Twitter has become one of the most sought after places to discuss a wide variety of topics, including medically relevant issues such as cancer. This helps spread awareness regarding the various causes, cures and prevention methods of cancer. However, no proper analysis has been performed, which discusses the validity of such claims. In this work, we aim to tackle the misinformation spread in such platforms. We collect and present a dataset regarding tweets which talk specifically about cancer and propose an attention-based deep learning model for automated detection of misinformation along with its spread. We then do a comparative analysis of the linguistic variation in the text corresponding to misinformation and truth. This analysis helps us gather relevant insights on various social aspects related to misinformed tweets.

中文翻译：

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分析癌症相关推文中错误信息的程度

Twitter 已成为讨论各种话题（包括癌症等医学相关问题）最受追捧的地方之一。这有助于传播有关癌症的各种原因、治疗方法和预防方法的认识。但是，没有进行适当的分析，讨论此类声明的有效性。在这项工作中，我们的目标是解决此类平台中传播的错误信息。我们收集并展示了一个关于专门谈论癌症的推文的数据集，并提出了一个基于注意力的深度学习模型，用于自动检测错误信息及其传播。然后，我们对与错误信息和真实信息相对应的文本中的语言变化进行比较分析。这种分析有助于我们收集与误导推文相关的各种社会方面的相关见解。