#Subverse release date 2020 archive#

These ten embedding models were learned from about 400 million tweets and 7 billion words from the general text.

The general data consist of news articles, Wikipedia data and other web data. In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets with the general data. In this paper, we present ten word embedding data sets. Therefore, it is necessary to have word embeddings learned specifically from tweets. Tweets are short, noisy and have unique lexical and semantic features that are different from other types of text. The embedding of a word captures both its syntactic and semantic aspects. They are usually generated from a large text corpus. Word embeddings have been used in many NLP tasks. Finally, our dataset can assist qualitative work focusing on in-depth case studies of specific narratives, events, or social theories.Ī word embedding is a low-dimensional, dense and real-valued vector representation of a word. For instance, we hope this dataset may be used for cross-platform studies of social media, as well as being useful for other types of research like natural language processing. Overall, we are confident that our work will motivate and assist researchers in studying and understanding 4chan, as well as its role on the greater Web. We also present a statistical analysis of the dataset, providing an overview of what researchers interested in using it can expect, as well as a simple content analysis, shedding light on the most prominent discussion topics, the most popular entities mentioned, and the toxicity level of each post. We augment the data with a set of additional labels, including toxicity scores and the named entities mentioned in each post.

#Subverse release date 2020 archive#

To the best of our knowledge, this represents the largest publicly available 4chan dataset, providing the community with an archive of posts that have been permanently deleted from 4chan and are otherwise inaccessible. This paper presents a dataset with over 3.3M threads and 134.5M posts from the Politically Incorrect board (/pol/) of the imageboard forum 4chan, posted over a period of almost 3.5 years (June 2016-November 2019). We also use word2vec models to identify narratives around QAnon-specific keywords, and our graph visualization shows that some of QAnon-related ones are closely related to those from the Pizzagate conspiracy theory and 'drops' by 'Q.' Finally, we analyze content toxicity, finding that discussions on /v/GreatAwakening are less toxic than in the broad Voat community.

To further understand the discourse around QAnon, we study the most popular named entities mentioned in the posts, along with the most prominent topics of discussion, which focus on US politics, Donald Trump, and world events. More precisely, we analyze a large dataset from /v/GreatAwakening, the most popular QAnon-related subverse (the Voat equivalent of a subreddit) to characterize activity and user engagement. In this paper, we provide an empirical exploratory analysis of the QAnon community on, a Reddit-esque news aggregator, which has recently captured the interest of the press for its toxicity and for providing a platform to QAnon followers. At the same time, governments are thought to be controlled by 'puppet masters,' as democratically elected officials serve as a fake showroom of democracy. Among these, the QAnon conspiracy theory has emerged in 2017 on 4chan, broadly supporting the idea that powerful politicians, aristocrats, and celebrities are closely engaged in a global pedophile ring. Online fringe communities offer fertile grounds for users to seek and share paranoid ideas fueling suspicion of mainstream news, and outright conspiracy theories.