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首页> 外文会议>2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare >Automated content based short text classification for filtering undesired posts on Facebook
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Automated content based short text classification for filtering undesired posts on Facebook

机译：基于内容的自动化短文本分类，可过滤Facebook上不需要的帖子

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摘要

Online Social Networking (OSN) sites are always helpful for being socialized and to get exposed to a social environment. But, privacy and prevention of undesired posts on user wall is the only problem of biggest concern. User should have the ability to control the message posted on their own private wall to avoid undesirable contents to be displayed. The existing OSN sites have very little support regarding this problem. For example, Facebook filters messages on the basis of identity of sender i.e. only friend, friend of friend or group of friends can post any message; no content based preferences are supported. Taking this fact into consideration, the proposed work contributes to address such problem through a machine learning based soft classifier for labeling messages in support of contents of message. This work experimentally evaluates an automated scheme to filter out unwanted messages posted on Facebook walls by assigning a set of categories with each short text message based on its contents.
机译：在线社交网络（OSN）站点始终有助于社交化并暴露于社交环境中。但是，隐私和防止在用户墙上发布不想要的帖子是最需要关注的唯一问题。用户应具有控制张贴在自己的私人墙上的消息的能力，以避免显示不想要的内容。现有的OSN站点对此问题几乎没有支持。例如，Facebook根据发件人的身份过滤消息，即只有朋友，朋友的朋友或朋友组可以发布任何消息；不支持基于内容的首选项。考虑到这一事实，提出的工作有助于通过基于机器学习的软分类器解决该问题，该分类器用于标记消息以支持消息的内容。这项工作实验性地评估了一种自动方案，该方案通过根据其内容为每个短文本消息分配一组类别来过滤掉张贴在Facebook墙上的有害消息。

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