Social media provides high-volume and real-time data, which has been broadly used in diverse applications in sales, marketing, disaster management, health surveillance, etc. However, distinguishing between noises and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. The rich information is not only included in the short textual content but also embedded in the images and videos. In this paper, we introduce an effective and efficient framework for event detection with social media data. The framework integrates both textual and imagery content in the hope to fully utilize the information. The approach has been demonstrated to be more accurate than the text-only approach by removing 58 (66.7%) false-positive events. The precision of event detection is improved by 6.5%. Besides, based on our analysis, we also look into the content of these images to further explore the space of social media studies. Finally, the closely related text and image from social media offer us a valuable text-image mapping, which can enable knowledge transfer between two media types.

中文翻译：

---

了解文本以外的社交媒体：Twitter上的可靠实践

社交媒体可提供大量实时数据，这些数据已广泛用于销售，市场营销，灾难管理，健康监控等领域的各种应用中。但是，要区分噪音和可靠信息可能会面临挑战，因为社交媒体用户生成的内容系统拥有大量每秒更新大量信息的用户。丰富的信息不仅包含在简短的文本内容中，而且还包含在图像和视频中。在本文中，我们介绍了一种有效的社交媒体数据事件检测框架。该框架整合了文本和图像内容，以期充分利用信息。通过消除58个（66.7％）的假阳性事件，该方法已被证明比纯文本方法更为准确。事件检测的精度提高了6.5％。此外，在分析的基础上，我们还研究了这些图像的内容，以进一步探索社交媒体研究的空间。最后，来自社交媒体的密切相关的文本和图像为我们提供了宝贵的文本-图像映射，它可以实现两种媒体类型之间的知识传递。