Research in analysis of microblogging platforms is experiencing a renewed
surge with a large number of works applying representation learning models for
applications like sentiment analysis, semantic textual similarity computation,
hashtag prediction, etc. Although the performance of the representation
learning models has been better than the traditional baselines for such tasks,
little is known about the elementary properties of a tweet encoded within these
representations, or why particular representations work better for certain
tasks. Our work presented here constitutes the first step in opening the
black-box of vector embeddings for tweets. Traditional feature engineering
methods for high-level applications have exploited various elementary
properties of tweets. We believe that a tweet representation is effective for
an application because it meticulously encodes the application-specific
elementary properties of tweets. To understand the elementary properties
encoded in a tweet representation, we evaluate the representations on the
accuracy to which they can model each of those properties such as tweet length,
presence of particular words, hashtags, mentions, capitalization, etc. Our
systematic extensive study of nine supervised and four unsupervised tweet
representations against most popular eight textual and five social elementary
properties reveal that Bi-directional LSTMs (BLSTMs) and Skip-Thought Vectors
(STV) best encode the textual and social properties of tweets respectively.
FastText is the best model for low resource settings, providing very little
degradation with reduction in embedding size. Finally, we draw interesting
insights by correlating the model performance obtained for elementary property
prediction tasks with the highlevel downstream applications.

使用多种模型，比较了 9 个 tweet 表征表示在 8 个文本和 5 个社交元素属性上的表现，Bi-directional LSTM（BLSTM）和 Skip-Thought Vectors 最能编码文本和社交属性，在低资源情况下表现不错的是 FastText。

语义推文表示的解释

Interpretation of Semantic Tweet Representations

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。