用于研究言语行为中的幽默的社交媒体模拟

Xiaoying Liles
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引用次数: 0

摘要

本报告介绍了社交网络话语完成任务(DCT)的开发情况,以调查社交媒体点赞回复中的幽默。贝尔等人(Bell et al.,2021 年)和桥本和尼尔森(Hashimoto and Nelson,2020 年)分别引入了 DCTs 和 DCTs(DCTs 用于探索 L2 的幽默生成),前者通过模拟在线论坛互动来生成与语料库数据相当的语言样本,后者则通过 "CMC-for-CMC "方法将 DCTs 扩展到了计算机辅助交流(CMC)。该任务让参与者参与幽默语境、有趣的赞美以及在线互动的近距离模拟,从而提高了幽默的产生率。本报告讨论了该工具的益处,提供了支持其有效性和有效性的初步证据,并概述了其在 L2 语用学研究中的未来发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A social media simulation for investigating humor in speech acts
This report presents the development of the SocialNet Discourse Completion Task (DCT) to investigate humor in social media compliment responses. Building on observations by Bell et al. (2021), who introduced DCTs to explore L2 humor production, and Hashimoto and Nelson (2020), who emulated online forum interaction to produce language samples comparable to corpus data, this study extends DCTs to computer-mediated communication (CMC) using a “CMC-for-CMC” approach. The task engages participants with humorous contexts, amusing compliments, and close emulation of online interactions, revealing increased humor production rates. This report discusses the benefits of the instrument, provides preliminary evidence supporting its effectiveness and validity, and outlines its future directions in L2 pragmatics studies.
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