A framework for validating the merit of properties that predict the influence of a twitter user. - Stefan Räbiger; Myra Spiliopoulou (Expert Syst. Appl., 2015)

SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification. - Stefan Räbiger; Mishal Kazmi; Yücel Saygin; Peter Schüller; Myra Spiliopoulou (SemEval@NAACL-HLT, 2016)

How to Provide Developers Only with Relevant Information? - Stefan Räbiger; Ataman Girisken; Cemal Yilmaz 001 (IWESEP@SANER, 2016)

Predicting Worker Disagreement for More Effective Crowd Labeling. - Stefan Räbiger; Gizem Gezici; Yücel Saygin; Myra Spiliopoulou (DSAA, 2018)

How Does Tweet Difficulty Affect Labeling Performance of Annotators? - Stefan Räbiger; Yücel Saygin; Myra Spiliopoulou (CoRR, 2018)

How do annotators label short texts? Toward understanding the temporal dynamics of tweet labeling. - Stefan Räbiger; Myra Spiliopoulou; Yücel Saygin (Inf. Sci., 2018)

Analyzing crowd workers learning behavior to obtain more reliable labels (Kitle çalışanlarının öğrenme tutumlarının daha güvenilir etiketler elde etmek içinanaliz edilmesi) - Stefan Räbiger (2018)

Discovering the Prerequisite Relationships Among Instructional Videos From Subtitles. - Mehmet Cem Aytekin; Stefan Räbiger; Yücel Saygin (EDM, 2020)

Context-based extraction of concepts from unstructured textual documents. - Saima Gul; Stefan Räbiger; Yücel Saygin (Inf. Sci., 2022)