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.

对推文中的情绪的时间轨迹的研究表明，正情绪和负情绪都遵循非线性的昼夜节律（24小时）和昼夜节律（7天）模式。但至此，此类发现可能依赖于乐器，因为它们仅依赖于使用语言查询字数统计的编码。此外，研究表明，与其他类型的内容相比，自我指称内容对个人具有更高的相关性和意义。研究推文中情绪表达的时间模式中的自我指涉材料的特殊性很有趣，但是当前的研究是基于通用文本作品。迄今为止，尚未将通过表情符号在推文中分享的情感的时间变化与文字分析进行比较。因此，本研究着重于几个比较：（i）在自我引用推文与其他主题推文之间，（ii）在文本作品的编码与表情符号的编码之间，最后（iii）使用不同的情感分析工具（语言查询和字数统计-LIWC）在文本作品的编码之间；价数感知词典和情感推理器-VADER和胡柳情感词典-胡柳）。在超过700万条自我参考和近1800万条其他主题内容编码的推文中，我们发现（i）在自我表达之间的表达情绪的时间轨迹中，可以观察到形状和振幅方面的相似性和差异性。参考和其他主题推文，（ii）所有工具在两个数据集中都具有重要的昼夜节律和昼夜节律模式，但并非总是如此，并且通常存在昼夜节律和昼夜节律模式的对应关系，最后（iii）从表情符号中的情感表达编码中获得的昼夜节律和昼夜节律模式有时与文本分析不同，这表明在使用表情符号时存在一定的互补性。两种表达方式。我们从关于情感和幸福的文献的角度讨论研究结果的含义。