Textual Analysis of Facebook Communities Related to Depression

Because depression is a debilitating illness affecting individuals of all ages, genders, and socioeconomic groups, being aware of its signs helps speed up the process by which patients seek care from specialists and improve their chances of recovery. More individuals are exchanging knowledge on health, illnesses, and treatments because of the rise of online social networks. This article outlines the findings of an analytical study that used data from Facebook communities that deal with depression between June and December of 2022. We analyze the importance of these experiments using methods for analyzing and interpreting textual data, such as sentiment analysis and topic modeling. Our method uses the Crowdtangle system to capture open data in Facebook and Linguistic Inquiry and Word Count (LIWC) to identify specific properties of depression texts. With these tools, we assess the psychological characteristics of social media writings based on pre-processed LIWC dictionaries. Understanding these communities may help in the creation of new products and artifacts, enhancing the understanding of how depressive users behave in addition to the creation of regulations for nearby patients and medical professionals. Also, employing textual analysis techniques such as word frequency and sentiment analysis, we provide the findings of textual analysis relating to depression disorder in Facebook networks. The results support earlier research showing that posts about depression are more authentic, intimate, and have a negative discourse nature.