Laura Canuelas, Jorge Cid, Marloes Geboers, Emillie de Keulenaar, Mayu Lida, Wouter Meys, Mariola Pagan, Giovanna Salazar, Iris Steenhout, Alexei Tsinovoi, Lisa Krieg
This project aims to see how controversies, narratives and images related to the MH-17 crash have unfolded on Instagram, specifically, how does tragic content get mediated on an aesthetics based platform such as Instagram.
To answer the first research question a co-hashtag analysis was executed.
First finding: Two big clusters of hashtags: 1) plane crash 2) soccer player
Decision: manually deleted the soccer related hashtags, in order to see more depth in the 1st cluster
Second finding: +10000 nodes, the largest part of them have below 150 connections (see graph)
Decision: filter out all nodes with less than ca. 150 connections
Applying the layout force atlas
Node size on the basis of node degree (highly connected nodes are bigger)
Calculating modularity at 0.8, color partition on the basis of modularity
We found five clusters: 1) instagram pop culture, 2) photography (malaysian local) 3) plane crash and mourning malaysian local, 4) mixed cluster: plane crash, mourning universal/international/dutch, aviation universal, 5) political issues and international community (also Russian language tags).
We started our research with a sample of 17238 images with their corresponding metadata (ID, hashtags, users, URL, etc), scraped to a .csv file. From this we took a random sample of 1000 posts. To find a sample of a thousand random images, we used the Microsoft Excel Random Number Generator [=RAND()]. Once random numbers were each associated to an image, we had our sample. We extracted it from the data file, all the while trying to find a way to keep it connected with its metadata.
A problem, however, arose: we needed to identify the right images from our data file, but the ID of images in there did not correspond with that in the metadata. Those in the metadata were part of the URL from which the images were originally extracted. To identify our sample in the data file, then, we extracted their ID from their URLs. This was done manually by copy-pasting the URLS into Microsoft Word and erasing the webpage base of the URL, exposing the picture ID. Once we had clean names, Emilie made a folder with the sample images.
Although we created an Image Plot using saturation scales earlier in this process, the result did not show any trends and categories, and, so, proved not to be very useful.
We analyzed the hashtag clusters that our hashtag group provided with their graphs (Gephis) and identified five, main trends: political; Malaysia; professional photography; airplanes; and generic mourning. We did not take into account the Instagram centric cluster, as they were not particular to the theme of the tragedy (instead, they were a group of hashtags aiming to attract more followers, specific to Instagram).
We took 50 images out of every cluster, due to time concerns and the complications of coding them manually. We coded those related to the most popular hashtags we found in each cluster.
Our coding process divided our sample into text-based images, portraits (individual and collective), memes, user generated and/or intervened content and news related (journalistic/ professional) images. We completed this coding in different spreadsheets, each dedicated to a different cluster, and finally combined them into a file that Imageplot could read.
(Preliminary) findings relating to user engagement and image analysis
By modifying the ImagePlot script so as to add a third dimension to the plots we were able to make some plots that made more sense, in the plot underneath the size of the images is adjusted to its likes.
Youd need to zoom in though to get a better look of this third dimension. (Saturation - brightness on the axes, which leads to clustering of memes such as the ribbon).
Unfortunately in the plots, the result of our manual coding could not be applied, however as we only coded 50 images per hashtag topic cluster we were able to engage in a qualitative visual exploration of the images that were found in the hashtag clusters. In the table underneath our preliminary findings are written down.
Remarks and Observations
All of the images were user intervened, mostly through filter usage
They seemed artistic type of tributes (conceptual, abstract pictures)
Almost no memes (in the sample there was just one sign holder)
Text based pictures are really popular
Very few journalistic/ news images
Portraits feature both single persons and groups
The black ribbon meme was the only one that showed up multiple times
Most pictures have been user intervened in someway
Pictures of funerals
Text messages were more sentimental (mourning or hope) than political
Hashtags related to mourning
Sign holders: Say no to Putin
News illustrations are more pro-russian
Lots of news images
User generated content tends to be more caricatures with political
Evoking an airplane often refers to violence of accident -- "crash", disaster + violent and insufferable fate of victims.
Personal mourning often caption of personal observations and own, personal memory
Skepticism against 'world order' and 'the media' (with conspiracy images). Use of media against the media.
Corrective engagement: critiques of social media environment/trends
Images expressing effort to understand situation, more than anything; trying to find clues as to what, exactly, the situation at hand means. Bewilderment.
Surreal images: trying to find lost aircraft in random places, or expressing hope that the airplane would be in lost place
Images as effort (or curiosity) to remember how things were before the crash: when plane was intact; when people were alive; when ties were still bound. Try to re-enact perception and experience of passengers in indefinite time before crash; some images repost photos that passengers posted before boarding into MH17. Form of mourning?
The meme of black ribbon appeared constantly
Several images were user generated by putting together a collage, where portraits, black ribbons and news images coincide
The tone of the pictures tended to be sober and respectful
Several religious references, such as images of religious sites and prayers
The image of the arrival of coffins at the airport is recurrent
Another limitation was the fact that our coding of image categories within hashtag clusters could not be taken into account when making the ImagePlots due to a lack of time, this should be done to make plots more meaningful.
Plots with categories and engagement metrics should reveal which categories of images (memes, text based, portraits, professional news images) trigger user engagement and how this is cluster/topic/issue related so as to shed light on user cultures on Instagram and their engagement with certain kinds of images.
Current news issues, such as the Gaza war, get entangled with the tragedy as well, which says something about the messiness of the platform, at least when it comes to the case studied here.
Our findings resonate with Lev Manovichs claims about the Euromaidan, where the mundane and the exceptional become intertwined. This study zoomed in on the exceptional - the MH17 crash - also in the exceptional and tragic, the mundane and vague (like photography, the search for likes and followers, and completely different political current concerns) become entangled with mourning for the MH17 victims and respective political demands.
To gain more insight in the last part of our research question: what is platform specific or platform dependent about the way the mh-17 tragedy unfolded on Instagram, a comparative analysis should be pursued so as to shed light on differences and similarities in the way narratives and images surrounding mh-17 unfold on other platforms such as Facebook, Wikipedia and Tumblr.