Geo-social data has been an attractive source for a variety of problems such as mining mobility patterns, link prediction, location recommendation, and influence maximization. However, new geo-social data is increasingly unavailable and suffers several limitations. In this paper, we aim to remedy the problem of effective data extraction from geo-social data sources. We first identify and categorize the limitations of extracting geo-social data. In order to overcome the limitations, we propose a novel seed-driven approach that uses the points of one source as the seed to feed as queries for the others. We additionally handle differences between, and dynamics within the sources by proposing three variants for optimizing search radius. Furthermore, we provide an optimization based on recursive clustering to minimize the number of requests and an adaptive procedure to learn the specific data distribution of each source. Our comprehensive experiments with six popular sources show that our seed-driven approach yields 14.3 times more data overall, while our request-optimized algorithm retrieves up to 95% of the data with less than 16% of the requests. Thus, our proposed seed-driven approach set new standards for effective and efficient extraction of geo-social data.