Hi and welcome to my talk. My name is Annika Kaltenhauser, and I will present our late-breaking work:
MEMEography Understanding Users Through Internet Memes .
In our late-breaking work, we propose a method we call MEMEography.
With MEMEographies, we analyze internet memes as a means of understanding an offline population
when access to the field is beyond reach.
Ethnography is a common research method in HCI evolving around the researcher observing
people within their natural environment. Ethnography is especially valuable for understanding unknown,
specific or complex domains, for example, hospitals.
However, just when we were ready to conduct fieldwork for a research project to develop
an AI-based expert system for intensive care units in 2020, visitation bans and other measures
related to the COVID-19 pandemic destroyed our plans.
Luckily, many people today engage in online communities. Within this trend, we can search
for an alternative to physically observing our field sites. In other words, with large
quantities of user-generated content, we can observe users "there" in online settings.
We propose MEMEography a kind of ethnography, which uses internet memes as the subject of
inspection. An internet meme is an information item, often bearing a humorous note, copied
or imitated via the internet.
Within the concrete example of a MEMEography of the intensive care context, I will now
describe the basic steps and the decisions to be made when running a MEMEography:
In a first step, we collected public posts, the respective numbers of likes and comments,
and the posts threads of comments on Instagram. We decided to filter by hashtags to capture
a snapshot of the existing meme posts. Alternatively, one could collect memes on other platforms,
over a longer period of time, or by filtering for users.
Using these hashtags, we retrieved a sample of more than 3,000 (3,396) posts from 480
users on Instagram. The collected posts span the period between July 2015 and December
2020. We reduced our data set to only posts from 2020, which led to a final corpus of
1,531 posts posted by 225 users.
For our MEMEography, we chose a mixed-methods approach, including text analysis and thematic
analysis.
We used optical character recognition to detect and extract text from the posts and further
analyzed their sentiment. Consistent with previous work reporting that humor is used
in intensive care units to process negative emotions, less than 10% of posts captions
and image texts indicated a positive sentiment.
Through word count analysis, we identified common topics and their occurrence over the
year 2020. Like the word cluster "problem" being mentioned increasingly throughout the year
or "COVID-19" , which reached a peak particularly in March, April and May.
For thematic analysis, we subsampled our data set via likes and excluded posts that did
not display memes. This resulted in a final data set of 419 internet memes to analyze
thematically. We argue that this procedure provides a quick and guided exposure to the
topics that resonate most with the online community.
Within thematic analysis, we identified five recurring themes:
First, we gained insights into the intensive care environment.
Second, we identified a broad spectrum of challenges reported in the memes. For example,
many memes reported problems between colleagues within the intensive care unit and across
other departments or with patients and their relatives.
Third, within the memes, processes and work tasks were mentioned or reflected upon.
Forth, memes reported on emotions. The most frequently reported emotions were frustration
and anger. Positive emotions, such as pride or happiness, were strongly underrepresented.
Fifth, the highly specialized knowledge of intensive care medicine is reflected in the
meme s jargon. In our data set, we found 231 technical abbreviations, medical terminology,
as well as idioms. MEMEography allows us to understand experiences
and thoughts wrapped in humor. Moreover, memes can offer us insights into the individual
perspectives of intensive care practitioners. Unlike and on-site observations in a potentially
challenging environment, MEMEography allows us to spend as much time as we need with the
described situations.
However, the results of MEMEographies are limited by their biased and unrepresentative
data sets of the entire offline population. Additionally, we must keep in mind that the
people who post publicly on social media may want to attract attention by posting more
extreme and polarized content.
Nevertheless, we gathered rich and valuable insights through MEMEography, without ever
setting foot into an intensive care unit. We argue that MEMEographies can be generalized
to other contexts and provide great potential for contact-free user research.
Thank you for your interest in our work. For more details, check out our paper.