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Towards early identification of online rumors based on long short-term memory networks

机译：基于长短期内存网络的在线谣言早期识别

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摘要

In the social media environment, rumors are constantly breeding and rapidly spreading, which has become a severe social problem, often leading to serious consequences (e.g., social panic and even chaos). Therefore, how to identify rumors quickly and accurately has become a key prerequisite for taking effective measures to curb the spread of rumors and reduce their influence. However, most existing studies employ machine learning based methods to carry out automatic rumor identification by extracting features of rumor contents, posters, and static spreading processes (e.g., follow-ups, thumb-ups, etc.) or by learning the presentation of forwarding contents. These studies fail to take into account the dynamic differences between the spreaders and diffusion structures of rumors and non-rumors. To fill this gap, this paper proposes Long Short-Term Memory (LSTM) network based models for identifying rumors by capturing the dynamic changes of forwarding contents, spreaders and diffusion structures of the whole (in the afterwards identification mode) or only the beginning part (in the halfway identification mode, i.e., early rumor identification) of the spreading process. Experiments conducted on a rumor and non-rumor dataset from Sina Weibo show that the proposed models perform better than existing baselines.
机译：在社交媒体环境中，谣言经常繁殖和快速传播，这已成为一个严重的社会问题，往往导致严重后果（例如，社会恐慌甚至混乱）。因此，如何快速准确地识别谣言已成为采取有效措施抑制谣言传播并减少其影响的关键前提。然而，大多数现有的研究采用基于机器学习的方法来通过提取谣言内容，海报和静态扩展过程（例如，随访，拇指向上等）或通过学习转发的呈现来进行自动谣言识别内容。这些研究未能考虑谣言和非谣言的吊具和扩散结构之间的动态差异。为了填补这个差距，本文通过捕获整个转发内容，扩散器和扩散结构的动态变化或仅开头部分来提出基于长期内存（LSTM）网络的基于网络的基于网络的基于长期内存（LSTM）网络的模型来识别谣言（在远程识别模式，即，早期谣言识别）的传播过程。来自新浪微博的谣言和非谣言数据集进行的实验表明，所提出的模型比现有基线更好。

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