Visualizing the Dream Life of Russians During Wartime

Hexagons on a dark sky background

Editor’s Note: Liubov Popovets is a data journalist and an infographics designer who works for Russian independent media. She left Russia soon after the country invaded Ukraine. When she agreed to share this project—which explores human subconsciousness during times of war and political repression—she requested anonymity for the professors and students at two universities in Russia with whom she worked due to the risk of penalties and censorship they may face for pursuing the research. For this reason, Nightingale is withholding the names of project collaborators as well as their respective institutions.

Why did I decide to tackle this topic? Because my own dreams became more anxious and detailed after the Russian invasion started a year ago, and, I supposed, this was also happening to others. I was interested in finding common patterns in those dreams and trying to connect them to reality. I wanted to discover what people really feel. Plus, I wanted to create an artistic, data-journalistic piece which would reflect the metaphorical state of a dreaming mind.

It all started in April last year, less than two months after the war started, when I wrote to a sociology student who had posted a Google form asking people to describe their recent dreams. The survey was distributed through personal and social media networks and the majority of responses were from other students. I suggested that she show me the survey results so I could do an analysis and create infographics on the data. I thought the idea was a gem and gems have to see the world. She agreed enthusiastically that we make a longform story. We waited until more people filled out the questionnaire; in total, about 900 responses came in, almost all from Russians. Here’s the demography of the respondents:

Bar chart showing age: 438 respondents were 15-25, 270 were 26-35, 43 were 36-45, 97 were over 45.
Bar chart showing respondents by age.
Bar charts showing urbanization: 729 lived in cities over 1 million, 53 were in 500,000 to 1 million, 64 were in smaller areas.
Bar chart showing respondents by urbanization.

Thereafter, we read every dream, and manually marked and analyzed them. I admit that the sample might not be 100% representative, however, it did capture a particular slice of the population of a certain age (generally younger) and residence (generally urban). Here, won’t focus so much on interpreting the dreams, but rather the creative process of the project. If you want to read the article and see the final graphics that I’m going to break down, please go here

Categorizing the dreams

We read several works on dreams people have in times of political repressions and dramatical changes. I suggested we categorize them by the role of the dreamer, the action they took, the emotions they experienced, and the people and place they mentioned, if any.

One of the sociologists offered to use the emotion wheel theory, which presumes that our feelings have a spectrum with eight basic emotions that change just like colors on a color wheel. We also categorized three modes of behavior: active, passive, and neutral. Finally, we grouped dreams into the most common dream plot lines, such as running away, failing to do something, fighting, etc.

Analysis and interpretation

We assigned numbers to each activity and each emotion. Afterwards, I merged similarities so finally we had eight emotions and 11 actions. As the four students and I marked the dreams manually and independently, we had to double check everything after each other. If one of us was uncertain what category to choose, we discussed it together, and you can imagine the amount of manual work there was—it took nearly three weeks. Note that there were more emotions and actions than dreams collected, as people could experience several emotions and do several things in each dream.

Then, the question of the underlying causes of the dreams arose. As we focused on the quantitative research, it was clear we needed a specialist to answer how the dreams changed and why they changed that way. I asked a psychoanalyst at another university to explain the repetitive plotlines and symbols that our respondents had described. She noted that there are two main types of psyche that specialists deal with in practice: one tries to symbolize in dreams what’s going on in reality (neurotic), and another that tries to get rid of what’s going on in reality (psychotic). The events of the war make it so difficult to cope with reality, she told me, that the situation intensifies the psychotic scenario more than the neurotic one.


Once I had measurable data, I needed to represent it. I started with making draft charts in Excel. Next, I created different sized shapes in Illustrator. I chose to associate the mood with color, and the following charts show my result.

The first set of data shows feelings. Respondents reported a sense of fear and shock or surprise in most cases.

Hexagons that are sized according to frequency of feelings in the dreams. 533 felt fear or concern, 245 were surprised, 183 felt guilt or sadness, 169 were interested, 146 were not defined, 129 were happy and calm, 116 were angry, 75 felt disgust or boredom, 55 felt admiration.
Hexagons sized according to frequency of feelings in the dreams. The largest share (more than 60%) felt fear and concern. About 20% felt guilt or sadness. More than 10% felt anger.

When it came to actions, dreamers often tried to do something, but failed. Many also reported running away or being a victim.

Hexagons that are sized according to frequency of actions in dreams: 306 had unsuccessful tries, 258 had running away, 238 had being a victim, 116 had rescues, 146 had looking for something, 128 had waiting, 125 had helping, 93 had conflict, 61 had flirting, 50 had running late, 47 had having fun and 27 had some undefined action.
Hexagons sized according to frequency of actions in the dreams. The most common actions were trying unsuccessfully to do something, running away, and being a victim.

Anger and conflict occurred less frequently than other emotions and actions but there were still enough instances for these metrics to warrant further questioning. The psychoanalyst said that dreams with anger and conflict might be due to the aggression people feel in real life. Even if we claim we are against violence, we might feel angry because we can’t do much about the pain and injustice we’ve been witnessing.

Finally, we noticed that respondents took an active stance in their dreams, as shown in the chart below, despite that they were not always able to achieve their goals.

Hexagons that are sized according to frequency of dreamers' roles: 427 active roles, 253 neutral, observative roles, 162 passive roles.
Hexagons sized according to frequency of dreamers’ roles. About half the time, the dreamers were active in their own dreams.

I picked a simple column chart to represent the most frequently cited people in the dreams. I used Figma and applied a contour effect to the portraits. Initially I wanted to emphasize the dreamers’ general attitudes to these people with a color. Putin’s bar, for example, would be red as he appeared in a negative light in most dreams; Zelensky’s would be green as he appeared as a wise and generous man. This, however, would break the minimalistic design concept I had chosen, so I put portraits of positive characters right, the negative ones left and those who were mentioned for comparison, like Hitler and Lenin, in the center of the supposed axis. (I’m still not sure it was the best option.) 

Instances of people who occur in dreams, in a bar chart: Putin at 49, Zelensky at 9, Shulman at 8, Lenin at 8, Navalny at 5, Arestovych at 5, Hitler at 3, Lukashenka at 3, Kadyrov at 3 and Solovyev at 3.
Instances of famous figures appearing in dreams.

I also made a wordcloud to see the most common words.

Word cloud, where more frequent words appear bigger than less frequent words. Biggest words are "speak," "go," "understand," "house," "person," "start," and "look."
The words that appeared most in the dream descriptions are larger than the others in the word cloud.

I was also curious about relating emotions to actions, so I decided to add some interactivity. The Sankey diagram is a good option to show such connections, and I created two in Flourish. Color, again, reflected the mood of emotions, their spectrum.

A Sankey diagram showing the relationship between the active/neutral/passive positions of the dreamers and the frequency of each emotion for those actions.
A Sankey Diagram showing relationships between the position of the dreamer and the emotions they felt. The Sankey is showing unique emotions—not unique dreamers. Because multiple emotions could be in one dream, there are more roles on the left side of graphic than unique dreamers.


As I was full of confidence to make the project figurative and mysterious, I experimented with three AI platforms—Midjourney, DALL-E, and Stable Diffusion—to create images from texts. I used both exact quotes from the dreams’ texts and the words that could convey the atmosphere: “hyperrealistic,” “foggy,” “horror movie,” etc. Midjourney dealt the best with the task because the others gave results that were too literal.

Final takeaways

I put the final project into a layout in Readymag. The emotions were the central plotline of the story. I added an analysis of the results at the end. Here’s a summary of the findings:

  1. The most frequent emotion experienced in dreams is fear. In the second place there was surprise. Admiration and trust were the rarest emotions.
  2. With actions, people most often tried to do something but did not succeed. Another popular action in dreams was running away and trying to escape. Respondents almost didn’t rest or have fun.
  3. Despite the dreams being often pessimistic, dreamers usually took an active stance in them. Somewhat less frequently, they watched from the sidelines, not actively participating.
  4. Mother was the most frequent “visitor” in dreams with loved ones. Respondents also noted that they witnessed grandmothers and grandfathers, including those who had already passed away.
  5. Of famous people, respondents most often mentioned Vladimir Putin, Volodymir Zelensky, and Ekaterina Shulman, a popular opposition political expert.

What else could I take away from my work? There are a lot of vibrant themes which get overlooked because authors don’t know how to enrich them with data, or what methodology to apply. Even if a topic seems too abstract and presumes only qualitative data, it’s not always a reason to neglect it; in some project like this, you can count occurrences of qualitative features. Or, you can create an approximate and artistic visualization, illustrating the storyline. With qualitative data, you can collect data manually, and it won’t make your project necessarily amateur, but actually turn it into something outstanding. I wish you all the luck and perseverance with the topics which excite and inspire you the most!

Author profile

Liubov is a dataviz lecturer at the Higher School of Economics, with a MA in data journalism, and author of the call_me_data project. Liubov loves finding data stories in art and literature.