论文标题
建立和使用个人知识图来改善社交媒体上的自杀意念检测
Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media
论文作者
论文摘要
许多人在世界上患有自杀念头。为什么一个人可能会遭受自杀意念的原因背后有很多原因。作为自我表达，情绪释放和个人互动的最受欢迎的平台，个人可能会在社交媒体上表现出许多自杀念头的症状。然而，数据和知识方面的挑战仍然是障碍，从而限制了基于社交媒体的检测绩效。数据隐含性和稀疏性使得很难根据其帖子发现个人的内在真正意图。受到心理研究的启发，我们构建和统一了一个高级自杀的知识图，并具有深层神经网络，可在社交媒体上进行自杀意念检测。我们进一步设计了两层注意机制，以明确推理并为个人的自杀念头建立关键的风险因素。关于微博和Reddit的绩效研究表明：1）使用构造的个人知识图，基于社交媒体的自杀念头检测可以实现超过93％的精度； 2）在个人因素的六类类别中，职位，个性和经验是前3个关键指标。在这些类别下，张贴的文本，压力水平，压力持续时间，张贴的图像和反刍动物思维有助于一个人的自杀念头检测。
A large number of individuals are suffering from suicidal ideation in the world. There are a number of causes behind why an individual might suffer from suicidal ideation. As the most popular platform for self-expression, emotion release, and personal interaction, individuals may exhibit a number of symptoms of suicidal ideation on social media. Nevertheless, challenges from both data and knowledge aspects remain as obstacles, constraining the social media-based detection performance. Data implicitness and sparsity make it difficult to discover the inner true intentions of individuals based on their posts. Inspired by psychological studies, we build and unify a high-level suicide-oriented knowledge graph with deep neural networks for suicidal ideation detection on social media. We further design a two-layered attention mechanism to explicitly reason and establish key risk factors to individual's suicidal ideation. The performance study on microblog and Reddit shows that: 1) with the constructed personal knowledge graph, the social media-based suicidal ideation detection can achieve over 93% accuracy; and 2) among the six categories of personal factors, post, personality, and experience are the top-3 key indicators. Under these categories, posted text, stress level, stress duration, posted image, and ruminant thinking contribute to one's suicidal ideation detection.
