Tell me what you post, and I'll tell you how close you are to burnout

Tell me what you post, and I'll tell you how close you are to burnout

SNSF researchers have developed a technique for analyzing texts on social networks using AI to identify physical and mental exhaustion

Burnout can be revealed through automatic analysis of users' posts on social networks
Burnout can be revealed through automatic analysis of users' posts on social networks

Burnout refers to a state of profound physical and mental exhaustion. It is difficult to detect because its symptoms can easily overlap with those of depression and anxiety. But Artificial Intelligence may hold the key to better recognizing it.
In an article recently published in the journal Frontiers in Big Data, a team of researchers funded by the Swiss National Science Foundation (SNSF) described a new technique that uses natural language processing to detect burnout.
The latter is typically diagnosed by means of psychological tests which take the form of a response rating scale. For example: “I'm exhausted at the end of the day: never/sometimes/every day”.
However, this type of verification has significant limitations. For example, some people are reluctant to select answers "never" od "everyday" or are tempted to lie to influence results.
More comprehensive tests consisting of open-ended questions could also be used to detect burnout.
These controls collect more relevant information, but require extensive analysis. Consequently, they are rarely used in practice.

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A technique based on the scrutiny of texts, especially of Reddit

This is the problem that Mascha Kurpicz-Briki, professor of data engineering at the Bern University of Applied Sciences in Biel, and her team wanted to address. The squad included Ghofrane Merhbene, Sukanya Nath and Alexandre Puttick.
To do so, they used AI-powered natural language processing methods to identify indicators of burnout.
The method successfully identified 93 percent of burnout cases. Kurpicz-Briki says: “Natural language processing effectively detects burnout and does so relatively efficiently, which is very promising”.
For this work, she and her team analyzed texts from Reddit, a social media platform that serves as a forum for topically organized discussions.
The data engineering professor at the BFH in Biel has built a database of more than 13.000 free text samples.
Some of them have been culled from discussions related to burnout, while others have been culled from forums on a number of other topics.

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Burnout can be revealed through automatic analysis of users' posts on social networks
Burnout can be revealed through automatic analysis of users' posts on social networks

Models trained on different data for a good diagnostic method

Mascha Kurpicz-Briki then applied machine learning to develop a technique for determining whether a text contains burnout indicators.
In particular, it ranked text samples first. The contents of burnout-related discussion threads were manually categorized to exclude those where “burnout” referred to something else.
Texts from other discussion threads not related to mental health were labeled as not related to burnout.
Based on these examples, the professor at the Bern University of Applied Sciences trained several models.
Each used different approaches to determine whether or not a previously unseen text from the model contained indicators of burnout.
The models were then brought together to create the diagnostic method, which proved to be very effective.
The results are promising, but need to be confirmed.
As a next step, collaboration with medical experts will be necessary to verify the conclusions of this preliminary survey on real burnout cases and on a representative sample of the population. And it should be borne in mind that the data collected on Reddit is anonymous.

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Burnout can be revealed through automatic analysis of users' posts on social networks
Burnout can be revealed through automatic analysis of users' posts on social networks