The concept of wellness, as proposed by Halbert L. Dunn, recognizes the importance of multiple dimensions, such as social and mental well-being, in maintaining overall health. Neglecting these dimensions can have long-term negative consequences on an individual's mental well-being. In the context of traditional in-person therapy sessions, efforts are made to manually identify underlying factors that contribute to mental disturbances, as these factors, if triggered, can potentially lead to severe mental health disorders. Our research focuses on introducing a meticulous task aimed at identifying indicators of wellness dimensions and detecting their presence in self-narrated human writings on the Reddit social media platform. We have mentioned Ethics and Broader Impact.

The Language Model Models (LLMs) have demonstrated their ability to process and understand natural language inputs accurately. This indicates that LLMs are capable of superior natural language processing capabilities. The GPT embeddings are words generated from the GPT backend of the ChatGPT developed by OpenAI, which can produce accurate outputs for user inputs. In this paper, we generate GPT embeddings and apply machine learning algorithms for fake news prediction and sentiment analysis. The proposed method produces remarkable results with an improvement of approximately 12.59% in accuracy compared to traditional embeddings. For fake news detection, GPT embeddings with SVM outperformed LSTM with Glove embeddings. We evaluated 10 machine learning models using 4 versions of GPT embeddings, namely Ada, Babbage, Curie, and Davinci. The sentiment analysis execution resulted in an impressive accuracy of 98.6%. We made our embeddings publicly available for both datasets. We believe that this is a valuable contribution since generating such embeddings requires access to GPT, which is not freely available to researchers.

Everyone now has internet and social media access, making it simple to get information and news. On the other hand, there are fake news articles, also. It not only makes difficult for the public to find their truthfulness but also misleads them. Consequently, developing intelligent systems for separating news is critical. In this paper, four deep learning techniques such as Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks-BiLSTM (CNN-BiLSTM) are used to detect fake news. We trained these models on three large datasets, namely, CoAID, GossipCop, and PolitiFact, that include fake news from social media and news articles. The text is divided into two categories: fake and real. We also trained and compared the results with ten well-known machine-learning classifiers, e.g., Naive Bayes, Decision Tree, Support Vector Machine, K-Nearest Neighbors Classifier, etc. The experimental results indicate that the deep learning-based BERT method achieves better accuracy and F1-score of 91.94\% and 92.06\% for PolitiFact, 99.09\% and 98.38\% for CoAID, 85.59\% and 82.40\% for GossipCop, respectively. These results suggest that deep learning algorithms provide favorably accurate results and outperform machine learning models in terms of accuracy.