# FailedRevolutions：使用Twitter研究ISIS支持的前提
#FailedRevolutions: Using Twitter to Study the Antecedents of ISIS Support
摘要：
在相当短的时间内，伊拉克和叙利亚伊斯兰国（伊斯兰国）设法控制了叙利亚和伊拉克的大片土地。对许多观察家来说，这个“国家”建立的速度之快令人瞠目结舌。为了更好地了解这个组织及其支持者的根源，我们使用Twitter的数据进行了一项研究。我们首先收集大量提到ISIS的阿拉伯语推文，并将其分为支持ISIS和反对ISIS。这种分类原来是很容易做到的，简单地使用名称的变体用来指该组织：全名和描述为“国家”是与支持，而缩写通常表示反对。然后，我们通过分析支持和反对用户的历史时间表来“回到过去”，并查看他们在ISIS之前的时期，以深入了解支持的先决条件。为了实现这一目标，我们使用ISIS之前的数据构建了一个分类器，以“预测”，回顾过去，谁将支持或反对该组织。出现的关键故事是对失败的阿拉伯之春革命的沮丧之一。ISIS的支持者与ISIS的反对派在很大程度上不同，他们更多地提到失败的阿拉伯之春起义。我们还发现了支持和反对的时间模式，这似乎与重大新闻有关，例如关于领土获得的报道，关于可怕暴力行为的报道，以及关于空袭和外国干预的报道。
英文摘要：
Within a fairly short amount of time, the Islamic State of Iraq and Syria (ISIS) has managed to put large swaths of land in Syria and Iraq under their control. To many observers, the sheer speed at which this "state" was established was dumbfounding. To better understand the roots of this organization and its supporters we present a study using data from Twitter. We start by collecting large amounts of Arabic tweets referring to ISIS and classify them into pro-ISIS and anti-ISIS. This classification turns out to be easily done simply using the name variants used to refer to the organization: the full name and the description as "state" is associated with support, whereas abbreviations usually indicate opposition. We then "go back in time" by analyzing the historic timelines of both users supporting and opposing and look at their pre-ISIS period to gain insights into the antecedents of support. To achieve this, we build a classifier using pre-ISIS data to "predict", in retrospect, who will support or oppose the group. The key story that emerges is one of frustration with failed Arab Spring revolutions. ISIS supporters largely differ from ISIS opposition in that they refer a lot more to Arab Spring uprisings that failed. We also find temporal patterns in the support and opposition which seems to be linked to major news, such as reported territorial gains, reports on gruesome acts of violence, and reports on airstrikes and foreign intervention.
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。
