# User Representation Learning for Social Networks:
## 3 Datasets  
An important goal of this study is to explore the performance of different types of datasets in the training of user representation models. A great deal of work is undertaken in the preparation of the datasets. In the previous study [17], 100 Twitter users from each of the fields of Economy, Crypto Economy, Politics, Technology, and Fashion were manually selected and the most recent 1000 tweets, which had been composed by these 500 users, were collected using the Twitter REST API. With this method, a gold standard dataset consisting of 500K labeled (user, group) tweets was built. A separate Twitter profile was created to be used in the process of selecting users for each category. A small number of users were selected as a starting point by examining various articles and blogs on the internet such as \"Top Economics Influencers\" and \"Who to follow on Twitter for technology\". We then identified a total of 500 profiles by visiting more than 5000 profiles with the help of the automatic \"follow\" suggestions made by Twitter. We considered the presence of the following criteria in the manual examination of the profiles:  
* Relevance: High degree of relevance to the specified category. * Popularity: High number of followers (at least a few thousand followers). * Posting behavior: Being an active Twitter user who tweets occasionally. Sixty percent of the users in each group are separated and a training dataset is created using the tweets of these randomly selected users. The tweets of the remaining 40% of the users are used as the test dataset. The training dataset is named as 300K-TW. 300K-TW is used as a source and expanded by enriching it with additional information to create two new datasets. Location and description information of the users were also collected with Twitter REST API. We call the username and location dataset as NL. A related study proposed an approach for combining such non-textual discrete features for better user representations [33]. It should be noted that some users do not share their location or description information. A second dataset named as NLD-RLC is created with combining the data from NL with user description and some additional network features. These additional features are lists of users' names that engaged with the tweets in any of the following ways: retweet, like, or comment. The purpose of the NL and NLD-RLC datasets are to enrich the feature pool, therefore, they are not intended to be used solely by themselves. All of the datasets are briefly described in Table 1. A test dataset version of each of the 300K-TW, NL, NLD-RLC datasets also exists. They are obtained in the same fashion described above but are not given separately merely for reasons of simplicity. As far as we know, collecting interaction data over semantically grouped social media users is an untested approach. The NLD-RLC dataset contains the usernames of the Twitter accounts that interacted with the tweets in the 300K-TW dataset. The standard Twitter API and the wrappers implemented in different programming languages that built around it such as Tweepy, Twarc, Twitter4J, and Bear, provide ways to collect \"retweet\", \"comment\" and \"like\" activity data of Twitter accounts. In other words, one can get the list of tweets that are retweeted/commented/liked by a user through the APIs (within the bounds of rate-limiting). However, to our knowledge, getting the list of users who retweeted/commented/liked a given tweet is not provided by the free, Standard Twitter API, yet there are some workarounds for this. However, they are mostly insufficient, because the availability of the results is particularly affected by the popularity of the accounts and by the time factor. Limits also occur in the case of manually viewing the lists on a web browser. Twitter shows only up to 100 users. The NLD-RLC dataset was scraped by automating the interactive steps of browsing the lists of each tweet in the 300K-TW dataset.