Information coming from social media is vital to the understanding of the dynamics involved in multiple events such as terrorist attacks and natural disasters. With the spread and popularization of cameras and the means to share content through social networks, an event can be followed through many different lenses and vantage points. However, social media data present numerous challenges, and frequently it is necessary a great deal of data cleaning and filtering techniques to separate what is related to the depicted event from contents otherwise useless. In a previous effort of ours, we decomposed events into representative components aiming at describing vital details of an event to characterize its defining moments. However, the lack of minimal supervision to guide the combination of representative components somehow limited the performance of the method. In this paper, we extend upon our prior work and present a learning-from-data method for dynamically learning the contribution of different components for a more effective event representation. The method relies upon just a few training samples (few-shot learning), which can be easily provided by an investigator. The obtained results on real-world datasets show the effectiveness of the proposed ideas.

中文翻译：

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用于理解现实世界事件的流形学习


来自社交媒体的信息对于了解恐怖袭击和自然灾害等多种事件的动态至关重要。随着相机的传播和普及以及通过社交网络共享内容的手段，可以通过许多不同的镜头和有利位置来跟踪事件。然而，社交媒体数据带来了许多挑战，并且通常需要大量的数据清理和过滤技术来将与所描述的事件相关的内容与其他无用的内容分开。在我们之前的努力中，我们将事件分解为代表性组件，旨在描述事件的重要细节以表征其决定性时刻。然而，缺乏最低限度的监督来指导代表性成分的组合在某种程度上限制了该方法的性能。在本文中，我们扩展了之前的工作，并提出了一种从数据中学习的方法，用于动态学习不同组件的贡献，以实现更有效的事件表示。该方法仅依赖于少数训练样本（少样本学习），这些样本可以由研究者轻松提供。在现实世界数据集上获得的结果表明了所提出的想法的有效性。