Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.

翻译：恐怖主义已成为全球性祸害，对国家发展造成严重后果。除每日残害无辜民众、阻碍教育活动开展外，恐怖主义还在制约经济增长。若能获取准确数据，机器学习与自然语言处理技术可通过实时预测未来恐怖袭击事件，为反恐斗争作出贡献。本文隶属于一项研究项目，旨在利用社交网络文本提取必要信息，构建适用于恐怖袭击预测的完备数据集。我们收集了3000条关于布基纳法索恐怖主义的社交网络文本，并选取子集对现有自然语言处理方案进行实验。实验表明，现有方案在位置识别方面精度不足，而我们的方案解决了这一问题。我们将进一步扩展该方案，以提取日期和行动信息，最终实现项目目标。