TFE ‐ AOD : Detecting Depressed Radical Co‐Occurrence Individuals in Social Networks by Combining Temporal Fine‐Grained Emotions and Aggressive Outbursts

ABSTRACT Depressed individuals with co‐occurring radical tendencies pose greater potential risk than general depression sufferers. Previous studies largely detect depression sufferers or classify depressive symptoms in social networks, ignoring the radicalisation process under long‐term complex emotions. Thus, we propose TFE‐AOD, a property‐specific method that incorporates temporal fine‐grained emotions into aggressive outbursts to enhance detection of depression individuals with radical tendencies. The proposed method first classifies fine‐grained emotions via an attention‐based Transformer, introducing irony (e.g., self‐mockery, sarcasm, struggle and powerlessness) to capture explicit depressive traits. It then combines fine‐grained emotions with nocturnal activity to identify depression in long text sequences, leveraging time‐dependent localisation to align with symptom accumulation. Nocturnal activity reflects the higher nighttime complaint propensity of depressed individuals. Finally, a knowledge‐enhanced pretrained model detects co‐occurrence by incorporating aggressive outburst features (alcohol abuse, drug dependency, covert hateful mentality, violence exposure). Experiments on Sina Weibo (SWDD) and a self‐constructed Douban dataset show that TFE‐AOD outperforms state‐of‐the‐art models in detecting this co‐occurrence.