Social platforms have emerged as crucial platforms for disseminating information and discussing real-life social events, offering researchers an excellent opportunity to design and implement novel event detection frameworks. However, most existing approaches only exploit keyword burstiness or network structures to detect unspecified events. Thus, they often need help identifying unknown events regarding the challenging nature of events and social data. Social data, e.g., tweets, is characterized by misspellings, incompleteness, word sense ambiguation, irregular language, and variation in aspects of opinions. Moreover, extracting discriminative features and patterns for evolving events by exploiting the limited structural knowledge is almost infeasible. To address these challenges, in this paper, we propose a novel framework, namely EnrichEvent, that leverages the linguistic and contextual representations of streaming social data. In particular, we leverage contextual and linguistic knowledge to detect semantically related tweets and enhance the effectiveness of the event detection approaches. Eventually, our proposed framework produces cluster chains for each event to show the evolving variation of the event through time. We conducted extensive experiments to evaluate our framework, validating its high performance and effectiveness in detecting and distinguishing unspecified social events.

翻译：摘要：社交平台已成为传播信息和讨论现实社会生活事件的关键载体，为研究者设计和实现新型事件检测框架提供了绝佳机遇。然而，现有方法大多仅利用关键词突发性或网络结构检测未指定事件，因此在应对事件与社交数据本身的复杂性时，常难以识别未知事件。社交数据（如推文）具有拼写错误、信息不完整、词义歧义、语言不规范以及观点维度多变等特征。此外，仅凭有限的结构化知识提取演化事件的判别性特征与模式几乎不可行。针对这些挑战，本文提出名为EnrichEvent的新型框架，该框架利用流式社交数据的语言表征与上下文表征。具体而言，我们通过融合上下文知识与语言学知识，检测语义相关的推文，从而提升事件检测方法的有效性。最终，所提框架为每个事件生成聚类链条，以展示事件随时间演化的动态特征。通过大量实验评估，验证了该框架在检测与区分未指定社交事件方面具有卓越性能与有效性。