In addition to moral foundations, several other user-level attributes could be integrated to enhance stance detection performance. These attributes include:
Personality Traits: Utilizing frameworks like the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) can provide insights into how individual differences influence stance expression. For instance, users with high openness may express more progressive stances, while those high in conscientiousness may lean towards traditional views.
Emotional States: Incorporating emotional intelligence and sentiment analysis can help models understand the emotional context behind stances. By analyzing the emotional tone of a user's posts, models can better predict their stance based on the emotional resonance of the content.
Cultural Background: Understanding the cultural context of users can significantly impact stance detection. Different cultures may prioritize different moral foundations or have varying norms around expressing opinions. Incorporating cultural indicators can help models adapt to these differences.
Social Influence: User interactions, such as the influence of peers or social networks, can shape stances. Analyzing the social dynamics of users, including their connections and the stances of their social circles, can provide valuable context for stance detection.
Historical Behavior: Tracking a user's historical stances and changes over time can help models identify patterns and predict future stances more accurately. This longitudinal approach can reveal shifts in opinion that are influenced by external events or personal experiences.
By integrating these attributes, stance detection models can achieve a more nuanced understanding of user behavior, leading to improved accuracy and relevance in predicting stances on various topics.