发帖
雷达卡
摘要翻译：
研究表明，在线讨论区中的评论垃圾邮件（未经请求的、无关的、辱骂性的、仇恨性的、商业广告等）已经成为Web2.0应用程序中的一种普遍现象，迫切需要对评论垃圾邮件进行反制或打击。我们提出了一种在YouTube（最大和流行的视频分享网站）论坛中自动检测评论垃圾邮件的方法。该技术基于对用户评论活动日志的挖掘，提取指示垃圾邮件行为的模式（如后续评论之间的时间间隔、多个无关视频中完全相同的评论存在）。对从YouTube上抓取的数据进行了实证分析，证明了该方法对评论垃圾邮件的检测是有效的。
---
英文标题：
《Mining User Comment Activity for Detecting Forum Spammers in YouTube》
---
作者：
Ashish Sureka
---
最新提交年份：
2011
---
分类信息：
一级分类：Computer Science 计算机科学
二级分类：Information Retrieval 信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
--
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Computer Science 计算机科学
二级分类：Human-Computer Interaction 人机交互
分类描述：Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
包括人为因素、用户界面和协作计算。大致包括ACM学科课程H.1.2和所有H.5中的材料，除了H.5.1，它更有可能以多媒体作为主要学科领域。
--
---
英文摘要：
Research shows that comment spamming (comments which are unsolicited, unrelated, abusive, hateful, commercial advertisements etc) in online discussion forums has become a common phenomenon in Web 2.0 applications and there is a strong need to counter or combat comment spamming. We present a method to automatically detect comment spammer in YouTube (largest and a popular video sharing website) forums. The proposed technique is based on mining comment activity log of a user and extracting patterns (such as time interval between subsequent comments, presence of exactly same comment across multiple unrelated videos) indicating spam behavior. We perform empirical analysis on data crawled from YouTube and demonstrate that the proposed method is effective for the task of comment spammer detection.
---
PDF链接：
https://arxiv.org/pdf/1103.5044
京公网安备 11010802022788号
