Bragging is the act of uttering statements that are likely to be positively
viewed by others and it is extensively employed in human communication with the
aim to build a positive self-image of oneself. Social media is a natural
platform for users to employ bragging in order to gain admiration, respect,
attention and followers from their audiences. Yet, little is known about the
scale of bragging online and its characteristics. This paper employs
computational sociolinguistics methods to conduct the first large scale study
of bragging behavior on Twitter (U.S.) by focusing on its overall prevalence,
temporal dynamics and impact of demographic factors. Our study shows that the
prevalence of bragging decreases over time within the same population of users.
In addition, younger, more educated and popular users in the U.S. are more
likely to brag. Finally, we conduct an extensive linguistics analysis to unveil
specific bragging themes associated with different user traits.

本文利用计算社会语言学方法，首次对美国推特上的炫耀行为进行了大规模研究，包括炫耀的普遍性、时间动态和人口统计因素的影响。研究发现，相同用户群体中的炫耀行为随时间减少。此外，在美国，年轻、受教育程度较高和受欢迎的用户更容易炫耀。最后，我们还进行了广泛的语言学分析，揭示了不同用户特征下的具体炫耀主题。

社交媒体中谁更多地吹嘘？大规模吹嘘分析

Who is bragging more online? A large scale analysis of bragging in  social media

The present paper introduces new sentiment data, MaCMS, for
Magahi-Hindi-English (MHE) code-mixed language, where Magahi is a
less-resourced minority language. This dataset is the first
Magahi-Hindi-English code-mixed dataset for sentiment analysis tasks. Further,
we also provide a linguistics analysis of the dataset to understand the
structure of code-mixing and a statistical study to understand the language
preferences of speakers with different polarities. With these analyses, we also
train baseline models to evaluate the dataset's quality.

本文介绍了用于情感分析任务的新情感数据集 MaCMS，包含马加希语 - 印地语 - 英语（MHE）混合编码语言，这是第一个用于情感分析任务的马加希语 - 印地语 - 英语混合编码数据集。此外，我们还对数据集进行了语言学分析，以了解代码混合的结构，并进行了统计研究以了解具有不同极性的说话者的语言偏好。通过这些分析，我们还训练了基准模型来评估数据集的质量。