The development of artificial intelligence seems to have no limits. Despite the fact that AI’s rapid advancement has begun to potentially permeate every aspect of existence, at times posing a threat to human necessity, certain advancements have provided much-needed respite.
In a recent report, it was revealed that work is underway to develop an artificial intelligence capable of detecting early symptoms of anxiety and depression, a mental health disorder that has become quite prevalent in modern society.
According to the findings published in the journal Language Resources and Evaluation, the AI will also collaborate with the microblogging platform Twitter to perform the same tasks.
Researchers at the University of Sao Paulo (USP) in Brazil reported that the model’s preliminary results indicated the possibility of predicting a person’s likelihood of developing melancholy based solely on their social media acquaintances and followers.
In the first stage of this research, a database containing information about 47 million publicly posted Portuguese texts and the network of connections between 3,900 Twitter users was created and named SetembroBR. Before the survey, these users had reportedly been diagnosed with or treated for mental health issues. During the COVID-19 pandemic, these tweets were collected.
The research also collected tweets from acquaintances and followers because individuals with mental health issues prefer to follow certain accounts, such as discussion forums, influencers, and celebrities who publicly acknowledge their depression.
The second stage, which is still in progress, has yielded some rudimentary results, such as the possibility of determining a person’s likelihood of developing depression based solely on their social media acquaintances and followers, without considering their own posts.
The researchers used deep learning (AI) to create four text classifiers and word embeddings (context-dependent mathematical representations of the relationships between words) using models based on bidirectional encoder representations from transformers (BERT), a machine learning algorithm used for NLP.
These models resemble a neural network that learns contexts and meanings by observing sequential data relationships, such as the relationships between words in a sentence. The training input consisted of 200 randomly selected tweets from each user.
The researchers discovered that BERT performed the best among the models for predicting depression and anxiety. Due to the fact that the models analysed sequences of words and complete sentences, it was possible to observe, for instance, that individuals with depression tended to write about subjects related to themselves, using first-person pronouns and phrases, as well as mortality, crises, and psychology.
