Some researchers from Penn Medicine and Stony Brook University conducted an interesting research, according to which the posts they make users in social media, can indicate the existence of diseases before a clinical diagnosis is made. The results of the survey have shown that people use words and dialects in their posts that betray diseases such as diabetes, depression, anxiety, and psychosis.
999 people participated in the survey. Researchers analyzed 949.530 posts, containing at least 500 words, using one tool processing of the language. Researchers looked at posts for 21 medical conditions and it was found that 21 illness can indeed be predicted from posts.
People who use words such as "family", "god" and "prayer" were 15 times more likely to suffer from diabetes.
Also, one thing to expect is that people who are addicted to alcohol are more likely to use words like "bottle", "drink", and "drunk".
People suffering from an anxiety disorder cite words related to physical symptoms such as "head", "stomach" and "pain" in their posts.
The researchers, of course, emphasize that they do not have any illness all the people who report such words. However, those who report it, are more likely be diagnosed with a related condition.
The results of this research could be considered as a counsel for doctors to analyze posts in social media in order to detect a person's medical condition in advance.
Raina Merchant, principal research author and director of Penn Medicine, said that if one wants to lose weight, for example, his doctor should know his eating habits and the degree of exercise. A good way to get it access in this information is to take a look at posts in social half. Many times, when people have a problem, they present it a little better. So the doctor's reliance on the patient's words may not be enough. Social media better show people's habits.
Of course, the fact that researchers were able to diagnose a disease through posts in Facebook, before doing a clinical diagnosis, means that most people give too much information on social networking platforms.
If you are interested in research, you can find its full findings here..