Relation extraction (RE) is a vital task within natural language processing. Previous works predominantly focus on extracting relations from plain text. However, with the evolution of communication habits, many individuals employ symbolic representations, e.g. emoticons, to convey nuanced information. This shift in communication prompts a pertinent question: How do emoticons impact the performance of RE models? In response, we introduce a novel Emoticon-infused Relation Extraction (EmoRE) task and present the EmoRED dataset, the first human-annotated dataset specifically designed for relation extraction in text containing emoticons. EmoRED samples encompass various emoticon types, each serving a unique role—some aid in entity identification, while others facilitate relation comprehension. Alongside dataset construction, we conduct extensive experiments to scrutinize behaviors of both supervised models and large language models within emoticon-rich text. Experimental results reveal significant variations in the behavior of existing models in the EmoRE task, highlighting the need for future models that can consistently harness emoticon information.