Social media creates crucial mass changes, as popular posts and opinions cast a significant influence on users' decisions and thought processes. For example, the recent Reddit uprising inspired by r/wallstreetbets which had remarkable economic impact was started with a series of posts on the thread. The prediction of posts that may have a notable impact will allow for the preparation of possible following trends. This study aims to develop a machine learning model capable of accurately predicting the popularity of a Reddit post. Specifically, the model is predicting the number of upvotes a post will receive based on its textual content. I experimented with three different models: a baseline linear regression model, a random forest regression model, and a neural network. I collected Reddit post data from an online data set and analyzed the model's performance when trained on a single subreddit and a collection of subreddits. The results showed that the neural network model performed the best when the loss of the models were compared. With the use of a machine learning model to predict social trends through the reaction users have to post, a better picture of the near future can be envisioned.
翻译:社交媒体产生了至关重要的大规模变化,因为流行的海报和观点对用户的决定和思维过程产生了重大影响。例如,最近由r/wallstreetbet 引发的、具有显著经济影响的Reddit暴动开始于线上的一系列文章。预测可能具有显著影响的职位,将有助于为可能的以下趋势做准备。这项研究旨在开发一个机器学习模型,能够准确预测Reddit 文章的受欢迎程度。具体地说,该模型正在预测基于其文字内容的一篇文章将收到的高音数量。我试验了三种不同的模型:基线线性回归模型、随机森林回归模型和神经网络。我从一个在线数据集中收集了Reddit 数据并分析了模型在就单一子编辑和子编辑集进行培训时的性能。结果显示,当模型丢失时,神经网络模型表现最佳。使用机器学习模型通过反应用户预测社会趋势时,可以设想出一个更美的近期前景。