The ongoing technological advancement has a huge effect on the ways how we view and plan our cities. A key factor in these developments is the abundant availability of data. The amount of information we generate every day grows exponentially and covers more and more aspects of our lives. Various researchers agree that Big Data has the potential to become a rich and fruitful asset for spatial planning. Planners might become able to monitor multiple aspects of our cities in real-time, making it possible to set measures or interventions immediately. On the other hand, these developments also propose a threat to privacy of peoples lives. Risks of misusing the recorded data have been addressed my many critical thinkers. Besides, even when having good intentions, the excitement surrounding Big Data may easily lead to drawing false conclusions. Still, new technologies and Big Data have some enormous potentials which can help us to secure and enhance many aspects of quality of life in our cities. A novel data source are social media sites, used by millions of people worldwide. Users of these portals also generate data having the potential of becoming highly valuable for spatial planning. Social Media Geographic Information (SMGI) contains not only information about the location of people at certain times, but enables to localise discussions and sentiments of people towards specific topics. Therefore it might help us to understand the dynamics of cities and unveil some knowledge we had no access to before. By its nature SMGI also often conveys many challenging characteristics of Big Data. It is vaguely structured, very diverging in quality, and cannot be assessed by traditional methods. Furthermore it is heavily biased, lacks validity, and representativeness. Still, when applying the correct pre-processing and analysis steps, its spatial and temporal flexibility, together with the broad range of its content promise to become a highly valuable source of information for spatial planning. A main aim of the project was to show how data processing works and to present Natural Language Processing methods currently mostly unknown in the domain of spatial planning, such as topic modelling and sentiment analysis. The masters thesis tries to define and assess the concrete value of social media analysis for urban planners. It deals with the questions of how to make use of such data in a clear, correct, ethical and useful way. To answer these questions, a case study was conducted by recording all tweets from May to October 2018 in London through Twitters Streaming API. The 8.3 million captured tweets were analysed on their spatial and temporal distribution, their content and their sentiment measures, and the combinations of these factors. Then, the results were assessed on their quality and the usefulness for spatial planning. In conclusion, the amount of valuable, valid and useful information that could be extracted from these 8 million tweets was very little. In many cases, Twitter data did reflect real world phenomena, for example by showing that there is a higher activity of tweets dealing with the topic education around schools and universities. Still, topics and themes being potentially more useful for spatial planning, such as crime, social controversies or users sentiment scores, didnt reflect real-world indicators. For this reason the following assesses work the value of Twitter data for spatial planning to be relatively low. Although the downloaded data displays an enormous flexibility regarding space, time and content, it is generally not reliable enough. Furthermore leads the overassessment of new technologies to the reappearance of the God-Father-Model of planning. Therefore it becomes crucial for planners to evaluate the qualities, dangers, and potentials of Big Data critically.