Mayor Eric, Bietti Lucas M
Institute of Work and Organizational Psychology, University of Neuchâtel, Rue Emile Argand 11, Neuchâtel 2000, Switzerland.
Division of Clinical Psychology and epidemiology, Department of Psychology, University of Basel, MIssionsstrasse 61a, Basel 4055, Switzerland.
R Soc Open Sci. 2021 May 26;8(5):201900. doi: 10.1098/rsos.201900.
The study of temporal trajectories of emotions shared in tweets has shown that both positive and negative emotions follow nonlinear circadian (24 h) and circaseptan (7-day) patterns. But to this point, such findings could be instrument-dependent as they rely exclusively on coding using the Linguistic Inquiry Word Count. Further, research has shown that self-referential content has higher relevance and meaning for individuals, compared with other types of content. Investigating the specificity of self-referential material in temporal patterns of emotional expression in tweets is of interest, but current research is based upon generic textual productions. The temporal variations of emotions shared in tweets through emojis have not been compared to textual analyses to date. This study hence focuses on several comparisons: (i) between Self-referencing tweets versus Other topic tweets, (ii) between coding of textual productions versus coding of emojis, and finally (iii) between coding of textual productions using different sentiment analysis tools (the Linguistic Inquiry and Word Count-LIWC; the Valence Aware Dictionary and sEntiment Reasoner-VADER and the Hu Liu sentiment lexicon-Hu Liu). In a collection of more than 7 million Self-referencing and close to 18 million Other topic content-coded tweets, we identified that (i) similarities and differences in terms of shape and amplitude can be observed in temporal trajectories of expressed emotions between Self-referring and Other topic tweets, (ii) that all tools feature significant circadian and circaseptan patterns in both datasets but not always, and there is often a correspondence in the shape of circadian and circaseptan patterns, and finally (iii) that circadian and circaseptan patterns obtained from the coding of emotional expression in emojis sometimes depart from those of the textual analysis, indicating some complementarity in the use of both modes of expression. We discuss the implications of our findings from the perspective of the literature on emotions and well-being.
对推特中分享的情绪的时间轨迹研究表明,积极情绪和消极情绪均呈现非线性的昼夜节律(24小时)和七日节律(7天)模式。但到目前为止,这些发现可能依赖于工具,因为它们完全依赖于使用语言查询词频统计进行编码。此外,研究表明,与其他类型的内容相比,自我参照内容对个体具有更高的相关性和意义。研究推特中自我参照材料在情绪表达时间模式中的特殊性很有意义,但目前的研究基于一般文本作品。迄今为止,通过表情符号在推特中分享的情绪的时间变化尚未与文本分析进行比较。因此,本研究着重于几项比较:(i)自我参照推文与其他主题推文之间的比较;(ii)文本作品编码与表情符号编码之间的比较;最后(iii)使用不同情感分析工具(语言查询和词频统计-LIWC;效价感知词典和情感推理器-VADER以及胡刘情感词典-胡刘)对文本作品进行编码之间的比较。在一个包含超过700万条自我参照和近1800万条其他主题内容编码推文的集合中,我们发现:(i)在自我参照推文和其他主题推文表达情绪的时间轨迹中,可以观察到形状和幅度方面的异同;(ii)所有工具在两个数据集中均呈现出显著的昼夜节律和七日节律模式,但并非总是如此,并且昼夜节律和七日节律模式的形状通常存在对应关系;最后(iii)从表情符号中的情感表达编码获得的昼夜节律和七日节律模式有时与文本分析的模式不同,这表明两种表达模式的使用存在一定的互补性。我们从关于情绪和幸福感的文献角度讨论了我们研究结果的意义。