A researcher from Stellenbosch University discovered that negative emotions can be reinforced through social media interactions, with individuals suffering from depression being particularly susceptible to this influence.
For people with depression, the internet can offer an opportunity difficult to find in real life: a space for open communication, finding like-minded people, and feeling less alone. However, these online connections can also have a dark side, as negative emotions can potentially reinforce each other when communicating on social media.
This is the problem studied by Dr. Kurt Marais, a lecturer in the Department of Logistics at Stellenbosch University (SU), as part of his recently completed doctoral dissertation in operations research. He investigated how emotional states transfer in online communities, focusing specifically on depression, and examined how this process can be measured and better understood.
The study focused on a subreddit dedicated to depression on the Reddit platform, where users from different countries, age groups, and social strata share their experiences, seek advice, and support others. Over 56 days, Marais analyzed 12,133 unique posts. The average post length was 178 words, although the longest contained 5,192 words. Most posts came from users in the United States, the United Kingdom, Canada, India, and Germany.
Among the most frequently occurring words were 'like', 'feel', 'life', and 'want', reflecting users' attempts to describe their emotional experiences and desire to feel more than depressed. There was a frequent use of first-person stop words such as 'I', 'am', 'my', and 'me'. Words like 'feeling', 'depression', and 'thinking' appeared in the list of most frequently used words alongside 'feel', 'depressed', and 'think', respectively.
The study also revealed patterns in when people most often posted content. Activity increased at the beginning of the week, with Mondays and Tuesdays recording more posts than other days, while Saturdays were relatively quieter. The day of the week also influenced user reactions: Monday posts attracted more upvotes, while Saturday posts received more comments.
Marais also found that the online community became particularly active late at night. 'It is interesting that the number of posts increased from 10 PM to 5 AM. This confirms previous research suggesting that people with depression tend to be more active on social media at night.'
However, one of the most significant findings was the influence of users' emotions on each other. Marais calls this emotional reinforcement—a process where people can amplify feelings expressed by others in their online network. He explained that if two people post negative content, they are likely to continue doing so more often and longer, thereby intensifying these emotions. This effect was even stronger among people with depression. Although it is also observed with positive emotions, the effect is not as strong in the case of exchanging negative emotions.
To better identify these emotional patterns, Marais developed a depression-specific lexicon, which is a specialized list of words and expressions related to depressive emotional vocabulary. He found that this tool performed better than general sentiment analysis tools and could identify relevant language in posts about other mental disorders, including anxiety and PTSD.
Marais also compared his approach with large language models trained on mental health-related material, asserting that his specialized lexicon offers a more reliable way to determine depressive mood in this context. The study was not limited to Reddit. Using insights gained from the online community, Marais created an agent-based simulation model to study how mental health-related content might spread on Twitter, now known as X. The model was based on observed user behavior, realistic interaction models, natural language processing, and potential intervention strategies. The tweets used in the study were collected before Twitter rebranded to X, so the results pertain to the platform's design, features, and algorithms at that time.
Some posts contained simple statements about personal experiences with depression, including phrases such as 'I am diagnosed with depression', 'I am fighting depression', and 'I suffer from depression'.
The simulation showed that people's emotional states can persist over time, and interaction with others can also influence these states. Marais found that repeated exposure to similar negative emotions can strengthen and deepen these feelings.
For Marais, these findings have implications beyond academic research. He noted that researchers gain tools to study mental health online, practitioners gain insight into contemporary experiences of depression, platform developers receive evidence-based recommendations for improving user well-being, and social media users themselves better understand their role in algorithm-driven environments. This study highlights the complex role that social media can play in the lives of people suffering from depression. Online platforms can provide connection and support, but the same networks can create conditions where negative emotional patterns are constantly reinforced. Marais believes that his work can contribute to a better understanding of this relationship and help shape a healthier digital environment.
