Advancement with artificial intelligence seems to know no bounds. While AI's rapid progress has potentially began entering every walk of existence, at times threatening human necessity, some advancement has come as a much needed relief.
In a recent report it has been made known that work is in progress for creating an AI that would detect early signs of anxiety and depression- a mental health disorder that has found quite prevalence in contemporary times.
The findings that have been published in the journal Language Resources and Evaluation mentions that the AI will also collaborate with micro-blogging platform Twitter to do the same.
Researchers at the University of São Paulo (USP) in Brazil said that preliminary findings from the model suggested the possibility of detecting the likelihood of a person developing depression based solely on their social media friends and followers.
The first step in this study involved constructing a database, called SetembroBR, of information relating to a corpus of 47 million publicly posted Portuguese texts and the network of connections between 3,900 Twitter users. These users had reportedly been diagnosed with or treated for mental health problems before the survey. The tweets were collected during the COVID-19 pandemic.
Because people with mental health problems tended to follow certain accounts such as discussion forums, influencers and celebrities who publicly acknowledge their depression, the study also collected tweets from friends and followers.
The second step, still in progress, has provided some preliminary findings, such as the possibility of detecting the likelihood of a person developing depression based solely on their social media friends and followers, without taking their own posts into account.
Following pre-processing of the corpus to maintain original texts by removing non-standard characters, the researchers deployed deep learning (AI), to create four text classifiers and word embeddings (context-dependent mathematical representations of relations between words) using models based on bidirectional encoder representations from transformers (BERT), a machine learning algorithm employed for NLP.
These models correspond to a neural network that learns contexts and meanings by monitoring sequential data relationships, such as words in a sentence. The training input consisted of a sample of 200 tweets selected at random from each user.
The researchers found that among the models, BERT performed best in terms of predicting depression and anxiety. They said that because the models analysed sequences of words and complete sentences, it was possible to observe that people with depression, for example, tended to write about subjects connected to themselves, using verbs and phrases in the first person, as well as topics such as death, crisis and psychology.