Research in social media analysis is experiencing a recent surge with a large
number of works applying representation learning models to solve high-level
syntactico-semantic tasks such as sentiment analysis, semantic textual
similarity computation, hashtag prediction and so on. Although the performance
of the representation learning models are better than the traditional baselines
for the tasks, little is known about the core properties of a tweet encoded
within the representations. Understanding these core properties would empower
us in making generalizable conclusions about the quality of representations.
Our work presented here constitutes the first step in opening the black-box of
vector embedding for social media posts, with emphasis on tweets in particular.
In order to understand the core properties encoded in a tweet representation,
we evaluate the representations to estimate the extent to which it can model
each of those properties such as tweet length, presence of words, hashtags,
mentions, capitalization, and so on. This is done with the help of multiple
classifiers which take the representation as input. Essentially, each
classifier evaluates one of the syntactic or social properties which are
arguably salient for a tweet. This is also the first holistic study on
extensively analysing the ability to encode these properties for a wide variety
of tweet representation models including the traditional unsupervised methods
(BOW, LDA), unsupervised representation learning methods (Siamese CBOW,
Tweet2Vec) as well as supervised methods (CNN, BLSTM).

本研究对推特文本表示方法的核心属性进行评估和分析，旨在打开社交媒体文章向量表示的黑匣子，以便更好地理解这些方法为文本编码的方式以及其表现的能力，以有助于社交媒体信息处理的应用。