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，为研究人员设计和实施新颖的事件检测框架提供了绝佳机会。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。因此，鉴于事件和社交数据本身的挑战性，这些方法往往难以识别未知事件。社交数据（例如推文）具有拼写错误、不完整性、词义歧义、非规范语言以及观点层面多变性等特点。此外，仅利用有限的结构知识来提取演化事件的判别性特征和模式几乎不可行。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。具体而言，我们借助上下文和语言学知识来检测语义相关的推文，从而提升事件检测方法的效能。最终，我们提出的框架为每个事件生成聚类链，以展示事件随时间演化的变化过程。我们进行了大量实验来评估该框架，验证了其在检测和区分未指定社会事件方面的高性能与有效性。