Effective information management has long been a problem in organisations that are not of a scale that they can afford their own department dedicated to this task. Growing information overload has made this problem even more pronounced. On the other hand we have recently witnessed the emergence of intelligent tools, packages and resources that made it possible to rapidly transfer knowledge from the academic community to industry, government and other potential beneficiaries. Here we demonstrate how adopting state-of-the-art natural language processing (NLP) and crowdsourcing methods has resulted in measurable benefits for a human rights organisation by transforming their information and knowledge management using a novel approach that supports human rights monitoring in conflict zones. More specifically, we report on mining and classifying Arabic Twitter in order to identify potential human rights abuse incidents in a continuous stream of social media data within a specified geographical region. Results show deep learning approaches such as LSTM allow us to push the precision close to 85% for this task with an F1-score of 75%. Apart from the scientific insights we also demonstrate the viability of the framework which has been deployed as the Ceasefire Iraq portal for more than three years which has already collected thousands of witness reports from within Iraq. This work is a case study of how progress in artificial intelligence has disrupted even the operation of relatively small-scale organisations.

长期以来，有效的信息管理一直是组织的一个问题，这些组织的规模不足以使他们自己的部门可以承担这项任务。越来越多的信息过载使这个问题更加明显。另一方面，我们最近目睹了智能工具，程序包和资源的出现，这些知识，工具和程序包和资源的出现使将知识快速地从学术界转移到行业，政府和其他潜在受益者成为可能。在这里，我们演示采用最先进的自然语言处理（NLP）和众包方法如何通过使用支持冲突地区人权监控的新颖方法来转换其信息和知识管理，从而为人权组织带来可衡量的收益。进一步来说，我们报告了阿拉伯语Twitter的挖掘和分类，目的是在特定地理区域内的连续社交媒体数据流中识别潜在的侵犯人权事件。结果表明，诸如LSTM之类的深度学习方法使我们能够以75％的F1分数将这项任务的精度提高到接近85％。除了科学见解之外，我们还展示了已被部署为框架的可行性。伊拉克停火门户网站已超过三年，已经从伊拉克内部收集了数千份证人报告。这项工作是一个案例研究，说明人工智能的进步如何甚至破坏了相对较小的组织的运作。