## 2 Related Works  
The rapid growth of multimedia, social media, and IT tools, especially in the entertainment sector, attracts growing numbers of users. Social media like Facebook, Twitter, or Instagram give users a chance to have fun, relax, express themselves, or even earn money. On these platforms, people exchange their opinions and share ideas, problems, and lifiebacks. It is where people give each other support that they do not receive in real life. However, the massive use of social networking sites has also become a new area for many researchers who use textual information to detect the mental condition of users. As some researchers claim [19; 20; 21], textual information can reveal one's mental condition. What a person says and what kind of language register he/she uses can be related to their current state [22]. Modern NLP tools facilitate the collection of vast amounts of textual data from various social sources, extracting valuable information, which can be analyzed and validated to detect the desired information. Some interesting research concerns applying NLP and machine learning, including the Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Multilayer Perceptron (MLP), Logistic Regression (LR), and Decision Tree (DT) algorithms, to the identification identifying health information on social media sites [23; 24]. For example, Twitter was the object of interest of Spanish researchers [25], who created the SAD (Spanish Anorexia Detection) based on records collected from this social networking site. The study concerned detecting anorexia in tweets from numerous accounts referring to anorexia and non-anorexia. Using machine learning approaches, they tried to automatically detect anorexia symptoms in the corpus. The authors aimed to establish linguistics statistics (the number of words, stop words, tweets, and the number of users) and then to extract the information on verbs, nouns, adjectives, and adverbs. As to grammatical tagging, the study revealed that anorexia nervosa tweets are characterized by fewer words and more negative language. Another interesting approach [26] refers to developing characteristic language traits among anorexic patients who, according to the authors, share some similar psychological features like anxiety, depression, controlling the amount of food, obsessive thinking about the body, and a disturbed body image. The participants answered a few questions about their bodies and their way of spending their spare time. The task also included a picture description, which was the crucial step as it helped to detect the most features differentiating anorexia patients from their healthy peers. The study analyzed linguistic features, including Lexical and Syntactic Indexes, as well as data derived from the LIWC-22 software, to compare the individuals with anorexia group (ANG) and a control group during different language tasks. The authors revealed that the ANG exhibited a higher content density, lower personal deixis frequency, and increased lexical richness in certain tasks. Syntactically, the ANG showed a lower complexity in noun phrases and overall syntactic complexity.  
Studies using social media accounts to find the specific traits of AN language have been increasing in popularity. In [27], the authors used Yahoo! answers to examine the linguistic characteristics of various question types related to eating disorders. They used a strategy that involved employing diverse linguistic analysis methods to study the language employed in individuals' inquiries to reveal the cognitive, social, and emotional dimensions that underlie their needs. Through term frequency analysis, Part-of-Speech (POS) analysis, and sentiment analysis, they investigated the linguistic content, stylistic elements, and emotional expressions in two categories of questions, namely informational and socio-emotional ones.