# User Representation Learning for Social Networks:
###### Abstract
FastText library [9] provides a simple and efficient method for word embeddings in text classification tasks. Unlike word2 vec, FastText uses n-grams so that the local word orders are not ignored. Fair is an NLP framework for text classification and language models [10]. Flair provides a simple interface for different types of word embeddings and language models such as word2 vec, GloVe, and Bert. BERT (Bidirectional Encoder Representations from Transformers) [11] and ELMO (Embeddings from Language Models) [12] have started to transform the word embedding methods by introducing context aware approaches. Prior to these models, the state-of-the-art word representation techniques were context-independent models such as Glove, Word2 vec, and FastText, which does not take into account word order in their training phase. Also, once their training phase is completed, they produce one vector for each word and the model is not needed afterwards. However, for the context aware approaches, when they are used in the downstream NLP tasks, their model is needed to produce vectors for words or sentences because the resulting representations vary each time depending on the input. For example, in the sentence \"The mayor of Batman plans to sue DC Comics for using his city's name in their Batman Comic book franchise\" the word \"[PERSON]\" has two very different meanings. First meaning is the city name in Turkey and the second is the fictional superhero from the movies like \"The Dark Knight\". In a static word representation method, the word Batman has only one vector, but in a dynamic representation system its vector is produced depending on the context. ELMO uses a character-based representation of the input text. Then, it applies character convolutions to these representations to obtain word level embeddings. The main part of the model is the LSTM (long short-term memory) layers where two language models are employed in opposite directions. The forward (right-to-left) and backward (left-to-right) representations obtained from the LSTM layers are concatenated in the output layer. BERT model, on the other hand, uses a newer neural architecture called transformers [13]. BERT uses sub-word tokens of the input text and constructs words from these tokens. Then, it is pre-trained on large unlabeled data for two unsupervised tasks, which are Next Sentence Prediction and Masked Language Model. After the pre-training is completed, the model is used in downstream NLP tasks. ConceptNet Numberbatch provides multilingual representations of words built on a combination of semantic knowledge graph and existing pre-trained word embeddings such as Glove and word2 vec [14]. It can leverage a human-made knowledge graph by using the retrofitting method for improving the vector values of pre-trained embedding models [15]. Doc2 vec model is a neural document embedding technique also known as Paragraph Vector. It is an extension to the word2 vec model for the representation of single sentences or documents [16]. In this model, a paragraph identifier is added to the input of the neural network created to learn word vectors, which will be called document id or paragraph id (1-hot encoded vector of the documents). Thus, the neural network is able to take the document id as input and learn the vector representations of the documents of different word lengths. Paragraph vector model presents two different architectures: Distributed Memory (PV-DM) and Distributed Bag-of Words (PV-DBOW). PV-DM works in a similar way to the CBOW word2 vec model, while PV-DBOW works in a similar way to the skip-gram word2 vec model. In the framework of this study, the vector space representation of the social media (Twitter) account is interchangeably referred to as user representation, user embedding, or user vector. Our assumption is that the user vector of a person from the fashion world would be closer to another person from the same world compared to a person from the world of politics.