While misinformation and disinformation have been thriving in social media for years, with the emergence of the COVID-19 pandemic, the political and the health misinformation merged, thus elevating the problem to a whole new level and giving rise to the first global infodemic. The fight against this infodemic has many aspects, with fact-checking and debunking false and misleading claims being among the most important ones. Unfortunately, manual fact-checking is time-consuming and automatic fact-checking is resource-intense, which means that we need to pre-filter the input social media posts and to throw out those that do not appear to be check-worthy. With this in mind, here we propose a model for detecting check-worthy tweets about COVID-19, which combines deep contextualized text representations with modeling the social context of the tweet. We further describe a number of additional experiments and comparisons, which we believe should be useful for future research as they provide some indication about what techniques are effective for the task. Our official submission to the English version of CLEF-2020 CheckThat! Task 1, system Team_Alex, was ranked second with a MAP score of 0.8034, which is almost tied with the wining system, lagging behind by just 0.003 MAP points absolute.

中文翻译：

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CLEF 的 Alex 团队 CheckThat！2020 年：使用 Transformer 模型识别值得检查的推文

虽然错误信息和虚假信息多年来一直在社交媒体上盛行，但随着 COVID-19 大流行的出现，政治和健康错误信息融合在一起，从而将问题提升到一个全新的水平，并引发了第一次全球信息流行病。与这种信息流行病的斗争有很多方面，其中最重要的是事实核查和揭穿虚假和误导性的说法。不幸的是，手动事实检查是耗时的，而自动事实检查是资源密集型的，这意味着我们需要预先过滤输入的社交媒体帖子并丢弃那些看起来不值得检查的帖子。考虑到这一点，我们在这里提出了一个模型，用于检测关于 COVID-19 的值得检查的推文，该模型将深度上下文化文本表示与推文的社会背景建模相结合。我们进一步描述了一些额外的实验和比较，我们认为这对未来的研究应该有用，因为它们提供了一些关于哪些技术对任务有效的指示。我们正式提交英文版 CLEF-2020 CheckThat！任务1系统Team_Alex以0.8034的MAP得分排名第二，几乎与获胜系统并列，仅落后0.003 MAP绝对值。