The addictive nature of social media, particularly platforms like Instagram and TikTok, leads users to spend excessive amounts of time doomscrolling. A lecturer from Stellenbosch University examined depression-related content on social media platforms such as Reddit and Twitter (now known as X) and found that by reinforcing negative emotions, users can exacerbate and deepen each other’s depression.
Dr Kurt Marais, a lecturer in the Department of Logistics at Stellenbosch University (SU) — who recently earned his doctorate in operations research from SU — examined how the sentiment of emotional content spreads through social media and explored ways to measure its impact on users’ mental health, focusing specifically on depression.
Marais analysed posts from a depression-focused subreddit (an online discussion group or forum on Reddit where users post and comment on a specific topic) to identify common words, phrases, and patterns.
The lingo
He developed a depression-specific sentiment lexicon (a structured list of words used to measure depressive emotional tone in text) that can accurately detect language linked to depression compared to general tools.
Marais collected 12,133 unique posts over 56 days. Posts averaged 178 words, and the longest was 5,192 words.
He notes that people of diverse ages, habits and countries meet in this community to share experiences and to provide and receive support.
Most of the posts came from the United States of America, the United Kingdom, Canada, India and Germany.
Marais says the most frequently used words in these posts were “like”, “feel”, “life” and “want” — with people often writing about how they felt and their desire to feel something other than depression.
“This is reflected in the frequent use of first-person stop words such as ‘I’, ‘am’, ‘my’ and ‘me’.
“Words such as ‘feeling’, ‘depression’ and ‘thinking’ appear in the most frequently used word list alongside ‘feel’, ‘depressed’ and ‘think’, respectively.
“Posts increased on Mondays and Tuesdays, with fewer posts on Saturdays.
“Relative to other days of the week, posts sent on Mondays received more engagement in the form of upvotes, while posts on Saturdays received more engagement in the form of comments.
“Interestingly, the number of posts increased from 10pm until 5am.
“This supports previous studies showing that people living with depression tend to be more active on social media at night.”
Amplified feelings
Marais says another interesting finding is emotional reinforcement — when people amplify each other’s emotions online.
“For instance, if two people are both posting negative content, they are likely to keep doing so more frequently and for longer, thereby intensifying those emotions.
“This effect was even stronger among people with depression. It also happens with positive emotions, although the effect is not as strong as is the case with shared negative emotions.”
Marais argues that the depression-specific lexicon that he developed is more reliable than current large language models trained on mental health-related content.
“The lexicon was more accurate than widely used general sentiment tools when analysing posts from subreddits related to other mental health conditions as well, such as anxiety and posttraumatic stress disorder.
“It improves on traditional open-source word lists that are designed for general language use but would miss the subtle emotional expressions found in mental health conversations.”
Rebecca Matle, Tsholofelo Makhathini, Faaiza Gangat, and Minah Mojela 30 Jun 2026 Mental health content
In addition to the depression-specific lexicon, Marais used the insights from Reddit to develop an agent-based simulation model of interactions on Twitter (now X) to explore how mental health-related content spreads on the platform.
He says the model draws on realistic interaction patterns, observed user behaviour, and insights from natural language processing and intervention strategies.
The tweets, which reflected users’ emotional state (positive, negative, or neutral), were collected before the platform’s rebranding; therefore, the findings apply to the platform’s design, features, and algorithms at that time.
Marais notes that the tweets contained phrases that affirmed the user’s experience of depression in a manner that is not hyperbole, such as “I am diagnosed with depression”, “I am fighting depression” and “I suffer from depression”.
Each phrase represents a dataset of tweets and the corresponding user who posted one or more of these phrases.
The model reflects both how individuals maintain their emotional state over time and how others influence them in their network.
It also showed that repeated exposure to similar negative feelings tends to reinforce and deepen those emotions over time, Marais says.
Improving digital health
He says four stakeholders may benefit from his study.
“Researchers are equipped with tools to study mental health online; practitioners obtain insights into contemporary experiences of depression; platform developers receive evidence-based recommendations for promoting user well-being; and social media users gain a better understanding of their role within algorithm-driven environments.”
His study is a step towards improving digital health and well-being, Marais notes.
It also highlights the urgent need to prioritise mental health considerations in the design of digital platforms.
