Ideologies, emotions, and anti-immigrant stance in the context of türkiye: a social media analysis through natural language processing (NLP) techniques

In recent years, global migration has been on the rise, leading to intensified debates and growing anti-immigrant sentiment in many parts of the world. Türkiye, as one of the leading host countries for immigrants and refugees, has become a focal point in these discussions. Despite extensive research in Western Europe and the U.S., where right-wing ideologies are often linked with anti-immigrant views, the dynamics in non-Western countries like Türkiye are less explored. This study aims to investigate whether there is an interaction between ideological positions and emotional responses in shaping public stances toward immigration in Türkiye, incorporating Affective Intelligence Theory (AIT), which emphasizes the role of emotions—particularly fear, anxiety, and anger—in shaping political judgment and behavior. The research utilizes social media data sourced from Twitter to analyze the interaction between ideologies and emotions in shaping anti-immigrant sentiment. Natural Language Processing (NLP) techniques, including fine-tuned pre-trained BERT-based models like BERTurk and TurkishBERTweet, are employed to classify immigration-related tweets and detect pro- and anti-immigrant stances. The findings challenge the conventional association between right-wing ideologies and anti-immigrant sentiment commonly observed in Western Europe and the U.S., revealing a more complex range of views across both right- and left-wing ideologies in Türkiye. The study further highlights how emotions, as explained by Affective Intelligence Theory, can either reinforce or shift ideological stances on immigration. This interaction between emotions and ideologies provides deeper insights into the complex dynamics shaping immigration discourse in Türkiye, offering a nuanced understanding of public sentiment.