Relevancer aims at identifying relevant content in social media streams. Text mining is the main approach.


Ali Hürriyetoglu (@hurrial), Mustafa Erkan Başar (@me_basar), Nelleke Oostdijk, Antal van den Bosch (@antalvdb), Aslıhan Arslan (@miniminiibirkus), Uğur Özcan (@uozcan12), Jurjen Wagemaker (@jurjenwagemaker), Ghiath Ghanem, and Ron Bortman.


Hürriyetoglu, A., Gudehus C., Oostdijk, N. H. J., & van den Bosch, A. P. J. (2016a). Relevancer: Finding and Labeling Relevant Information in Tweet Collections. In International Conference on Social Informatics (pp. 1-15). Springer International Publishing. LINK:

Hürriyetoglu, A., Wagemaker J., Oostdijk, N. H. J., & van den Bosch, A. P. J. (2016b). Analysing the Role of Key Term Inflections in Knowledge Discovery on Twitter. In Proceedings of the 2st International Workshop on Knowledge Discovery on the WEB. Cagliari, Italy, September 8-10, 2016.

Hürriyetoglu, A., van den Bosch, A., & Oostdijk, N. (2016c). Using Relevancer to Detect Relevant Tweets: The Nepal Earthquake Case. Working notes of FIRE 2016 - Forum for Information Retrieval Evaluation, Kolkata, India, December 7-10, 2016. Url:



Machine learning:


Licensed under GPLv3 (See


This project was supported by Floodtags (@FloodTags) and the Dutch national programme COMMIT (@COMMIT_nl as part of the Infiniti project, Work Package ADNEXT (@Adnext_Commit).