashtag signaling counterdis-
form distinct clusters? What do these hashtag course. Breonna Taylor, another victim of police brutality,
networks suggest about the broader contexts and is referenced in the #blm corpus.
intersections within the sphere of black activism? 1
crowdtangle.com
Table 1
Comparison of BT and #blm statistics
BT blacklivesmatter
Type-token density incl. hashtags 0.96 0.88
Type-token density excl. hashtags 0.56 0.79
Average post length in tokens 19 69
Average post length in tokens excl. hashtags 16 60
Average sentence length in tokens 7 10
Average sentence length in tokens excl. hashtags 6 8
Table 2
Frequencies and percentages of hashtags for BT and #blm
Hashtags Frequencies Percent Hashtags Frequencies Percent
#blackouttuesday 305159 100% #blacklivesmatter 555992 100%
#blacklivesmatter 29849 9.78% #blm 98124 17.65%
#theshowmustbepaused 15954 5.22% #georgefloyd 73198 13.17%
#blackoutday2020 7745 2.53% #justiceforgeorgefloyd 46370 8.34%
#georgefloyd 6742 2.21% #blackouttuesday 25621 4.61%
#justiceforgeorgefloyd 6178 2.02% #love 24172 4.35%
#TheShowMustBePaused 4667 1.52% #nojusticenopeace 22030 3.96%
#blm 4557 1.49% #protest 20363 3.66%
#love 3664 1.20% #icantbreathe 17774 3.20%
#BlackLivesMatter 3003 0.98% #racism 17189 3.09%
#vidrasnegasimportam 2725 0.89% #justice 15364 2.76%
#stopracism 2665 0.87% #breonnataylor 14953 2.69%
#icantbreathe 2523 0.82% #blackgirlmagic 14821 2.67%
#blackout 2382 0.78% #justiceforbreonnataylor 14768 2.66%
The type-token density is a measure for language com- 4. Hashtag Co-Occurences
plexity. It allows insight into the complexity of both
discourses, which we present in Table 1. Expectedly, Omena introduces hashtags as natively digital objects
type-token density is high in both discourses when mea- that enable users to join debates on the local and global
sured including hashtags. We attribute this to special scale through their indexing function [19]. Following
characteristics of social media language, such as frequent Roger’s digital methods approach [20], we use these hash-
use of hashtags, non-standardized spelling and the use of tags as digital traces [21] to study the #blm movement in
emojis. While BT has a higher type-token density than the light of BT. Co-occurrence analysis allows to extract
#blm when hashtags are included (0,96 vs 0,88), we can a network of hashtags, which gives insight into the move-
see that #blm has a higher type-token density when hash- ments’ relations to other activist discourses indexed by
tags are excluded (0.79 vs 0.56 without hashtags). This hashtags.
shows that more different hashtags are used in #black- For the co-occurence analysis, we preprocessed both
outtuesday posts than in #blm posts. At the same time, corpora in the same way. We extracted the hashtags from
the text without hashtags is less lexically diverse than each caption using regex, lowercased the hashtags and
in #blm posts. The type-token density in #blackouttues- counted the occurences of each hashtag. We selected
day is only slighty above average (0.56), while #blm has the top 1,000 hashtags for each corpus and created a
a type-token ratio that is quite high (0.79). #blm posts co-occurrence network, counting the co-occurence for
are also longer than #blackouttuesday posts. On aver- each hashtag pair. Each network was imported to Gephi,
age, #blm post lengthis three times longer than that of where we used the modularity algorithm [22] to find
#blackouttuesday posts (69 tokens incl. hashtags to 19 hashtag clusters [23]. In a last step, we plotted the net-
tokens). This ratio remains consistent when substracting work for each modularity class within each of the two
hashtags (60 vs 16 tokens). networks. These plots were the basis for our qualita-
tive exploration of the hashtag clusters. Through this
exploration, we were able to name each cluster based
on the hashtags they contain. We extracted the modu-
larity classes associated with each hashtag to conduct 6. Results
a quantitative assessment of the hashtag clusters. We
excluded the search hashtag from each network during 6.1. Core concerns and narratives
this mapping process to mitigate potential biases (#black-
We visualize the most frequent themes occuring in the
outtuesday for the one, #blm for the other). Each post
datasets in figure 1 and figure 2, with the bubble size
was then assigned to a specific class based on a majority
representing the relative frequency within the 100 most
rule approach which considered the hashtags present in
frequent topics. The colors for shared topics in both
the post. We labeled cases as ’ambigious’ where a clear
discourses correspond. The datasets share many common
majority for a particular modularity class was not evident.
themes. We identify that both datasets contain many
The hashtag clusters are saved in a digital repository.2
”calls to action”. Apparently, many posts aim to activate
readers politically, for example by joining protests or
5. Topic Modeling signing petitions. Other calls to action are more generic,
manifesting in topic words like fight or change. Other
Topic modeling is a method to cluster themes in large posts ask readers to become conscious of racism and
corpora that is widely applied in the digital humanities. white privilege. In the #blm dataset, 30% of posts are
Typical for social media data, our posts are quite short, ”calls to action”, while only 13 % of the BT posts fall into
with post length ranging from 𝑥 ¯ = 19 token for BT posts this category.
to 𝑥
¯ = 69 token for #blm posts. Therefore, we chose to We identified the theme ”voice-of-color” in the top-
employ BERTopic [24] for the topic modeling due to its ics. The voice-of-color is an established thesis from crit-
ability to handle sparse data. ical race theory. It holds that alleged minority status
We applied only minimal preprocessing. We removed brings with it a presumed competence to speak about
@mentions for privacy protection and deleted 29 post race and racism [28]. The speech act of ”breaking the
duplications that are a result of the scraping process. silence” appears in both corpora. It is more present in the
Next to this, we removed words with two or less letters, #blm dataset (for a comparison, see figure 1 and figure 2).
stopwords and the hashtags. Within the BT dataset, this becomes visible with topics
After preprocessing and topic modeling, we assigned that include words such as voice, heard, voices, use, space.
labels to the 100 most frequent topics of each dataset Within the #blm dataset, this becomes even more clear,
(postprocessing). For a human eye, it becomes clear that with topics such as Silent, silence, quiet, staying, silenced,
many topics follow broader themes. Therefore, we add an Voice, voices, heard, amplify, use.
additional step by identifying broader themes consisting Both corpora share themes, but we identify two big
of similar topics after the postprocessing. differences. Common themes are mentions of other anti-
Firstly, we apply the baseline application of BERTopic racist movements, references to African American artists
[24], using UMAP for dimensionality reduction and the and musicians, or references to platform affordances. A
HBDSCAN minimal cluster size to 30 [25, 26]. This re- difference lies in internationality. 21% of BT topics are
duced the amount of topics drastically, which is why we written in other languages than English, such as Spanish,
set the minimal cluster size to 150 items. Although the German, French or Russian which points to the interna-
baseline application of BERTopic [24] yields good results tional character of BT (considering the 100 most frequent
in terms of readibility and topic diversity, we conducted topics). In contrast, #blm is rooted in the English speak-
an experimental study for finetuning on samples of both ing countries U.S., Canada, and Australia [29]. Another
datasets to increase topic diversity (n=0,1%). Maximal difference between the corpora is the perspective of soli-
Marginal Relevance considers the similarity of tokens darity which is prevalent in BT posts. 7% of topics relate
with the document, along with the similarity of already to solidarity, using hashtags like #icare or #togetherfor-
selected keywords and keyphrase [27, 24]. We found that change. In contrast, the #blm dataset thematizes equality
topic words consisted of two words instead of one in and privilege, calling out white privilege (3% of topics).
many cases, but would sometimes contain the same word While solidarity expresses the perspective of an outsider,
twice. The application of MMR did not increase topic the corresponding #blm theme expresses a deeper under-
diversity. We found that the right preprocessing is more standing of racism and systems of oppression.
important to obtain high topic diversity.
6.2. Connection with other spheres of
Black activism
The modularity algorithm discovered five communities
within the hashtag co-occurences for #blm, and six for
2 BT. In case of BT, hashtags were split unevenly between
https://osf.io/cu2bj/?view_only=bc770f9539c64682a0bd477d5bd6bb99
the classes: Classes 2–4 contained between 3.6% (𝑛=33, related to off-topic hashtags. For example, two hash-
class 3) and 5.8% (𝑛=53, class 4) of all hashtags, while tag networks contain mundane content related to the
the classes 0, 1, and 5 contained between 16.6% (𝑛=152, food blogging and wildlife, such as #animalsofinsta, #ani-
class 0) and 35.4% (𝑛=325, class 1) of all hashtags. The mallover or #wildlifeonearth. Another network revolves
smallest cluster contains hashtags referring to food and purely around U.S. sports men and basketball. Unlike
animals. Class 4 contains references to sport and class the comparable co-occurence network at #blm, these net-
5 the #blackgirlmagic and #blackbusiness theme. The works do not contain any other hashtags from African-
classes 0, 1, and 5 contain political hashtags, multilin- American communities except for #blm and #blackexcel-
gual hashtags, and content-related hashtags (like #por- lence within the animal topics. This shows the main-
trait). The hashtag #blackouttuesday appears in class 0, stream character of BT compared to #blm. This is sup-
the multi-lingual class, possibly as the unique bridge to ported by the wide political spectrum visible in solidar-
the other clusters. When mapping posts to modularity ity hashtags, referencing conservative and republican
classes based on the top 1000 hashtags, 71.6% (𝑛=56783) hashtags alongside hashtags on the political left such as
of all posts were identified to belong to class 1, the clus- #socialist or contents related to the democratic party.
ter that contains most hashtags related to the movement, A shared topic are support networks for black busi-
like #theshowmustbepaused and #justiceforgeorgefloyd. nesses and empowerment content of black women, re-
10.6% of posts (𝑛=8371) were mapped to the multilin- lated to hashtags such as #BlackGirlMagic, #BlackEm-
gual cluster (0), 8.21% (𝑛=6507) to cluster 5, and 6.28% powerment or #melaninpoppin. Scholars have estab-
(𝑛=4975) of posts were classified as ambiguous, as they lished that empowerment occurs within the realm of
did not show a clear majority for the one or the other social media. For example, #BlackGirlMagic, introduced
cluster. A minority of posts was mapped to classes 2–4. by CaShawn Thompson, negotiates societal presentations
The hastags co-occuring in the #blm network are more of Black women [30]. Black women-centered discourse
evenly split into four clusters: from 37.3% (𝑛=373) in class achieves empowerment by highlighting their experiences
3 to 13.7% (𝑛=137) in class 2. Hashtags contained in the in ways that are often neglected or distorted in traditional
largest cluster (3) are mostly non-political and mundane media outlets [31]. Within the #blm corpus, these hash-
(#family, #food, #college, #followme). The smallest class tags are closely connected to mental health content. In
(2) contains #black+x hashtags, like #blackqueen, #black- the BT context, hashtags are connected to support hash-
news, #blackbloggers. Class 0 (24.4%, 𝑛=244) contains tags for Black businesses.
hashtags revolving around justice in combination with
different topics, as well as allyship hashtags (e.g. #istand- 6.3. Counter Speech
withyou), while class 1 (24.6%, 𝑛=24.6) clusters hashtags
related to politics and policy issues (e.g #notmypresident, Another hashtag shared by both #blm and BT is the
#guncontrol). The classification based on the hashtag colorblind hashtag #alllivesmatter. #Alllivesmatter is a
mapping for posts shows a more even distribution for the counter-protest hashtag whose content argues that equal
#blm hashtag which is congruent with the hashtag distri- attention should be given to all lives regardless of race
bution across clusters. Most posts were mapped to cluster [32]. The ”All Lives Matter” movement is, ”one of the pri-
1 (31.1%, 𝑛=127410), with the ambiguous classification mary ways in which people resisted the #blm movement
coming second (19.2%, 𝑛 =78706). 17.5% (𝑛 =71907) [...] in the form of [...] a counterslogan to undermine
posts were mapped to the mundane class 3, and 17.3% the purpose and message of the #blm call to action” [33].
(𝑛 =70947) to class 0. Finally, the smallest amount of Powell et al. have shown that the use of #blm or #All-
posts (15.0%, 𝑛 =61308) were classified to cluster 2. LivesMatter are signals of political identity [34].
In general, the #blm co-occurence network shows that
the social movement is closely related to other hashtags 6.4. Political Hashtags
of Black activism, while also containing links to popular
culture that are common to Instagram, such as art or Several political hashtags appear close to #blm, like
photography. In the aftermath of George Floyd’s death, #berniesanders, #NeverTrump, #NeverBiden and #Pro-
the hashtags #justiceforgeorgefloyd, #protest, #policebru- gressives. A number of city hashtags are close to these
tality, #justiceforbreonnataylor and the German hashtag hashtags, namely #LosAngeles, #Hollywood, #Brooklyn,
#gegenrassismus (”against racism”), are closest to dif- #Atlanta, and #Chicago. A study analyzing Twitter pro-
ferent spellings of #blm. An example of the hashtag’s file information found that the #blm movement is largely
relatedness to other movements of Black activism are ignored in places with a large percentage of white or
empowerment hashtags, such as #blackexellencexx, #un- Hispanic populations, compared to places with smaller
apologeticallyBlack and #BlackGirlMagic. percentages of these groups [35]. Published in 2019, the
While BT co-occurence networks contain hashtags study was conducted before BT which gave the #blm
from a wider political spectrum than #blm, they are also movement a new spark both in the U.S. and internation-
ally. A geospatial analysis could provide insight into intersectional discrimination of African Americans and
whether this finding remains true after BT and if it is carry unconscious biases that are potentially harmful.
true for both #blm and BT posts.
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7. Discussion: Digital memorial
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A. Appendix
Figure 1: Distribution of BT topics sorted after themes.
Figure 2: Distribution of #blm topics sorted after themes.