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：社交平台已成为传播信息和讨论现实社会事件的关键平台，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来检测未指定事件，因而在事件本质的挑战性与社交数据的复杂性面前，往往难以有效识别未知事件。社交数据（如推文）具有拼写错误、信息不完整、词义歧义、语言不规范及观点表达多样等特点。此外，仅利用有限的结构化知识来提取演化事件的判别性特征与模式几乎不可行。为应对这些挑战，本文提出一种名为EnrichEvent的新型框架，该框架利用流式社交数据的语言学和上下文表征。特别地，我们通过上下文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效能。最终，本框架为每个事件生成聚类链，以展示事件随时间的演化轨迹。我们通过大量实验评估了该框架，验证了其在检测与区分未指定社会事件方面的高性能与有效性。