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<p>Social platforms have emerged as crucial platforms for distributing information and discussing social events, offering researchers an excellent opportunity to design and implement novel event detection frameworks. Identification of <i>unspecified events</i>, detection of events without using any prior knowledge, enables governments, aid agencies, and experts to respond swiftly and effectively to unfolding situations, such as natural disasters, by assessing severity and optimizing aid delivery. In addition, it provides valuable insights for marketing, commerce, and financial markets. Misspellings, incompleteness, word sense ambiguation, and irregular language characterize social data. While discussing an ongoing event, users share different opinions and perspectives based on their prior experience, background, and knowledge. Prior works primarily leverage tweets’ lexical and structural patterns to capture users’ opinions and views about events. However, tweets’ lexical and structural aspects do not perfectly reflect their views. In addition, extracting discriminative features and patterns for evolving events by exploiting the limited structural knowledge is almost infeasible. In this study, we propose an end-to-end novel framework, <i>EnrichEvent</i>, to identify unspecified events from streaming social data. In addition to lexical and structural patterns, we leverage contextual knowledge of the tweets to enrich their representation and gain a better perspective on users’ opinions about events. Compared to our baselines, the <i>EnrichEvent</i> framework achieves the highest values for <i>Consolidation</i> outcome with an average of 87% vs. 67% and the lowest values for <i>Discrimination</i> outcome with an average of 10% vs. 16%. These results show that the <i>EnrichEvent</i> framework understands events well and distinguishes them perfectly. Moreover, the <i>Trending Data Extraction</i> module in the <i>EnrichEvent</i> framework improves efficiency by reducing <i>Runtime</i> by up to 50%. It achieves this by identifying and discarding irrelevant tweets within message blocks. As a result, the <i>EnrichEvent</i> framework becomes highly scalable for processing streaming data. Our source code and dataset are available in our official replication package.</p>

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EnrichEvent: enriching social data with contextual information for emerging event extraction

  • 期刊论文

  • 发表日期：

    2025年6月10日

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  • Mohammadali Sefidi Esfahani,
  • Mohammad Akbari

摘要

Social platforms have emerged as crucial platforms for distributing information and discussing social events, offering researchers an excellent opportunity to design and implement novel event detection frameworks. Identification of unspecified events, detection of events without using any prior knowledge, enables governments, aid agencies, and experts to respond swiftly and effectively to unfolding situations, such as natural disasters, by assessing severity and optimizing aid delivery. In addition, it provides valuable insights for marketing, commerce, and financial markets. Misspellings, incompleteness, word sense ambiguation, and irregular language characterize social data. While discussing an ongoing event, users share different opinions and perspectives based on their prior experience, background, and knowledge. Prior works primarily leverage tweets’ lexical and structural patterns to capture users’ opinions and views about events. However, tweets’ lexical and structural aspects do not perfectly reflect their views. In addition, extracting discriminative features and patterns for evolving events by exploiting the limited structural knowledge is almost infeasible. In this study, we propose an end-to-end novel framework, EnrichEvent, to identify unspecified events from streaming social data. In addition to lexical and structural patterns, we leverage contextual knowledge of the tweets to enrich their representation and gain a better perspective on users’ opinions about events. Compared to our baselines, the EnrichEvent framework achieves the highest values for Consolidation outcome with an average of 87% vs. 67% and the lowest values for Discrimination outcome with an average of 10% vs. 16%. These results show that the EnrichEvent framework understands events well and distinguishes them perfectly. Moreover, the Trending Data Extraction module in the EnrichEvent framework improves efficiency by reducing Runtime by up to 50%. It achieves this by identifying and discarding irrelevant tweets within message blocks. As a result, the EnrichEvent framework becomes highly scalable for processing streaming data. Our source code and dataset are available in our official replication package.