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Research in social media analysis is experiencing a recent surge with a largenumber of works applying representation learning models to solve high-levelsyntactico-semantic tasks such as sentiment analysis, semantic textualsimilarity computation, hashtag prediction and so on. Although the performanceof the representation learning models are better than the traditional baselinesfor the tasks, little is known about the core properties of a tweet encodedwithin the representations. Understanding these core properties would empowerus in making generalizable conclusions about the quality of representations.Our work presented here constitutes the first step in opening the black-box ofvector 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 modeleach of those properties such as tweet length, presence of words, hashtags,mentions, capitalization, and so on. This is done with the help of multipleclassifiers which take the representation as input. Essentially, eachclassifier evaluates one of the syntactic or social properties which arearguably salient for a tweet. This is also the first holistic study onextensively analysing the ability to encode these properties for a wide varietyof tweet representation models including the traditional unsupervised methods(BOW, LDA), unsupervised representation learning methods (Siamese CBOW,Tweet2Vec) as well as supervised methods (CNN, BLSTM).

Interpreting the Syntactic and Social Elements of the Tweet Representations via Elementary Property Prediction Tasks