Understanding social media beyond text: a reliable practice on Twitter

Q1 Mathematics

Computational Social Networks Pub Date : 2020-04-30 DOI:10.21203/rs.3.rs-24618/v1

Qixuan Hou, Meng Han, Feiyang Qu, J. He

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引用次数: 3

Abstract

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.

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理解超越文本的社交媒体:Twitter的可靠实践

社交媒体提供了大量实时数据，已被广泛用于销售、营销、灾害管理、健康监测等领域的各种应用。然而，区分噪音和可靠信息可能具有挑战性，因为社交媒体作为一种用户生成的内容系统，有大量用户每秒更新大量信息。丰富的信息不仅包含在简短的文本内容中，而且嵌入在图像和视频中。在本文中，我们介绍了一种利用社交媒体数据进行事件检测的有效框架。该框架将文本和图像内容相结合，以期充分利用信息。该方法已被证明比纯文本方法更准确，消除了58个（66.7%）假阳性事件。事件检测的精度提高了6.5%。此外，基于我们的分析，我们还对这些图像的内容进行了研究，以进一步探索社交媒体研究的空间。最后，社交媒体中密切相关的文本和图像为我们提供了一个有价值的文本图像映射，可以实现两种媒体类型之间的知识转移。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.