Psychological Stress Detection from Social Media Data using a Novel Hybrid Model

An evolutionary and insightful graph neural network based hybrid model for deciphering tenacious stress detection of humans using facial emotion recognition: coinage to affective computing

“…Similarly, in Self-Report Method, few major shortcomings are: a) These methods rely on individual's own perceptions and interpretations of their stress levels (Maltman et al 2023); b) Mainly individual's subjective experiences are captured and this may not provide a complete understanding of the physiological responses associated with stress (Witte et al 2021); c) Stress perception may vary depending on situational context, mood, or personal factors, leading to inconsistent reporting; d) the accuracy and reliability can be compromised when individuals are asked to recall stress experiences from the past, particularly for longterm or chronic stress assessments; e) these methods often rely on periodic assessments or surveys, which may not capture stress fluctuations in real-time (Theon et al 2023), f) the diversity of available questionnaires and scale make it challenging to establish a universally applicable measure for stress detection. Behavioral methods also possess their own critics when aided via human: a) these observations can be subjective and prone to interpretation biases (Hajera and Ali 2018), b) behavioral responses to stress can vary significantly between individuals, c) behavioral cues associated with stress can be influenced by the context and environment in which they occur; d) behavioral cues can be present in any psychological changes as well; e) this analysis includes many technical complexities in implementation (Nijhawan et al 2022). Behavioral methods have their shortcomings majorly when they are curated by human interventions.…”