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Verifying information with multimedia content on twitter A comparative study of automated approaches

机译：在Twitter上使用多媒体内容验证信息自动化方法的比较研究

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摘要

An increasing amount of posts on social media are used for disseminating news information and are accompanied by multimedia content. Such content may often be misleading or be digitally manipulated. More often than not, such pieces of content reach the front pages of major news outlets, having a detrimental effect on their credibility. To avoid such effects, there is profound need for automated methods that can help debunk and verify online content in very short time. To this end, we present a comparative study of three such methods that are catered for Twitter, a major social media platform used for news sharing. Those include: a) a method that uses textual patterns to extract claims about whether a tweet is fake or real and attribution statements about the source of the content; b) a method that exploits the information that same-topic tweets should be also similar in terms of credibility; and c) a method that uses a semi-supervised learning scheme that leverages the decisions of two independent credibility classifiers. We perform a comprehensive comparative evaluation of these approaches on datasets released by the Verifying Multimedia Use (VMU) task organized in the context of the 2015 and 2016 MediaEval benchmark. In addition to comparatively evaluating the three presented methods, we devise and evaluate a combined method based on their outputs, which outperforms all three of them. We discuss these findings and provide insights to guide future generations of verification tools for media professionals.
机译：社交媒体上越来越多的帖子用于传播新闻信息，并伴随着多媒体内容。此类内容通常可能会产生误导或被数字化处理。此类内容通常会到达主要新闻媒体的首页，从而对其信誉产生不利影响。为了避免这种影响，迫切需要能够在很短的时间内帮助调试和验证在线内容的自动化方法。为此，我们对三种此类方法进行了比较研究，这些方法适用于Twitter（一种用于新闻共享的主要社交媒体平台）。其中包括：a）使用文本模式提取有关推文是虚假还是真实的声明以及有关内容来源的归属声明的方法； b）一种利用信息的方法，即就信誉而言，相同主题的推文也应相似； c）一种使用半监督学习方案的方法，该方案利用了两个独立信誉分类器的决策。我们对在2015年和2016年MediaEval基准测试范围内组织的“验证多媒体使用（VMU）”任务发布的数据集上的这些方法进行了全面的比较评估。除了比较评估三种提出的方​​法外，我们还基于其输出设计和评估一种组合方法，其性能优于所有三种方法。我们将讨论这些发现，并提供一些见识，以指导下一代面向媒体专业人员的验证工具。

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