(In Farsi:) برای یک زندگی معمولی. For an ordinary life.

In September 2022, when Iranians started a series of street demonstrations, everyone knew that a new social movement was taking shape. Many asked what this movement was all about; and many truly answered: abolishing a system of male-dominant gender relations. However, social movements not only criticize the status quo but also create and are moved by an image of society and life. So, I asked myself: What is the collective imagination that this movement aspires to and is being fueled by?

Looking for the answer to this question, I listened to almost every piece of music and watched almost every video that would come out over the course of the movement. “For” (or “Baraye” in Farsi) was a song that was released shortly after the start of the movement and soon drew special attention both domestically and internationally. Its lyrics were a selection of all the tweets in which users had used the hashtag “for” to say what the movement was for. The song begins with these phrases: “For dancing in the alley; for the fear at the time of kissing; for my sisters, yours, ours; for changing the rotten minds; for the shame of pennilessness; for the longing for an ordinary life…” The idea of “ordinary life” was so powerful that it immediately drew my attention to it. Was it the collective imagination behind the movement that I was looking for? And even before that, what is an ordinary life in the first place? How do people think about it? And maybe more importantly, do they think about it the same way?

I knew that to find good answers to these questions, there was nothing better to analyze other than the content people naturally create on social media. I started looking at tweets containing the phrase ordinary life, but this would make a huge body of tweets. I believe a deep engagement with the data comes from spending a lot of time reading through the texts and taking notes over and over again, but I also became interested in computational text analysis methods that let the machine find patterns in the data. These patterns might be occasionally insightful and can make the researcher think of patterns they would not have thought of otherwise. I knew that I especially needed to learn about the logic of these methods as a solely technical understanding of them would lead to not strongly supported conclusions or at best confusion.

During my time in the Digital Scholarship and Publishing Studio Summer Fellowship, I tried to learn about computational text analysis methods, more specifically, a method called topic modeling, and acquire some of the coding skills necessary for this kind of analysis. Topic modeling is an example of unsupervised analysis that goes over a series of documents (in my case, the interviews or the tweets) and outputs a series of word lists (i.e. topics) determining how much of each topic, each document contains. So far, I have read different articles about the topic modeling approach to understand its logic and process and have finished an introductory course in Python. I will also continue my project and start coding a topic modeling program to analyze my interview transcriptions, after this fellowship.

Topic modeling can be extremely insightful as it can show us what the notion of “ordinary life” is associated with, and more importantly, how convergent or divergent the perceptions about it are. This is extremely crucial as it provides empirical evidence for the debate about whether the movement is being driven by a unified collective imagination or follows only some of the middle-class aspirations.