What conclusions do the authors draw from their experiments?
Supervised models CNN, LSTM and BLSTM and unsupervised models BOW, DSSM, STV and T2V can  encapsulate most of the syntactic and social properties. Tweet length affects the task prediction accuracies for all models. LDA is insensitive to input word order, but, CNN, LSTM
and BLSTM are not.
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).
Research in social media analysis is recently seeing a surge in the number of research works applying representation learning models to solve high-level syntactico-semantic tasks such as sentiment analysis [1], semantic textual similarity computation [2], hashtag prediction [3] and so on. Though the performance of the representation learning models are better than the traditional models for all the tasks, little is known about the core properties of a tweet encoded within the representations. In a recent work, Hill et al. [4] perform a comparison of different sentence representation models by evaluating them for different high-level semantic tasks such as paraphrase identification, sentiment classification, question answering, document retrieval and so on. This type of coarse-grained analysis is opaque as it does not clearly reveal the kind of information encoded by the representations. Our work presented here constitutes the first step in opening the black-box of vector embeddings for social media posts, particularly tweets. Essentially we ask the following question: “What are the core properties encoded in the given tweet representation?”. We explicitly group the set of these properties into two categories: syntactic and social. Syntactic category includes properties such as tweet length, the order of words in it, the words themselves, slang words, hashtags and named entities in the tweet. On the other hand, social properties consist of `is reply', and `reply time'. We investigate the degree to which the tweet representations encode these properties. We assume that if we cannot train a classifier to predict a property based on its tweet representation, then this property is not encoded in this representation. For example, the model which preserves the tweet length should perform well in predicting the length given the representation generated from the model. Though these elementary property prediction tasks are not directly related to any downstream application, knowing that the model is good at modeling a particular property (e.g., the social properties) indicates that it could excel in correlated applications (e.g., user profiling task). In this work we perform an extensive evaluation of 9 unsupervised and 4 supervised tweet representation models, using 8 different properties. The most relevant work is that of Adi et al. [5], which investigates three sentence properties in comparing unsupervised sentence representation models such as average of words vectors and LSTM auto-encoders. We differ from their work in two ways: (1) While they focus on sentences, we focus on social media posts which opens up the challenge of considering multiple salient properties such as hashtags, named entities, conversations and so on. (2) While they work with only unsupervised representation-learning models, we investigate the traditional unsupervised methods (BOW, LDA), unsupervised representation learning methods (Siamese CBOW, Tweet2Vec), as well as supervised methods (CNN, BLSTM). Our main contributions are summarized below. The paper is organized as follows. Sections 2 and 3 discuss the set of proposed elementary property prediction tasks and the models considered for this study respectively. Section 4 and 5 presents the experiment setup and result analysis respectively. We conclude the work with a brief summary in Section 5.