Formally announced to the public following former President Donald Trump’s bans and suspensions from mainstream social networks in early 2022 following his role in the January 6 Capitol Riots, Truth Social was launched as an ``alternative'' social media platform that claims to be a refuge for free speech, offering a platform for those disaffected by the content moderation policies of then existing, mainstream social networks. The subsequent rise of Truth Social has been driven largely by hard-line supporters of the former president as well as those affected by the content moderation of other social networks. These distinct qualities combined with the its status as the main mouthpiece of the former president positions Truth Social as a particularly influential social media platform and give rise to several research questions. However, outside of a handful of news reports, little is known about the new social media platform partially due to a lack of well-curated data. In the current work, we describe a dataset of over 823,000 posts to Truth Social and and social network with over 454,000 distinct users. In addition to the dataset itself, we also present some basic analysis of its content, certain temporal features, and its network.

Moral outrage has become synonymous with social media in recent years. However, the preponderance of academic analysis on social media websites has focused on hate speech and misinformation. This article focuses on analyzing moral judgments rendered on social media by capturing the moral judgments that are passed in the subreddit /r/AmITheAsshole on Reddit. Using the labels associated with each judgment, we train a classifier that can take a comment and determine whether it judges the user who made the original post to have positive or negative moral valence. Then, we employ human annotators to verify the performance of this classifier and use it to investigate an assortment of website traits surrounding moral judgments in ten other subreddits. Our analysis looks to answer three questions related to moral judgments and how these apply to different aspects of Reddit. We seek to determine whether moral valence impacts post scores, in which subreddit communities contain users with more negative moral valence, and whether gender and age play a role in moral judgments. Findings from our experiments show that users upvote posts more often when posts contain positive moral valence. We also find that certain subreddits, such as /r/confessions, attract users who tend to be judged more negatively. Finally, we found that men and older age were judged negatively more often.

Modern deep learning systems like PyTorch and Tensorflow are able to train enormous models with billions (or trillions) of parameters on a distributed infrastructure. These systems require that the internal nodes have the same memory capacity and compute performance. Unfortunately, most organizations, especially universities, have a piecemeal approach to purchasing computer systems resulting in a heterogeneous infrastructure, which cannot be used to compute large models. The present work describes HetSeq, a software package adapted from the popular PyTorch package that provides the capability to train large neural network models on heterogeneous infrastructure. Experiments with language translation, text and image classification shows that HetSeq scales over heterogeneous systems. Additional information, support documents, source code are publicly available at https://github.com/yifding/hetseq.