The current research landscape in the field of online content analysis and detection is witnessing significant advancements, particularly in the areas of harmful content identification and emerging trend detection. Researchers are increasingly leveraging multimodal large language models (MLLMs) and large language models (LLMs) to enhance the accuracy and efficiency of detecting harmful content across various platforms, including social media and video sharing sites. These models are being employed not only for classification tasks but also as alternative annotators, significantly reducing the reliance on human annotation and mitigating the mental toll associated with it. Additionally, there is a growing focus on developing domain-agnostic and neurosymbolic approaches that can adapt to the evolving nature of language, particularly in dynamic environments like social media during health crises. These methods integrate neural networks with symbolic knowledge sources, offering improved performance in tasks such as mental health sentiment analysis. Furthermore, neural topic modeling is being advanced through the integration of LLMs, enhancing the interpretability and coherence of discovered topics while maintaining computational efficiency. Overall, the field is moving towards more sophisticated, adaptive, and interpretable models that can handle the complexities and rapid changes inherent in online content.