The rise of social media has amplified both the visibility and vulnerability of marginalized communities, particularly the transgender population in South Asia. While hate speech detection has seen considerable progress in high resource languages like English, under-resourced and code mixed languages such as Roman Urdu remains significantly understudied. This paper presents a novel Roman Urdu dataset derived from Instagram comments on transgender related content, capturing the intricacies of multilingual, code-mixed, and emoji-laden social discourse. We introduce a transphobic slur lexicon specific to Roman Urdu and a semantic emoji taxonomy grounded in contextual usage. These resources are utilized to perform fine-grained classification of sentiment and hate speech using both traditional machine learning models and transformer-based architectures. The findings show that our custom-trained BERT-based models, Senti-RU-Bert and Hate-RU-Bert, best performance, with F1 scores of 80.39% for sentiment classification and 77.34% for hate speech classification. Ablation studies reveal consistent performance gains when slur and emoji features are included.

As the anthropomorphization of AI in public discourse usually reflects a complex interplay of metaphors, media framing, and societal perceptions, it is increasingly being used to shape and influence public perception on a variety of topics. To explore public perception and investigate how AI is personified, emotionalized, and interpreted in public discourse, we develop a custom multi-labeled dataset from the title and description of YouTube videos discussing artificial intelligence (AI) and large language models (LLMs). This was accomplished using a hybrid annotation pipeline that combined human-in-the-loop validation with AI assisted pre-labeling. This research introduces a novel taxonomy of narrative and epistemic dimensions commonly found in social media content on AI / LLM. Employing two modeling techniques based on traditional machine learning and transformer-based models for classification, the experimental results indicate that the fine-tuned transformer models, particularly AnthroRoBERTa and AnthroDistilBERT, generally outperform traditional machine learning approaches in anthropomorphization focused classification.