Health & Wellbeing

AI system detects loneliness in natural speech patterns

AI system detects loneliness in natural speech patterns
An AI system can identify degrees of loneliness in older adults by analyzing natural language patterns
An AI system can identify degrees of loneliness in older adults by analyzing natural language patterns
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An AI system can identify degrees of loneliness in older adults by analyzing natural language patterns
An AI system can identify degrees of loneliness in older adults by analyzing natural language patterns

A new proof of concept study, led by researchers at University of California San Diego School of Medicine, has demonstrated how speech-analyzing artificial intelligence tools can effectively predict the level of loneliness in older adults.

Natural language processing (NLP) is an umbrella term encompassing a variety of techniques that process or analyze large volumes of unstructured natural speech and text. As artificial intelligence and machine learning systems have advanced, a number of fascinating preliminary studies have begun to suggest conditions such as psychosis, PTSD, bipolar disorder and depression may all be detected just by analyzing a person’s natural speech.

Now, a team of researchers is investigating whether these NLP tools can detect loneliness, a growing health concern that has been described as a bigger factor on premature mortality than obesity. Ellen Lee, senior author on the new research, suggests loneliness is a particularly difficult psychiatric condition to measure and because doctors generally struggle to quantify loneliness in patients there is a pressing need for some kind of objective measure.

"Most studies use either a direct question of 'how often do you feel lonely,' which can lead to biased responses due to stigma associated with loneliness, or the UCLA Loneliness Scale, which does not explicitly use the word 'lonely,'" explains Lee. "For this project, we used natural language processing or NLP, an unbiased quantitative assessment of expressed emotion and sentiment, in concert with the usual loneliness measurement tools."

The new study recruited 80 older adults. Each subject was evaluated using conventional loneliness assessments as well as completing a longer, more conversational, semi-structured interview lasting up to 90 minutes.

The interviews were transcribed and then analyzed with the help of a natural language system developed by IBM. As well as detecting loneliness in subjects not picked up by conventional assessments, the system uncovered differences in the way men and women talk about loneliness.

"NLP and machine learning allow us to systematically examine long interviews from many individuals and explore how subtle speech features like emotions may indicate loneliness,” says first author Varsha Badal. “Similar emotion analyses by humans would be open to bias, lack consistency, and require extensive training to standardize.”

The AI system reportedly could qualitatively predict a subject’s loneliness with 94 percent accuracy. The more lonely a person was feeling, the longer their responses were to direct questions regarding loneliness. The researchers even suggest the presence of a kind of “lonely speech” pattern could be used in the future to monitor the well-being of older subjects.

The men in the study were found to use more fearful and joyful words in their longer conversational interviews, while the women studied were more likely to explicitly vocalize feelings of loneliness. Even without incorporating these kinds of NLP tools in current practice, the researchers suggest the study’s findings offer clinicians important insights into the different ways men and women express loneliness.

The next stage of the research will be to combine other sensor data into the assessments (such as GPS tracking and sleep data) to personalize each individual finding. Plus, the system will need to be tested on larger, more diverse, populations to fine-tune its accuracy.

“Eventually, complex AI systems could intervene in real-time to help individuals to reduce their loneliness by adopting in positive cognitions, managing social anxiety, and engaging in meaningful social activities,” the researchers boldly conclude in the new study.

The study was published in The American Journal of Geriatric Psychiatry.

Source: UC San Diego Health

I can tell you from past experience that loneliness is crazy painful. I can see so many more people suffering now with this pandemic than ever before.
Nagi Mato
Personally, I think "older adults" needs to be quantified. I'm 60 and I don't in the least feel like and "older adult". Heck, most of the time I don't even feel like an adult! Just ask my wife! LOL!
Yes Guzman, the Covid pandemic has worsened peoples' problems, but even if it wasn't part of the picture, loneliness would still be ever-present. Makes me wonder if folks were much less lonely 50 years ago than today, and could get worse in the future. All the gadgets in our lives can't be helping. It's easier to connect but strangely out of touch. So modern technology is rushing to bring robots in our lives. Good luck. We are more materialist than ever. We have been taught (programmed) to constantly wish for the next big thing.

When AI perfects these NLP techniques the health authorities will be busy spotting and treating those in need. Perhaps a cure involving cellphone and television withdrawal and a trip to "companion centers" in the countryside will be prescribed.
I'm not convinced that they're truly measuring loneliness. They admit that there's no quantitative way to measure loneliness, so their assessment techniques and questions will have various assumptions and biases. The AI is measuring something, but exactly what isn't really clear. A reliable method for identifying people who claim to be lonely and then go on to commit suicide, or suffer other heath issues because of it, would be useful, but like any tool, I expect it will get misused and misunderstood.