构建俄语社交媒体文本中自杀倾向与反自杀信号检测的大规模数据集的方法
The methodology of Constructing the Large-Scale Dataset for Detecting Presuicidal and Anti-Suicidal Signals in Social Media Texts in Russian
浏览论文内容
中文总结 AI 辅助
本研究提出了构建俄语社交媒体自杀倾向与反自杀信号检测大规模数据集的方法，完成了5万余条文本的标注，公开了数据集、代码及材料，还开展了分类模型基础实验。
中文摘要 AI 辅助
自杀是一种由自身精神状态误导导致的可怕行为，该问题在许多国家普遍存在，俄罗斯的自杀人数也相当高。幸运的是，其中一部分人会在社交媒体上写下自己的挣扎，这为找到并帮助他们提供了途径，但这些有价值的文本会淹没在大量无关文本中，极大地减缓了对个人自杀风险的决策过程。为解决该问题，本研究提出了构建用于检测自杀倾向与反自杀信号文本的数据集的详细方法，该方法涵盖指令与分类表创建、标注、验证及标注后修正的流程。依据此方法，我们收集并标注了包含5万余条社交媒体文本的大规模俄语数据集，提供了数据集的统计数量及标注中的常见问题，还开展了基础分类模型构建实验，以展示不同标注水平下的运行性能，此外，我们将该数据集、代码及所有材料公开提供。
英文摘要
The suicide is a terrifying act of a person who is misled by his own mental state. This problem arises across many countries. Sadly, Russia also has quite high number of persons who committed suicide. Luckily, a subset of these people writes their struggles in social media, allowing a way to find them and help. However, these valuable texts disappearing in many irrelevant texts which is considerably slowing down the decision process about person's suicidal risk. To tackle this problem, in this work we have presented a detailed methodology of building the dataset for detecting texts that describe presuicidal and anti-suicidal signals. This methodology describes the process of instruction and class table creation, the process of annotation, verification and post-annotation correction. Guiding by this methodology, we collect and annotate a large-scale Russian dataset with more than 50 thousand texts from social media. We provide a count statistic of the dataset as well as common problems in annotation. We also conduct basic experiments of building the classification models to show the on go performance on different levels of annotation. Furthermore, we make the dataset, code and all materials publicly available.
