Our study reveals that posts more directly intended as advertising generate more negative results, while there are differences between the elements capable of generating more likes and more comments, respectively: likes are more general in nature, while comments are more specifically linked to the Berlin brand.
All posts generated over the course of a year on the German capital’s official Instagram account were encoded, and the characteristics of those that generated the most interaction with users in the form of likes and comments were analysed. This article analyses the role of one of the most booming social networks, Instagram, applied to the case of Berlin, a leading tourist city. It is becoming ever more important to create and transmit an image capable of stimulating high levels of engagement. Public authorities have been forced to become involved in these new realities, adapting their promotion channels to tourists’ new behaviour patterns and carefully cultivating interactions with them. Interactions between tourism and social networks are among the most notable phenomena of recent times, generating new approaches, in terms of both analyses of and policies for tourism promotion. Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom identification task and by 2.55% on the gender identification task. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models. Specifically, we adapt a knowledge-assisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. We propose GeM, a novel task-adaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. We collect a corpus of $150k$ items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease).
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