  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.
