While text classification can classify tweets, assessing whether a tweet is related to an ongoing flood event or not, based on its text, remains difficult. Inclusion of contextual hydrological information could improve the performance of such algorithms. Here, a multilingual multimodal neural network is designed that can effectively use both textual and hydrological information. The classification data was obtained from Twitter using flood-related keywords in English, French, Spanish and Indonesian. Subsequently, hydrological information was extracted from a global precipitation dataset based on the tweet's timestamp and locations mentioned in its text. Three experiments were performed analyzing precision, recall and F1-scores while comparing a neural network that uses hydrological information against a neural network that does not. Results showed that F1-scores improved significantly across all experiments. Most notably, when optimizing for precision the neural network with hydrological information could achieve a precision of 0.91 while the neural network without hydrological information failed to effectively optimize. Moreover, this study shows that including hydrological information can assist in the translation of the classification algorithm to unseen languages.

中文翻译：

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在多模态神经网络中使用上下文水文信息改进洪水推文的分类

虽然文本分类可以对推文进行分类，但基于文本评估推文是否与正在进行的洪水事件相关仍然很困难。包含上下文水文信息可以提高此类算法的性能。在这里，设计了一个多语言多模态神经网络，可以有效地使用文本和水文信息。分类数据是使用英语、法语、西班牙语和印度尼西亚语的洪水相关关键词从 Twitter 获得的。随后，根据推文的时间戳和文本中提到的位置，从全球降水数据集中提取水文信息。进行了三个实验，分析精度、召回率和 F1 分数，同时比较使用水文信息的神经网络与不使用水文信息的神经网络。结果表明，F1 分数在所有实验中都有显着提高。最值得注意的是，在优化精度时，有水文信息的神经网络可以达到 0.91 的精度，而没有水文信息的神经网络未能有效优化。此外，这项研究表明，包括水文信息可以帮助将分类算法翻译成看不见的语言。