VISUALISING HASHTAG ENGAGEMENT: IMAGERY OF POLITICAL POLARIZATION ON INSTAGRAM

Group Members

Project leaders: Janna Joceli, Elaine Rabello, André Mintz. Facilitator: Natalia Sanchez-Querubin. Team members: Suay Ozkula, Gabriela Sued, Ece Elbeyi, and Alessandra Cicali.

Contents

Summary of Key Findings

Hashtag engagement research is proposed through four main perspectives (media item, user, visual and grammars of hashtag), drawing the rise of political polarization in Brazil as a case study (pro-impeachment and anti-coup protests in March 2016). After exploring three perspectives, our general key findings: 1) the awareness of Instagram as a platform to ground studies on imagery of polarised political engagement. 2) That there is substantial difference between the panorama of an issue if we observe the most liked/commented/shared media items and the panorama that can be observed in the less popular ones. Thus, it is insufficient to focus the analysis on the ‘top’ items and users.

In what regards key findings from each perspective to visualise hashtag engagement:

i) Media item perspective:

The visual culture of the dominant voices presented similar composition, structures and adoption of visual elements in both protests, for instance, composed by public figures (pro-impeachment: actors or actresses, TV presenters, stand-up comedians, businesswoman; anti-coup: politicians, artists, LGBT and feminists activists, Independent Media) with few publications but accounting the most engaged media items, and the organisers of the protests or activists and non-official campaign accounts, these actors will probably not appear in the top ten most engaged lists, they have more publications sustaining influence and visibility along the day of the protests. It was curious to note the presence of the account petscharm in the dominant voice list of pro-impeachment protests; this profile depicted different breed of dogs wearing the official uniform of Brazil’s football team or Brazilian flag in all publications. However, and despite common characteristics, a close look at dominant voices revealed also the predominance of visual elements: green and yellow, selfies, Brazil’s football garment, and dogs for pro-impeachment; and red, political slogans, crowds, strong critique on Globo television. And lastly, along the anti-coup protests brazilians were more engaged with publications made by public figures, whereas, in pro-impeachment protests, publications made by the organisers of the protest and activists or non-official campaign accounts drew more attention from protesters (see table below).

ii) User perspective:

The text extraction of the captions of dominant and ordinary voices and its visualisation by clusters of tags, terms and political position show that there are a lot of common discourse among the groups. But looking further, we could see that dominant voices scored more in specificity and frequency when the post was about the locations of the protest. This suggests that dominance itself (the capacity of attract and maintain the attention/engagement) may not necessarily be related to the caption or hashtag content, but to pragmatic information with regards to protest points and locations, or their organising power. When it comes to comparing anti-coup (left-wing) and pro-impeachment (right-wing) user perspectives, all the analysis showed the pro-impeachment groups larger and well-connected, although they were fewer. They also included the protest-location group and, therefore suggesting again that the dominant voices in the pro-impeachment group were more connected and (through the connections and the protest-locations) more tightly and therefore potentially better organised. Among the ordinary voices, we could see more connections between the two main political groups (pro-impeachment and anti-coup). The mutual space seems to be linked to the aim of gathering and engaging people to a cause, stressing the collective dimension and setting a common horizon and agenda.

iii) Visual perspective:

Analysis of the visual content of pictures related to each protest (pro-impeachment and anti-coup) revealed a common general structure of the images of each protest. Three broad categories emerged of the bipartite image-label network, making it largely a triangular network with vertices related to: open-shots of the crowd, selfies and close-up portraits, and graphics (invitations to the protest, banners etc.). Also, both protests had a smaller label cluster related to ‘foods’, specifically, related to two types of food which are pejoratively attributed to each side of the dispute. As differences between protests, apart from labels related to the colors worn by partisans of each group, there were sub-clusters which reveal the manifestation of some stereotypes of each group. In the anti-coup group there is a subcluster in the portrait/selfie cluster related to beard and facial hair. In the pro-impeachment, we found a subcluster related to sunglasses and a small autonomous cluster related to dogs (most of which depicted wearing the yellow uniform of Brazilian football team). While many questions related to this perspective were left untouched due to operational issues in the research process, the research showed a potential for automatic annotation in revealing broad descriptions of a visual dataset. Some aspect to later pursue include the question of variability of these descriptions related to particular hashtags and between dominant and ordinary voices.


(see final presentation)

Introduction

Engagement is a key parameter in social media studies: a conductor for scientific analysis and thoughts. The overall engagement is not only a representative form (or depiction) of human activities, but also a common path to think political and social issues. However, engagement can stem from and be fostered by algorithms or bots, advertising, the popularity of actors or subjects, local or global context. On social media, engagement gathers the sum of different grammars of actions (Agre, 1994) or the reoccurrence of isolated actions, which, taken together, may represent collective thought. In other words, engagement is typically perceived through a dual logic: the sums of actions media items receive (e.g. the total number of likes and comments in a picture on Instagram); the recurrent use of natively digital objects or grammars of action from many people about a topic, e.g. the adoption of hashtags (that can be driven by personal, isolated or collective acts of communication). The first returns the most engaged list what can be defined as the dominant voices, the second returns the ordinary list that is composed by the ordinary voices.

Studies based on engagement have been commonly undertaken by vanity metrics instead of critical analytics; the former being comprised of measures of analysis based on a content or actor being well-known or influential, whereas, the latter, proposes metrics of engagement (dominant voice, concern, commitment, positioning and alignment) that focus on causes and issues overtime (Rogers, 2016). That is why we should not oversimplify engagement behind “the most engaged lists or active users”. On the contrary, we should investigate and analyze the domains of engagement activity; logic, structure and the vocabulary of actions together with an understanding of the social relations. Thus,instead of looking only at most popular actors/content or total of reactions on posts, how can we study engagement through the constant repetition of ordinary voice publications?

Four perspectives to visualise hashtag engagement on Instagram

We are proposing, to this project, the adoption of four perspectives through which we can discuss and visualise the engagement by hashtags, paying special attention to the most engaged list (dominant voice) and the ordinary list (ordinary voice):

Four perspectives to visualise hashtag engagement.png

Four perspectives to visualise hashtag engagement on Instagram (see project pitch presentation)

1. Media item - using metrics of audience engagement per media item combined with user activity over time (or the attention Instagram posts receive according to user activity over the time).

2. User- using metrics of caption or hashtag adoption per user (over time).

3. Visual - analysing visual characteristics of images associated with hashtags (over time)

4. Grammars of Hashtags - looking at forms of hashtag use: positioning/alignment, double sense hashtags*,hijacked hashtags, hate hashtag

This project thus seeks to explore visual methodologies (Rose, 2016) in order to grasp the logics and structures of hashtag engagement (production, circulation, actors, generated content), drawing the rise of political polarization in Brazil as a case study. The main objective is to map and visualise brazilian hashtag engagement along the right and left wing protests of March 2016, differentiating dominant from ordinary voices. March 2016 marked the culmination of a strong political polarization in Brazil, and it also represented the country’s worst economic crisis. Hashtag engagement was not only related to the most pronounced channels of pro-impeachment (center and right wing parties) and anti-impeachment (labor party and left wing) supporters, but also to the sources of historical data generated by millions of citizens. Demonstrations led by political parties and their support groups not only took millions of Brazilians to the streets, but also ended up with the second presidential impeachment in the history of Brazil.

An overview of the protests in Brazil (2013-2017)

Brazil has gone through significant political and socio-economic changes in the past few years; marked essentially by three waves of protests. The first emerged in June 2013; the rise of public transport (bus, metro, train) fares was the starting point of local protests, in early June, in Rio de Janeiro. After that a series of non-partisan demonstrations for social rights spread faster, and June 20 was noteworthy day because of the simultaneous protests that were reported in at least 80 cities (almost two million people went to the streets). Room for improvement was demanded for Brazilians, for instance in education, health care, safe environment, stopping corruption, among others (Omena and Rosa, 2015). The June Journeys or Brazilian Spring were considered a fluid and spontaneous movement; all parties fighting for a common good without effective leadership.

The second wave of protests represents the predominance of pro-impeachment demonstrations and took place in the following year of the presidential race (in October 2014), which ended with the Dilma Rousseff’s victory (51.64 percent) over the right wing candidate Aécio Neves (48.36 percent). Ever since, Dilma’s second term was jeopardised by political opposition and a series of protests along 2015, four in total: March 15, April 12, August 16, and December 13. The anti-government protests were led by the opposition’s nominated leaders and also by three groups allegedly to be non-partisan: Movimento Brasil Livre, Vem pra Rua Brasil and Revoltados Online (Omena and Rosa, 2015). Nevertheless, these groups were (and still are) right wing militants and agents of neo-liberal economic regime, and it is said that such sponsored actions along 2015 culminated in the removal of Dilma. The main accusations against Dilma Rousseff covered administrative misconduct and disregarding the federal budget.

The third wave of protests started in March 2016; the Labor Party, left wing and government supporters join forces to directly respond the series of protests occured in 2015. Millions of Brazilians went to the streets (again!): on 13 and 18 March. One of the parts was supporting Dilma Rousseff’s impeachment process (pro-impeachment), while the other part claimed the president should keep her position as Brazil’s president (anti-impeachment).

On 31 August 2016 Dilma Rousseff was impeached, but she did not lose her political rights for eight years (as it was meant to be). The political battle still goes on in Brazil, as well as polarized protests all over the country together with new requests for the impeachment of the current president of Brazil: Temer Golpista.

Initial data sets

The initial data sets are Instagram posts collected with Visual Tagnet Explorer covering the month of March in 2016. Taking into account Instagram API affordances (it goes back months/years in time) and rate limits, the data collection process was based on a list of representative hashtags of pro-impeachment and anti-coup protests (see data visual protocol below) - from 12 to 31 March 2016. After bringing together all data and removing duplicates, two new data sets were created to analyse the protests of March 13 (pro-impeachment) and March 18 (anti-coup) with a total of 22.423 media items. 17.502 unique users in the pro-impeachment data set and 1.711 anti-coup unique users.

Screen Shot 2017-07-17 at 11.22.20.png

Dataset visual protocol (see the final presentation)

Research questions

Based on the aforementioned hashtag engagement perspectives, the general research questions are:

  1. Which media items get higher audience engagement? What is their relationship with dominant and ordinary voices?

  2. Which are the users most active in the use of hashtags? What is their relationship with dominant and ordinary voices?

  3. Are there common characteristics to the visual content associated with a given hashtag within dominant and ordinary voices? Are there visible variations over time?

  4. How can the grammars of hashtags allow us to infer discursive alignment within the adopted positioning? How does this relate to the common visual characteristics identified within this group?

In addition to those, a specific research question had important role to conduct this exploratory study:

» How can we split ordinary versus dominant voice?

Methodology

Methods were advanced by critical analytics (Rogers, 2016), with the intention of understanding research based on hashtag engagement, and refining the comprehension of the grammars of action (Agre, 1994) of social media. We explored three of the four perspectives proposed to visualise hashtag engagement: media item perspective; user perspective and visual perspective. We executed the following methodological protocol:

(1) A given list of dominant and ordinary voices were pre established according to the audience engagement metrics maintained over time by hashtag users within the month covered by the dataset. The reasoning is that dominant voices are the ones that sustain influence and visibility over a longer period of time (a popular post does not necessarily characterize its publisher as a dominant voice)

(2) Quantitative analysis of media item and user centered metrics in relation to the selected hashtags.

(3) Automatic Content Analysis of the captions associated with pre-selected tags, in order to understand the user perspective of the post.

(4) Automatic coding of the images for gross characterization of the dataset.

(5) Visual network analysis for understanding the content structure of the images via a bipartite image-label network. This was further done by a replacement of graphic nodes by images themselves, pursuing a joint visual analysis of the image dataset in the network layout.

Regarding tools, we adopted:

(1) Memespector/Google Vision API. We used a customized version of Bernhard Rieder’s memespector script for batch automatic analysis of the visual content contained in the dataset within a particular frame of interest. Google Vision API Label Detection module was used for assigning broad descriptions to the images.

(2) Gephi was used for creating and analysing bipartite networks.

(3) Cortext was used to visualise the clusters of captions attached to media items according to its text content.

(4) Illustrator was used for visualizing the edited SVG file of the network, substituting the graphic nodes by the images themselves.

(5) RAW/Rank Flow for strategic visualizations of metrics.

Findings

Media item perspective: Analysing the dominant voice of Brazil’s protests in March 2016

To delimit the dominant voice of Brazil’s protests we generated a dataset using metrics of audience engagement per media item combined with user activity over time. In other words, a list with the 40 users who gathered more attention (likes + comments) on their Instagram posts along the day of the protests.

The dominant voices of pro-impeachment and anti-coup protests have a particular structure: they both are composed by public figures (who gathers high level of engagement with few publications) and the organisers of the protest and activist or non-official campaign accounts (these actors have more publications, they are capable to sustain influence and visibility along the day of the protests). The engagement flow of both groups have common ground: one horizontal, sustained in long time lapse; the other vertical, which displays high peaks of audience engagement between 3pm to 9pm. To better exemplify such structure, just as identify who are the dominant voices and how they visually depict the protests, see the following scatter plots that display of pro-impeachment and anti-coup protests (X = time, Y = total of engagement a user got along the day).

the dominant voice pro-impeachment.png

The dominant voice of pro-impeachment protests (13 March, 2017)

The pro-impeachment dominant voice is composed by public figures mainly represented by Globo’s actors or actresses, TV presenters, stand-up comedians, businesswoman. They had few publications along the day, but they hold high engagement rate (e.g.: araujovivianne, alvarogarnero, marcoluque, cariocadelegado, marciograciamgp,tiagoabravanel, luciliadiniz, ju.knust) In addition to those, the organisers of the protest: vemprarua; and among non-official campaign accounts: chegadecorruptos, foracorruptos_rn (anti-corruption movements which apparently do not support the Workers party politics, in especial Lula), they both create and share aggressive or humorous political memes, and clearly support the Federal Judge Sergio Moro.

In the first instance, the visual culture of the pro-impeachment dominant voice is characterized by selfies (e.g.: fanipacheco, marquesnara, jujunorremose), slogans in favour of a better Brazil (e.g.: “I have faith that we will this country” by luciliadiniz) or humorous posts such as the comedian cariocadelegado who interpreted a crying Dilma Rousseff, and used the caption “I´m sad” or the meme “Take out Dilma and put Paola in her place” - Paola was an infamous mexican soap opera character. Added to this, aggressive messages addressed to Dilma Rousseff e Lula comparing the former presidents to Zika (the mosquito) or demoniac creatures or an inflatable puppet wearing prison uniform, named Pixuleco in portuguese.

In a second stance, the pro-impeachment dominant voice shows support to Operation Car Wash and the Federal Judge Sérgio Moro. The non-official campaign accounts chegadecorruptos and foracorruptos_rn covered images of the protests in the main capitals and cities, e.g. Recife (e.g. a, b) Maceió, São Paulo (e.g. a, b), Brasília (e.g. a, b), Salvador (e.g. a, b), Rio de Janeiro (e.g. a, b), Vitória e Vila Velha, Natal, Campina Grande, and Curitiba. It is curious to note the presence of the account petscharm in the dominant voice list; this profile depicted different breed of dogs wearing the official uniform of Brazil’s football team or Brazilian flag in all publications (e.g. a, b, c, d), also adopting the same caption: 🇧🇷 Hoje é dia de manifestação para um país melhor. Eu faço minha parte, vem pra RUA! 🎉 🇺🇸 Today 's demonstration day for a better country. I do my part, comes to STREET ! 🎉 #foradilma #vemprarua #forapt . Photo : @mydog.mel ------------------------------------------ 🌎Thanks, Use #charmpets for a chance🌎 . 🌎 Obrigada, Use #charmpets para uma chance🌎

And finally, the colours yellow and green have an important visual role to these group of protesters; from wearing Brazil’s football garment to the use of Brazilian flag.

the dominant voice anti-coup.png

The dominant voice of anti-coup protests (18 March, 2017)

The anti- coup dominant voice is composed by public figures, e.g. Humberto Costa (senator), Flora Matos (singer), Alexandre Rudah (actor), Molamolera,( LGTB activist), and Organizations, e.g. Workers Party (Official Account), Muda Mais (Political Movement), and CUT (Central Workers Organization). Most of them pertain to Politics, Activism, Art, and Culture areas. There are few independent media and journalists in this group; they had a strong and consistent engagement along the day, e.g. Media Ninja, Revista Forum and Eduardo Nino. Some political activists with expertise in social media also have a consistent engagement e.g. Lili Ferrer. And at last there are some people that is involved in spiritual activities, e.g. transitivaedireta and Rosangela Pires.

The anti- coup dominant voice represents the protest at different levels. The political parties and activists show the experience of the protest with pictures of the crowds where the colour red predominates. Meanwhile, independent media and journalists depict the protest with political slogans embedded in posters. In the Art and Culture spheres: the adhesion of symbols, for instance, a red flower, some verbal posters (e.g. the image of Lula), or red clothes, a visual culture that do not show the protest itself.

After analysing the dominant voices of pro-impeachment and anti-coup protests, we summarised the common patterns and distinctions among them (see table below):

Dominant voices

Pro-impeachment

Anti-coup



Actors

Public Figures, organisers of the protest and activists or non-official campaign accounts

Actors or actresses, TV presenters, stand-up comedians, businesswoman

Politicians, Artists, Singers, Activists (political, LGBT, feminists), Independent Media


Engagement flow over the day

Horizontal flow: consistent engagement along the day.

Vertical peak: high peaks of audience engagement between 3pm to 9pm.

Public figures have few publications, but they hold high engagement rates.


Main Visual Elements

Adoption of representative colours, crowds, political slogans or humorous posts

Green and yellow, selfies, Brazil’s football garment, dogs

Red, political slogan, crowds, critique on Globo

The common patterns and distinctions among the dominant voices of Brazil’s protests in March 2016

To end this section, we present an interesting finding regarding total of dominant voice’s engagement by groups: in anti-coup protests brazilians were more engaged with publications made by public figures, whereas, in pro-impeachment protests, publications made by the organisers of the protest and activists or non-official campaign accounts drew more attention from protesters (see table below).



Program

Total of dominant voice’s engagement by groups:

Public Figures

Organisers of the protest and activists or non-official campaign accounts

Anti- Coup

144.676

202.010

Pro-impeachment

186.361

97.135

Summary of total engagement registered in dominant voice posts along the day of the protests according to Media Item Metrics, and organised by public figures vs. organisers of the protest and activist or non-official campaign accounts.

User perspective: Caption analysis through term-tag and political group networks

How have been user perspectives manifested in the captions of Instagram posts?

One of the research questions was related to the user perspective of engagement, trying to understand what connections could be made between terms used by people. Would there be any difference when comparing anti to and pro group, or dominant to ordinary voices?Are they using the same terms to express themselves? What are the used tags attached to the clusters of terms?

To address this issue, we ran a a co-term analysis on Cortext, clustering by co-occurrences of terms in captions (considering the specificity of the 50 top terms). Afterwards, we project a 3rd info upon the images, pointing the main tags attached to these clusters of terms.

ALLTermPol.png

All voices, both groups, caption content clusterized by tags.

In the image generated by Cortext, we can see 6 main clusters:

YELLOW: #vemprarua (#come to the streets), #foradilma (#get out Dilma), #foraPT (#out of Workers Party). It seems to be a right-wing (pro-impeachment) oriented group of terms and tags.

LIGHT GREEN: #protesto (#protest), #saopaulo (#São Paulo city), #todospelademocracia (#everyone for democracy). It can mean both groups, it is more connected to “going to streets”, which can go both groups.

DARK GREEN: #emdefesadademocraria (#In defense of the democracy), #globogolpista (#Globo scammer). These terms and tags seems to be more left-wing (anti-coup), which the main node is “corruption”, which is also a word very used for the pro-impeachment group.

RED: #naovaitergolpe (#there won’t be a coup), #vemprademocracia (#come to the democracy), #lulaministro (#Lula as minister). Very co-mentioned (dá pra ver pelos edges)

PURPLE: #democracia (#democracy), #vemprademocracia (#come to the democracy),#todospelademocracia (#everyone for democracy)

BLUE: #patriamada (#beloved homeland), #brasil (#Brazil).

You see a main component (network) that represents the protesters debate according to the post caption. Nodes are co-mentioned terms by specificity (meaninfulness), node size means number of mentions of particular terms. This shows that there is a common and connected agenda, even with different debates (clusters). We can see that the green, yellow and blue clusters are a main component, with high density connections.

When we look into the content of the clusters displayed, some interesting findings can be pointed:

  • The Blue group seems more isolated, but very co-mentioned. The discourse s very nationalist.

  • The Red group is clearly left wing. It is also very co-mentioned.

  • It is not clear what the content posted by the Purple group means.

  • The Yellow group seems to be a right-wing (pro-impeachment) oriented group, considering their terms and tags.

  • Yellow,light green and dark green clusters are partially overlaid, specially the light green one. Considering that the content of the yellow and the dark green are clearly anti-coup, it suggests that the light green means a pro group with the same kind of discourse of the anti group; a second hypothesis is these contents are double sense terms, or even generic terms.

  • The Light green content shows it can be a pro or an anti groups; it is more connected to “going to streets” discourse, which can go both groups.

  • The general terms and tags used by the Dark green clusterseem to be more left-wing (anti-coup); however, the main node is “corruption”, which is also a word very used for the pro-impeachment group.

  • The Purple group content is double sensed, can be anti and pro.

  • Even though in other perspectives (media item and visual) Dilma is not very often present in the images (Lula is more frequent), according to the caption and tag analysis in user perspective, she is more frequently cited.

We also ran an co-term analysis, this time aiming to see the specificities of the discourse adopted in each of the political perspectives (anti-coup and pro-impeachment). We clustered by co-occurrences of terms in captions (considering the specificity of the 50 top terms). Afterwards, we project a 3rd info upon it, pointing the political position connected to the clusters of terms.

ALLTermPol.png

All voices, caption content clusterized by political position.

As can be noticed from the figure, we have gathered 4 clusters of terms on the anti-coup dataset, and other 2 clusters of terms on the pro-impeachment group, each of them with an specific agenda. The two pro-impeachment clusters are more concise in terms of discourse; the anti-coup groups are more split and have remarkable differences in their agenda.

While the clusters remained the same as for the tags projection, the clusters now showed protest affiliation. The largest terms cluster included both pro and anti groups, but with few overlaps. Two anti-clusters overlapped with other, considerably smaller anti-groups, while one cluster showed small overlaps with an anti and a pro group. Although overlaps were rare, there was one cluster that was fairly well connected to some of the other clusters: the protest-location cluster, classed as pro-impeachment. One other trend was prominent: of the smaller unconnected clusters all were anti group (in addition to some larger, partially connected anti groups), while the pro groups were all larger and more connected.

Another interesting finding is that the Light green is pretty central in the network, sharing many terms with other clusters around, anti-coup. We hypothesize that is because 1) the anti-coup movement is more divided, or have more variations in terms of discourse, and 2) the content of the light green cluster is related to places of protests, a more general caption.

For a further exploration of the dataset, it was split into dominant and ordinary voices. These two datasets were also run through Cortext text extraction and visualised through network mapping. For both of these the same queries (terms and tags, terms and political group) were run. Some changes were made for the dominant voices group as it was a considerably smaller dataset. For instance, instead of a minimum frequency of 50 (showing terms appearing in at least 50 captions) a minimum frequency of 10 was chosen to adapt the query to a smaller data size. Similar adaptations were made to the maximum amount of keywords and nodes displayed.

When considering only the dominant voices, we can see how split they are, and also the political position of each cluster, according to the tags and caption terms. In the following picture, we added to the political position the info about the main subject of the term analysis made by Cortext.

The query on the terms network (= network of terms extracted by specificity) showed an interesting distribution. While the visualisation of all voices showed distinct clusters that were strongly interwoven, looking only at the dominant voices, they appeared in more densely connected clusters that were little connected. Those clusters differed in topics and claims with the most connected cluster revolving around protest locations. Thus, the dominant voices were tightly knitted sub-units concentrated by specialty or niche areas. This suggests that voices become more dominant when they focus on terms and tags pertaining to niche / specific subjects.

We ran the same analysis to the ordinary voices of both days of protests.

This map displays ordinary voices, that are highly connected. We can see 2 main big pro and anti clusters that overlap (green and purple). We can see many people, as expected for an ordinary voices group compared to the dominance voices one.

Two main clusters (green and purple) seem divided, but some terms in the middle connect them. Our main hypothesis is that it stands for people inviting each other to go to the streets (using terms like “Avenida Paulista” or “Come to the streets”), which could work for both groups since the locals of the both protests were the same.

Other hypothesis is that some common agendas are being displayed in the ANTI and PRO overlap. An example is the expression “wake up Brazil”, meaning for both groups a claiming for more political engagement. Also, we have in this overlap, sometimes, displays same terms targeting different directions/people according to each group. For instance, “go for democracy” means to pro-impeachment groups getting rid of the Workers Party president and, to anti-coup group, it means to keep the elected president in charge.

There is a small green pro-impeachment cluster upside, apart of the main network,not strongly related to these main clusters we discussed. These are specific terms with nationalistics content. What connects them to the main ordinary voice? “#BelovedHomeland” and #Liberty. There is another separate cluster anti-coup, the red one, with #Against The Coup connecting them to the main clusters.

Visual perspective

To pursue an analysis of the visual content of the protests, we made a batch analysis of the dataset using Google Vision API Label Detection module. We visualized the results as a bipartite network associating images and their labels. This was done for each of the protests as shown in the figures below.

On the left, bipartite image-label network of the ‘anti-coup’ protest dataset. Directed graph. Edges weighted by label detection score. Force Atlas 2 layout algorithm. 2,869 nodes, 40,927 edges.

On the right, bipartite image-label network of the ‘pro-impeachment’ protest dataset. Directed graph. Edges weighted by label detection score. Force Atlas 2 layout algorithm. 17,606 nodes, 73,127 edges.

Anti-coup image-label network with graphical nodes replaced by the images.

Images in the ‘crowd’ corner of the anti-coup protest network.

Images in the ‘portraits and selfiescorner of the anti-coup protest network.

Analysis of the networks formed revealed a common structure of the visual dataset for each protest. Which is presented in the figures below. Both of them showed a triangular structure with common vertices related to crowd open-shots, portraits/selfies, and textual graphics. At the center of each there were color labels related to the colors worn by partisans of each protest: red for the anti-coup and green and yellow for the pro-impeachment.

Structure of the image-label networks for each protest. Anti-coup on the left and pro-impeachment on the right.

Also, both networks had a food-related cluster in which one could find pictures of food which are pejoratively attributed by partisans of each group to their opponents. Boloney sandwiches for the anti-coup partisans and chicken drumsticks for the pro-impeachment ones.

Images in the food cluster of the anti-coup image network.

Specific subclusters were also observed in the networks of each protest, revealing types of content which were most found in one or the other group. In the pro-impeachment network there was a subcluster of the ‘portrait/selfie’ component which was labeled by terms related to sunglasses and eye wear. Also, there was a specific small cluster related to dogs and dog breeds. Regarding the sunglasses subcluster, we attribute its manifestation to sunglasses being a status symbol in Brazil and to the fact that pro-impeachment protests had larger adherence to middle and upper class ideals. Regarding the dog related cluster, this finding is coherent with the findings of the first perspective and is related to a hashtag hijacking by a pet shop which promoted themselves with the protest and the use patriotic motives.

Pro-impeachment network with sunglasses subcluster and dog cluster highlighted.

In the anti-coup network a subcluster related to facial hair and beard was observed close to the portrait/selfie large cluster. This was attributed to a cultural aspect of the Brazilian left, whose male partisans are commonly known for having beards.

It was not possible, due to operational issues, to pursue all questions specific to this perspective, including the observation of the variation among dominant and ordinary voices or among particular hashtags. Nevertheless, the findings show both the common aspects of the broad visual culture of protests in both sides of the political dispute, as well as specificities among them. Regarding the observed differences, whereas the reproduction of stereotypes in the findings should raise suspicion, it is possible to consider that stereotypes are actually part of how the partisans of each side represent themselves, since the images are mainly self-representations of the protesters.

Conclusions

The Media Item perspective drew the division of the dominant and ordinary voices, and to delimit the dominant voice of Brazil’s protests we generated a dataset using metrics of audience engagement per media item combined with user activity over time. Besides identifying distinctions and similar patterns, we found that dominant voices have a particular dynamic which deserves special attention. This dynamic, for instance, reveals the agency of two groups: on one side, popular or public figures, and on the other protests’ organises and non-official campaign accounts. Key findings were disclosed through detecting who are these actors, what visual content are depicted by them, and how powerful or influential are their posts over time. The importance of this close study afforded rich contextualization and detail of the visual culture of dominant voices, which were analysed through the combination of basic excel formulas and manual coding (at least in this project). However, such proceeding do not match the analysis for the ordinary voice due to its large dataset.

Regarding the User perspective, we used a content network mapping for a visualising the networks of terms with (1) hashtags projected onto them, and (2) political groups (anti-group and pro-impeachment group) projected onto them. Afterwards, we ran the same analysis for dominant and ordinary voices.

The text extraction of the dominant voices showed that the terms with the highest score (specificity and frequency) were about the locations of the protest. This suggests that dominance may not necessarily be related the caption or hashtag content, but to either (a) pragmatic information with regards to protest points and locations, or (b) organising power.

A second hypothesis that can be derived from the text extraction concerns the right-wing party’s efforts at making transport free on days of the right-wing protests. If dominant voices become dominant through their organisation and distribution of protest locations, then it follows that on days where more protesters travel (due to free transport), there is quantitatively more of the dominant content on Instagram. Thus, narratives of protests taking place on those days - in this case right-wing pro-impeachment protests - are more likely to become dominant and therefore have more potential for influence. Thus, there is an interesting dynamic between protest proliferation offline (making transport free) and post popularity on social media.

The visualisations suggests a few more things. All the pro-impeachment groups were larger and well-connected, although they were fewer. They also included the protest-location group. This suggests that the dominant voices in the pro-impeachment group were more connected and (through the connections and the protest-locations) more tightly and therefore potentially better organised.

As expected, ordinary voices shows more connections between the two main political groups (pro-impeachment and anti-coup) than dominant voices. The common space seems to be linked to the aim to gather and engage people. This goal is reached stressing the collective dimension and so using common words, such as “all together”, “come on”, “on the road”.

Regarding the Visual perspective, the findings suggest the need of further inquiry on the use of automated coding of images in large visual datasets. It is clear that the validity of using such tool is not the same for every research topic and should be assessed in each case. For this research, it was shown useful for describing the overall content of the images as well as for comparative analysis between the protest visual culture of protests of different political groups. For the development of hashtag engagement studies, however, there is still work to be done regarding the visual characteristics of images related to particular hashtags, since the study only regarded the dataset as a whole, without further segmentation.

Generally considering the overarching goal of developing four perspectives of hashtag engagement studies, this research has been important as an exploratory start. It has helped us to devise an initial methodology and draft the concepts. It has shown the relevance of Instagram as a platform for studying the political climate in Brazil, as much as its complexity, which requires refined methods and theory to approach it as a research subject. One of the main hypothesis that guided the proposal of hashtag engagement approach seems to be proven by the initial findings: that the most popular posts are a weak descriptor of the overall engagement of users with the political issue at hand. The dominant/ordinary split seems a good proposal for reaching a broader narrative of the not as popular posts, which, however, display certain patterns and characteristics that may serve a broad description. Finally, the study has also reinforced the understanding that online political engagement is not specific to the internet but, rather, is a manifestation of grounded positionings.

However, there is still a lot of work to be done. We have observed that each perspective provided a different outtake of the same phenomenon – for instance, the national anthem was only found in the user perspective. There were also findings that span across the perspectives, such as the dogs pictures, which were observed as part of the dominant voice and which were also an observed cluster of the visual analysis. This initial exploration thus suggest the possibility of the analysis to run on various iterations, aligning each perspective to the findings of the other. For instance, one hypothesis to pursue would be a possible correlation between the national anthem in the text part of the posts and the appearance of the national flag or the national colors in the images.

Ideally, it would be important to run the research with the same corpus once again to refine the approach and further ground the theory and methods. It is important not to limit the perspectives to single methods, as we did due to time constraints. Rather, the perspectives are defined by the research questions, which seem to require the combination of multiple methods. These are all questions left open by this particular endeavor, which guide research that we would like to pursue in the near future.

References

Agre, Philip E. 1994. “Surveillance and Capture: Two Models of Privacy.” The Information Society 10: 110–27. doi:10.1080/01972243.1994.9960162.

Rose, Gillian. 2016. Visual Methodologies. 4th Edition. UK: Open University.

Rogers, Richard. 2013. Digital Methods. Cambridge, MA: MIT Press.

Rogers, Richard. 2016. “Otherwise Engaged: Critical Analytics and the Meanings of Engagement.” YouTube. https://www.youtube.com/watch?v=sNwl-qGrK7M&;.

http://www.labic.net/wp-content/uploads/2015/09/The-Color-of-the-Street-DataWiz-2014.pdf

Topic revision: r5 - 30 Jul 2017, JannaJoceliOmena
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