The call for socially aware language technologies highlights the need for natural language processing (NLP) systems to incorporate social awareness to address issues like bias, evaluation, and risks. While NLP has made significant progress in formal linguistic tasks, it has lagged in integrating social awareness, which involves understanding social factors, context, and implications. Socially aware NLP aims to enhance applications by making them more natural, helpful, and safe, and to open new possibilities. The paper argues that substantial challenges remain for NLP to develop social awareness, and that we are at the beginning of a new era for the field. Socially aware NLP involves three key aspects: social factors, social interaction, and social implication. Social factors include speaker and receiver characteristics, social relations, context, social norms, culture, and communicative goals. Social interaction involves understanding the dynamics of human-human and human-AI interactions, while social implication refers to the broader impact of NLP systems on society, including potential harms like misinformation and bias. The paper discusses the importance of integrating social awareness into NLP development, emphasizing the need for diverse datasets, ethical considerations, and iterative design. It also highlights the challenges in defining and implementing social awareness, and the need for new tasks, metrics, and approaches to evaluate socially aware NLP systems. The paper calls for a unified subfield of socially aware language technologies to ensure that language processing advances are both technically sophisticated and socially aware. It also emphasizes the ethical considerations in developing such systems, including privacy, bias, and the potential for misuse. The future of socially aware NLP is seen as a critical step towards responsible AI that aligns with human society and values.The call for socially aware language technologies highlights the need for natural language processing (NLP) systems to incorporate social awareness to address issues like bias, evaluation, and risks. While NLP has made significant progress in formal linguistic tasks, it has lagged in integrating social awareness, which involves understanding social factors, context, and implications. Socially aware NLP aims to enhance applications by making them more natural, helpful, and safe, and to open new possibilities. The paper argues that substantial challenges remain for NLP to develop social awareness, and that we are at the beginning of a new era for the field. Socially aware NLP involves three key aspects: social factors, social interaction, and social implication. Social factors include speaker and receiver characteristics, social relations, context, social norms, culture, and communicative goals. Social interaction involves understanding the dynamics of human-human and human-AI interactions, while social implication refers to the broader impact of NLP systems on society, including potential harms like misinformation and bias. The paper discusses the importance of integrating social awareness into NLP development, emphasizing the need for diverse datasets, ethical considerations, and iterative design. It also highlights the challenges in defining and implementing social awareness, and the need for new tasks, metrics, and approaches to evaluate socially aware NLP systems. The paper calls for a unified subfield of socially aware language technologies to ensure that language processing advances are both technically sophisticated and socially aware. It also emphasizes the ethical considerations in developing such systems, including privacy, bias, and the potential for misuse. The future of socially aware NLP is seen as a critical step towards responsible AI that aligns with human society and values.