Quantifying the trustworthiness of social media content
Quantifying the trustworthiness of social media content
复制标题
量化社交媒体内容的可信度
DOI：
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发表时间：
2011
影响因子：
1.2
通讯作者：
Huan Liu
中科院分区：
文献类型：
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作者：
S. Moturu;Huan Liu
近年来，社交媒体的日益普及导致了大量用户生成内容的产生。这些信息中的很大一部分是有用的，并且已被证明是知识的重要来源。然而，由于这些信息大多是由很少或没有明显声誉的陌生人提供的，因此没有简单的方法来检测内容是否值得信赖。搜索引擎是知识的门户，但搜索相关性并不能保证搜索结果中的内容是可信的。一个偶然的观察者可能无法区分可信和不可信的内容。这项工作的重点是量化这些共享内容的价值及其可信度的问题。特别是，重点是共享健康内容，因为在该领域对不可信内容采取行动的负面影响很大。本研究使用了两个社交媒体应用程序Wikipedia和Daily Strength中的健康内容。信任的社会学概念被用来激励寻找解决办法。为此提出了一种两步无监督、特征驱动的方法：一个是特征识别步骤，其中指定相关的信息类别并识别合适的特征，另一个是量化步骤，其中提出了各种无监督评分模型。结果表明，这种方法是有效的，可以很容易地适应不同的社交媒体应用程序。
The growing popularity of social media in recent years has resulted in the creation of an enormous amount of user-generated content. A significant portion of this information is useful and has proven to be a great source of knowledge. However, since much of this information has been contributed by strangers with little or no apparent reputation to speak of, there is no easy way to detect whether the content is trustworthy. Search engines are the gateways to knowledge but search relevance cannot guarantee that the content in the search results is trustworthy. A casual observer might not be able to differentiate between trustworthy and untrustworthy content. This work is focused on the problem of quantifying the value of such shared content with respect to its trustworthiness. In particular, the focus is on shared health content as the negative impact of acting on untrustworthy content is high in this domain. Health content from two social media applications, Wikipedia and Daily Strength, is used for this study. Sociological notions of trust are used to motivate the search for a solution. A two-step unsupervised, feature-driven approach is proposed for this purpose: a feature identification step in which relevant information categories are specified and suitable features are identified, and a quantification step for which various unsupervised scoring models are proposed. Results indicate that this approach is effective and can be adapted to disparate social media applications with ease.