@inproceedings{he-etal-2025-exploring,
    title = "Exploring Content Predictability in Turn-Taking Through Different Computer-Mediated Communications",
    author = "He, Wanqing  and
      MacDonald, Calen C.  and
      Yoo, Yejoon  and
      Eizayaga, Marcos  and
      Shim, Ryun  and
      Katreczko, Lev D.  and
      Fussell, Susan R.",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.532/",
    pages = "7949--7962",
    abstract = "Previous studies of face-to-face (f2f) communication have suggested that speakers rely heavily on a variety of multi-modal cues to make real-time predictions about upcoming words in rapid turn-taking. To understand how computer-mediated communication (CMC) differs from f2f communication in terms of the prediction mechanism, this study assessed how the loss of multi-modal cues would affect word predictability in turn-taking. Participants watched videos, listened to audio, or read a transcript of f2f conversations. Across these three conditions, they predicted the same set of omitted words with different levels of predictability and semantic relatedness to other words in the context. Results showed that words of higher predictability were more accurately predicted regardless of CMC types. Higher response accuracy but longer response time were observed in conditions with richer cues, and for participants with more positive and less negative self-emotions. Meanwhile, semantic relatedness did not affect predictability. These results confirmed the key role of prediction in language processing and conversation smoothness, especially its importance in CMC."
}