RIP Emojis and Words to Contextualize Mourning on Twitter

Xinyuan Xu, R. Manrique, B. Nunes
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引用次数: 4

Abstract

This paper aims to investigate the use of emojis to contextualize mourning on Twitter. Specifically, we seek to determine (i) whether an emoji is sufficient to contextualize expressions of grief; (ii) which emojis most accurately represent mourning; (iii) whether only words are used to contextualize mourning; (iv) which words are used to characterize mourning in tweets; and, (v) if there are differences in the expression of mourning in different languages. For this, we use a multi-stage method to conduct a comprehensive analysis of the manifestations of grieving behavior on Twitter, and created machine learning models to classify expressions of mourning in tweets. The main contributions from this work are (1) a gold standard of manually annotated mourning tweets; (2) classification models produced using machine learning ensemble methods and BERT contextual embeddings; and, (3) an extensive analysis of our findings opening up opportunities for new research. The results of this paper reveal emojis alone are insufficient for identifying expressions of mourning in tweets, and the combination of both emojis and words is the most effective strategy for contextualizing mourning online -- the models achieved the 84.8%-97% F1 score in all datasets. Although words alone are capable of characterizing mourning contexts correctly, the English vocabulary is limited, and the contribution of RIP - the abbreviation for "rest in peace'' - is highly decisive. Our results have also shown that the most relevant emojis for this context were emotional ones, such as \includegraphics[width=1em]twitter_brokenheart.png, and emojis are used in a uniform fashion in both Spanish and English.
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安息的表情符号和文字在推特上表达哀悼
本文旨在研究使用表情符号来表达Twitter上的哀悼。具体来说,我们试图确定(i)表情符号是否足以表达悲伤的表达;(ii)哪些表情符号最准确地代表哀悼;(三)是否只使用文字来表达哀悼的语境;(四)推文中用哪些词来表示哀悼;(五)不同语言中哀悼的表达是否存在差异。为此,我们采用多阶段法对Twitter上的悲伤行为表现形式进行综合分析,并建立机器学习模型对tweet中的哀悼表达进行分类。这项工作的主要贡献有:(1)人工注释哀悼推文的金标准;(2)使用机器学习集成方法和BERT上下文嵌入生成的分类模型;(3)对我们的发现进行广泛的分析,为新的研究开辟机会。本文的研究结果表明,单独使用表情符号不足以识别推文中的哀悼表达,表情符号和文字的结合是最有效的在线哀悼情境化策略——模型在所有数据集中都达到了84.8%-97%的F1分数。虽然单词本身就能正确地描述哀悼的语境,但英语词汇是有限的,RIP的贡献是非常决定性的。RIP是“安息”的缩写。我们的研究结果还表明,在这种情况下,最相关的表情符号是情感表情符号,比如\includegraphics[width=1em]twitter_brokenheart.png,表情符号在西班牙语和英语中都以统一的方式使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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