@inproceedings{sedefoglu-etal-2024-leadempathy,
    title = "{L}ead{E}mpathy: An Expert Annotated {G}erman Dataset of Empathy in Written Leadership Communication",
    author = "Sedefoglu, Didem  and
      Lahnala, Allison Claire  and
      Wagner, Jasmin  and
      Flek, Lucie  and
      Ohly, Sandra",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.894",
    pages = "10237--10248",
    abstract = "Empathetic leadership communication plays a pivotal role in modern workplaces as it is associated with a wide range of positive individual and organizational outcomes. This paper introduces LeadEmpathy, an innovative expert-annotated German dataset for modeling empathy in written leadership communication. It features a novel theory-based coding scheme to model cognitive and affective empathy in asynchronous communication. The final dataset comprises 770 annotated emails from 385 participants who were allowed to rewrite their emails after receiving recommendations for increasing empathy in an online experiment. Two independent annotators achieved substantial inter-annotator agreement of {\textgreater}= .79 for all categories, indicating that the annotation scheme can be applied to produce high-quality, multidimensional empathy ratings in current and future applications. Beyond outlining the dataset{'}s development procedures, we present a case study on automatic empathy detection, establishing baseline models for predicting empathy scores in a range of ten possible scores that achieve a Pearson correlation of 0.816 and a mean squared error of 0.883. Our dataset is available at https://github.com/caisa-lab/LEAD-empathy-dataset.",
}

Empathetic leadership communication plays a pivotal role in modern workplaces as it is associated with a wide range of positive individual and organizational outcomes. This paper introduces LeadEmpathy, an innovative expert-annotated German dataset for modeling empathy in written leadership communication. It features a novel theory-based coding scheme to model cognitive and affective empathy in asynchronous communication. The final dataset comprises 770 annotated emails from 385 participants who were allowed to rewrite their emails after receiving recommendations for increasing empathy in an online experiment. Two independent annotators achieved substantial inter-annotator agreement of {\textgreater}= .79 for all categories, indicating that the annotation scheme can be applied to produce high-quality, multidimensional empathy ratings in current and future applications. Beyond outlining the dataset{'}s development procedures, we present a case study on automatic empathy detection, establishing baseline models for predicting empathy scores in a range of ten possible scores that achieve a Pearson correlation of 0.816 and a mean squared error of 0.883. Our dataset is available at https://github.com/caisa-lab/LEAD-empathy-dataset.
Sedefoglu, Didem Lahnala, Allison Claire Wagner, Jasmin Flek, Lucie Ohly, S ra
LeadEmpathy: An Expert Annotated German Dataset of Empathy in Written Leadership Communication