When Neguine Rezaii first moved to the United States a decade ago, she hesitated to tell people she was Iranian. Instead, she would use Persian. “I figured that people probably wouldn’t know what that was,” she says.
The linguistic ambiguity was useful: she could conceal her embarrassment at the regime of Mahmoud Ahmadinejad while still being true to herself. “They just used to smile and go away,” she says. These days she’s happy to say Iranian again.
We don’t all choose to use language as consciously as Rezaii did–but the words we use matter. Poets, detectives, and lawyers have long sifted through people’s language for clues to look for their motives and inner truths. Psychiatrists, too: perhaps psychiatrists especially. After all, while medicine now has a battery of tests and technical tools for diagnosing physical ailments, the chief tool of psychiatry is the same one employed centuries ago: the question “So how do you feel today?” Simple to ask, maybe–but not to answer.
“In psychiatry we don’t even have a stethoscope,” says Rezaii, who is now a neuropsychiatry fellow at Massachusetts General Hospital. “It’s 45 minutes of talking with a patient and then making a diagnosis on the basis of that conversation. There are no objective measures. No numbers.”
There’s no blood test to diagnose depression, no brain scan that can pinpoint anxiety before it happens. Suicidal thoughts cannot be diagnosed by a biopsy, and even if psychiatrists are deeply concerned that the covid-19 pandemic will have severe impacts on mental health, they have no easy way to track that. In the language of medicine, there is not a single reliable biomarker that can be used to help diagnose any psychiatric condition. The search for shortcuts to finding corruption of thought keeps coming up empty–keeping much of psychiatry in the past and blocking the road to progress. It makes diagnosis a slow, difficult, subjective process and stops researchers from understanding the true nature and causes of the spectrum of mental maladies or developing better treatments.
But what if there were other ways? What if we didn’t just listen to words but measure them? Could that help psychiatrists follow the verbal clues that could lead back to our state of mind?
“That is basically what we’re after,” Rezaii says. “Finding some behavioral features that we can assign some numbers to. To be able to track them in a reliable manner and to use them for potential detection or diagnosis of mental disorders.”
In June 2019, Rezaii published a paper about a radical new approach that did exactly that. Her research showed that the way we speak and write can reveal early indications of psychosis, and that computers can help us spot those signs with unnerving accuracy. She followed the breadcrumbs of language to see where they led.
People who are prone to hearing voices, it turns out, tend to talk about them. They don’t mention these auditory hallucinations explicitly, but they do use associated words–“sound,” “hear,” “chant,” “loud”–more often in regular conversation. The pattern is so subtle you wouldn’t be able to spot the spikes with the naked ear. But a computer can find them. And in tests with dozens of psychiatric patients, Rezaii found that language analysis could predict which of them were likely to develop schizophrenia with more than 90% accuracy, before any typical symptoms emerged. It promised a huge leap forward.
In the past, capturing information about somebody or analyzing a person’s statements to make a diagnosis relied on the skill, experience, and opinions of individual psychiatrists. But thanks to the omnipresence of smartphones and social media, people’s language has never been so easy to record, digitize, and analyze. And a growing number of researchers are sifting through the data we produce–from our choice of language or our sleep patterns to how often we call our friends and what we write on Twitter and Facebook–to look for signs of depression, anxiety, bipolar disorder, and other syndromes.
To Rezaii and others, the ability to collect this data and analyze it is the next great advance in psychiatry. They call it “digital phenotyping.”
Weighing your words
In 1908, the Swiss psychiatrist Eugen Bleuler announced the name for a condition that he and his peers were studying: schizophrenia. He noted how the condition’s symptoms “find their expression in language” but added, “The abnormality lies not in language itself but what it has to say.”
Bleuler was among the first to focus on what are called the “negative” symptoms of schizophrenia, the absence of something seen in healthy people. These are less noticeable than the so-called positive symptoms, which indicate the presence of something extra, such as hallucinations. One of the most common negative symptoms is alogia, or speech poverty. Patients either speak less or say less when they speak, using vague, repetitive, stereotypical phrases. The result is what psychiatrists call low semantic density.
Low semantic density is a telltale sign that a patient might be at risk of psychosis. Schizophrenia, a common form of psychosis, tends to develop in the late teens to early 20s for men and the late 20s to early 30s for women–but a preliminary stage with milder symptoms usually precedes the full-blown condition. A lot of research is carried out on people in this “prodromal” phase, and psychiatrists like Rezaii are using language and other measures of behavior to try to identify which prodromal patients go on to develop full schizophrenia and why. Building on other research projects suggesting, for example, that people at high risk of psychosis tend use fewer possessive pronouns like “my,” “his,” or “ours,” Rezaii and her colleagues wanted to see if a computer could spot low semantic density.
The researchers used recordings of conversations made over the last decade or so with two groups of schizophrenia patients at Emory University. They broke each spoken sentence down into a series of core ideas so that a computer could measure the semantic density. The sentence “Well, I think I do have strong feelings about politics” gets a high score, thanks to the words “strong,” “politics,” and “feelings.”