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基于社交媒体的事件感知与多模态事件脉络生成 被引量:4

Event Sensing and Multimodal Event Vein Generation Leveraging Social Media
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摘要 随着信息技术的发展和社交媒体的流行,普通用户已经完成了从信息接受者到信息产生者的转变,每个人都可以实时分享自己身边的信息,也可以转发自己感兴趣的内容,这使得社交媒体的数据量迅速增长。在海量数据中蕴含着丰富的社会事件发生和发展的记录,如何有效地从这些数据中挖掘出有价值的信息成为了当前信息领域的重要问题。针对该问题,介绍了基于社交媒体的事件感知与多模态事件脉络生成。基于社交媒体的事件感知与多模态事件脉络生成旨在通过分析社交媒体中的文本、时间、图像、评论、观点、情感和用户交互等多模态数据,感知事件并刻画事件的关系,从而实现对事件的总结。讨论了基于社交媒体的事件感知与多模态事件脉络生成的描述模型、概念、发展历史、关键技术与挑战以及其广泛的应用领域,综述了社交媒体分析在事件感知和事件总结方面的研究进展,并对其未来发展进行了展望。 With the development of information technology and popularity of social media,normal users have become information producers from receivers and everyone can share what happened around them and repost what they are interested in,which makes the information stored in social media increase rapidly.The large amount of data contains abundant and valuable records of social events.How to get valuable informations from these data has become one of the most important problems in information field.This paper introduced the new research field,including crowd-powered event sensing and multimodal summarization to solve this problem.Crowd-powered event sensing and multimodal summarization aim at sensing and analyzing events by analyzing multimodal data existed in social media to predict and summarize events effectively.This paper described the modal of event,the history of sensing,the key technology,challenges and wide application field,summarized the development of event sensing and summarization based social media analysis and looked into the future.
出处 《计算机科学》 CSCD 北大核心 2017年第S1期33-36,共4页 Computer Science
基金 国家重点基础研究发展计划(973计划)(2015CB352400) 国家自然科学基金(61332005 61373119)资助
关键词 社交媒体 事件感知 多模态数据 事件脉络 跨媒体 Social media Event sensing Multimodal data Storyline Cross media
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  • 1张珊,于留宝,胡长军.基于表情图片与情感词的中文微博情感分析[J].计算机科学,2012,39(S3):146-148. 被引量:55
  • 2Lee, Ryong,Wakamiya, Shoko,Sumiya, Kazutoshi.Discovery of unusual regional social activities using geo-tagged microblogs. World Wide Web . 2011
  • 3Barabási Albert-László.The origin of bursts and heavy tails in human dynamics. Nature . 2005
  • 4MATHIOUDAKIS M,KOUDAS N.TwitterMonitor:trend detection over the Twitter stream. SIGMOD’’10:Proceedings of the2010 ACM SIGMOD International Conference on Management of Data . 2010
  • 5Zhang Jing,Liu Biao,Tang Jie,et al.Social influence locality for modeling retweeting behaviors. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence . 2013
  • 6J. Leskovec,M. McGlohon,C. Faloutsos, et al.Cascading Behavior in Large Blog Graphs. SDM’’’’ 07 . 2007
  • 7Guo B,Yu Z,Zhou X,et al.From Participatory Sensing to Mobile Crowd Sensing. 2014IEEE International Conference on Pervasive Computing and Communications Workshops PERCOM Workshops . 2014
  • 8Cui A,Zhang M,Liu Y,et al.Discover breaking events with popular hashtags in twitter. Proceedings of the 21st ACM International Conference on Information and Knowledge Management . 2012
  • 9Ozdikis O,Senkul P,Oguztuzun H.Semantic expansion of hashtags for enhanced event detection in Twitter. Proceedings of the 1st International Workshop on Online Social Systems . 2012
  • 10Zhao S,Zhong L,Wickramasuriya J,et al.Human as real-time sensors of social and physical events:A case study of twitter and sports games. . 2011

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