Suicidal ideation prediction based on social media posts using a GAN-infused deep learning framework with genetic optimization and word embedding fusion

This study tackles the pressing issue of predicting suicidal tendencies on Twitter by introducing an inventive methodology that integrates Generative Adversarial Networks (GAN), Deep Learning methodologies, Word Embedding Fusion, and Genetic Algorithms. The framework is devised to capitalize on the unique strengths of each element, resulting in a more resilient and precise predictive model. The process initiates with the aggregation of Twitter posts, which undergo preprocessing and transformation into word embeddings. These embeddings establish the groundwork for subsequent stages. The utilization of GAN comes into play for generating synthetic data, enriching the dataset’s diversity and volume—an essential aspect for the effective training of deep learning models. Concurrently, a deep learning architecture is employed to decipher intricate patterns within the data. The harmonization of GAN-generated samples and genuine data optimizes the model's performance, ensuring a comprehensive comprehension of features indicative of suicidal ideation. The integration of Word Embedding Fusion further enhances the model by amalgamating information from diverse embedding techniques, leveraging their distinct perspectives. This step boosts the model’s capacity to interpret and generalize patterns, thereby contributing to an augmented prediction accuracy. The inclusion of Genetic Algorithms introduces an evolutionary optimization layer to the process. Genetic Algorithms play a crucial role in fine-tuning model parameters, optimizing overall performance by iteratively evolving candidate solutions. This not only enhances the model's predictive capabilities but also facilitates adaptability to the varied and dynamic linguistic expressions found on Twitter. Our work distinguishes itself from existing methodologies through this comprehensive approach that encompasses GAN, Deep Learning, Word Embedding Fusion, and Genetic Algorithm. The amalgamation of these techniques amplifies the predictive accuracy and resilience of the model, exhibiting promising results in the challenging task of predicting suicidal tendencies on Twitter. The adaptability of the proposed framework positions it as a potent tool for real-world applications, aiding in the timely identification and intervention in potential cases of self-harm.