# User Representation Learning for Social Networks:
###### Abstract  
Gathering useful insights from social media data has gained great interest over the recent years. User representation can be a key task in mining publicly available user-generated rich content offered by the social media platforms. The way to automatically create meaningful observations about users of a social network is to obtain real-valued vectors for the users with user embedding representation learning models. In this study, we presented one of the most comprehensive studies in the literature in terms of learning high-quality social media user representations by leveraging state-of-the-art text representation approaches. We proposed a novel doc2 vec-based representation method, which can encode both textual and non-textual information of a social media user into a low dimensional vector. In addition, various experiments were performed for investigating the performance of text representation techniques and concepts including word2 vec, doc2 vec, Glove, NumberBatch, FastText, BERT, ELMO, and TF-IDF. We also shared a new social media dataset comprising data from 500 manually selected Twitter users of five predefined groups. The dataset contains different activity data such as comment, retweet, like, location, as well as the actual tweets composed by the users. distributed representation; user representation; user embedding; social networks +
Footnote †: journal: Medical  
corpus. However, there are other ways to show the power of word vectors. Word vectors are used against other word representation methods in NLP problems such as clustering and classification. Their performance is analyzed with task-oriented evaluations. It is possible to represent social media users in semantic space, just like words, and to conduct various analogies as a measure of accuracy between these representations. One of the most difficult challenges in social media research is dealing with complex unstructured textual and network data. The use of emerging word embedding and document embedding techniques has been enhancing information retrieval, recommendation systems, security, and many others [4]. State-of-the-art distributed semantic representation techniques can be categorized into three classes [5]: classical word embeddings, character-level features, and contextualized word embeddings. Definitions of the state-of-the-art text representation methods that are used in this study are given as follows:  
Pre-Trained Word Embeddings are general purpose word vectors that are learned by training over large text corpora. Using pre-trained word embeddings is considered as a kind of transfer learning as they are used in improving the performance of other text processing tasks such as document classification and clustering, entity recognition, etc. Word embeddings are used as input features in deep learning models for solving NLP problems. Word2 vec and Glove are the best-known word-embedding algorithms. Word2 Vec is a neural network-based algorithm for training word embeddings [6]. It learns the semantic similarities between words with a simple fully connected layer. Word2 vec is based on the \"You shall know a word by the company it keeps\" idea of distributional hypothesis, which means that similar words are likely to appear close to each other in a corpus [7]. This results in similar words having closer vectors in the semantic space. Word2 vec algorithm introduces two architectures: Skip-gram and CBOW. While Skip-gram method learns the embeddings by predicting the surrounding context words of a given word, CBOW method predicts a target word given the context words. Global Vectors (also known as GloVe) is a log-bilinear regression model for word representation [8]. This model learns word embeddings using word co-occurrence matrix given a corpus and captures the global corpus statistics. In this aspect, while Word2 vec is considered as a predictive model, GloVe is considered as a count-based model that takes into account statistics of word occurrences.