Billions of individuals around the globe use social networks to share information. Social media platforms (SMPs) allow users to share real time information through posts or tweets. There are many forms of social media, such as Instagram, Facebook, Snapchat, TikTok etc. but most of them share this feature in which they share their daily news, interests, and updates. One of the most notable social media sites is Twitter. This platform has received a lot of attention these past few years due to its real-time nature, for example political events such as coups, revolutions, and protests which led to geopolitical changes. Twitter is a SMP that people use to share tweets about breaking news, daily updates of what’s happening around them, and other topics while they’re happening, which enables real-time insightful results. Due to the recent inflation in the Lebanese economy, the Lebanese currency (LBP) significantly lost its value. This led to a fuel shortage due to the high demand and low supply which is a direct result of the financial crisis. This resulted long lines and a hassle for people to find gas. These lines caused remarkable amount of traffic which the Lebanese infrastructure can’t handle which got to a point where some streets were blocked off with vehicles that are waiting to fill up. In this thesis, to detect the occurrence of such events, twitter users, also known as Tweeps, can be considered as social sensors, for example hashtags have become an artificial filter to sub categorize news under certain subjects like traffic, car crash reports, hazards, etc. This can be used to collect information and utilize it for our cause. We will use these social sensors and analyze their tweets to detect road-traffic events and notify people. To address this issue, we introduce a novel system that integrates user generated content on SMP as well as geographical data.