基于内容的社交网络视频共享垃圾邮件过滤
Content-Based Spam Filtering on Video Sharing Social Networks
摘要：
在这项工作中，我们关注的是视频共享社交网络中的垃圾邮件的检测。具体来说，我们调查多少视觉内容为基础的分析可以帮助检测视频中的垃圾邮件。这是一项非常具有挑战性的任务，因为涉及高级语义概念;社交网络的各种性质，阻止使用受约束的先验信息;而且，最重要的是，垃圾邮件的上下文依赖性。对社交网络的内容过滤是一项日益迫切的任务：由于其受欢迎程度，滥用的数量也趋于增加，这使用户群感到厌烦，并扰乱了他们的服务。我们系统地评估了几种处理视觉信息的方法：使用静态和动态（motionaware）功能，有或没有考虑上下文，有或没有潜在语义分析（LSA）。我们的实验表明，LSA是有帮助的，但考虑到上下文是至关重要的。整个方案效果良好，说明了该方案的可行性。
英文摘要：
In this work we are concerned with the detection of spam in video sharing social networks. Specifically, we investigate how much visual content-based analysis can aid in detecting spam in videos. This is a very challenging task, because of the high-level semantic concepts involved; of the assorted nature of social networks, preventing the use of constrained a priori information; and, what is paramount, of the context dependent nature of spam. Content filtering for social networks is an increasingly demanded task: due to their popularity, the number of abuses also tends to increase, annoying the user base and disrupting their services. We systematically evaluate several approaches for processing the visual information: using static and dynamic (motionaware) features, with and without considering the context, and with or without latent semantic analysis (LSA). Our experiments show that LSA is helpful, but taking the context into consideration is paramount. The whole scheme shows good results, showing the feasibility of the concept.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
