希望语音检测：和平之声的计算分析
Hope Speech Detection: A Computational Analysis of the Voice of Peace
摘要：
最近的Pulwama恐怖袭击（2019年2月14日，克什米尔Pulwama）引发了印度和巴基斯坦之间一系列不断升级的事件，为他们70年来的克什米尔争端增添了另一个插曲。在当今这个社交媒体无处不在的时代，核大国从未如此接近战争。在本文中，我们通过使用YouTube视频上的评论构建的大量语料库来分析这场不断演变的国际危机（791，289名用户对2，890个视频的204万条评论中有392，460名用户发布了921，235条英文评论）。我们在论文中的主要贡献有三个方面。首先，我们提出了一个观察，多语种词嵌入揭示精确和准确的语言集群，并随后构建一个文档语言识别技术，可以忽略不计的注释要求。我们展示了各种数据集的可行性和实用性，涉及几种低资源语言。其次，我们提出了一个分析的时间趋势的亲和平和亲战争的意图观察到，当两国之间的紧张局势处于顶峰，亲和平的意图在语料库是在其最高点。最后，在激烈的讨论的背景下，在一个政治紧张的局势，两个国家都处于全面战争的边缘，我们认为自动识别用户生成的网络内容，可以扩散敌意和解决这个预测任务，被称为\emph{hope-speech detection}的重要性。
英文摘要：
The recent Pulwama terror attack (February 14, 2019, Pulwama, Kashmir) triggered a chain of escalating events between India and Pakistan adding another episode to their 70-year-old dispute over Kashmir. The present era of ubiquitious social media has never seen nuclear powers closer to war. In this paper, we analyze this evolving international crisis via a substantial corpus constructed using comments on YouTube videos (921,235 English comments posted by 392,460 users out of 2.04 million overall comments by 791,289 users on 2,890 videos). Our main contributions in the paper are three-fold. First, we present an observation that polyglot word-embeddings reveal precise and accurate language clusters, and subsequently construct a document language-identification technique with negligible annotation requirements. We demonstrate the viability and utility across a variety of data sets involving several low-resource languages. Second, we present an analysis on temporal trends of pro-peace and pro-war intent observing that when tensions between the two nations were at their peak, pro-peace intent in the corpus was at its highest point. Finally, in the context of heated discussions in a politically tense situation where two nations are at the brink of a full-fledged war, we argue the importance of automatic identification of user-generated web content that can diffuse hostility and address this prediction task, dubbed \emph{hope-speech detection}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
