Abstract. The availability of timely and accurate information about ongoing events is important for relief organizations seeking to effectively respond to disasters. Recently, social media platforms, and in particular Twitter, have gained traction as a novel source of information on disaster events. Unfortunately, geographical information is rarely attached to tweets, which hinders the use of Twitter for geographical applications. As a solution, analyses of a tweet’s text, combined with an evaluation of its metadata, can help to increase the number of geo-located tweets. This paper describes a new algorithm (TAGGS), that georeferences tweets by using the spatial information of groups of tweets mentioning the same location. This technique results in a roughly twofold increase in the number of geo-located tweets as compared to existing methods. We applied this approach to 35.1 million flood-related tweets in 12 languages, collected over 2.5 years. In the dataset, we found 11.6 million tweets mentioning one or more flood locations, which can be towns (6.9 million), provinces (3.3 million), or countries (2.2 million). Validation demonstrated that TAGGS correctly located about 65–75 % of the tweets. As a future application, TAGGS could form the basis for a global event detection and monitoring system.