The concept of reposting also covers new posts containing only information that has already been posted [22]. The number of subscribers and posts in the other COVID-19 subreddits are clearly lower and address more specific aspects; therefore, in this work, our data source was r/Coronavirus. Users submit top-level postings, known as submissions, to each subreddit, while others respond with comments on the submissions. Submissions consist of a title (up to 300 characters) and either a web link or a user-supplied body text; in the latter case, the submission is also known as a self-post, while comments are always made up of a body text. In this work, we focus on analyzing the titles of Reddit posts. There are two reasons why we believe titles will be a useful basis for NLP analysis. First, Reddit strongly recommends double-checking the grammar, spelling, and punctuation of the titles: \"Read over your submission for mistakes before submitting, especially the title of the submission. Comments and the content of self-posts can be edited after being submitted; however, the title of a post cannot be. Make sure the facts you provide are accurate to avoid any confusion\" [23]. Second, Reddit also requests that posters make their titles factual, accurate, and relevant to the content of the post. As remarked in Rediquette: \"Please don't editorialize or sensationalize your submission title, keep your submission titles factual and opinion free. If it is an outrageous topic, share your crazy outrage in the comment section. Do not be vague. Make sure redditors know what they are getting. People do not have time to click on every submission to find out what is inside. Contribute value to the community by writing titles that accurately describe what is being shared. Be relevant. Subreddit subscribers like to read about specific topics that are related to their subreddit. If your submission is out of place, it will not gain any attention\" [23]. Figure 1: Number of posts per day for the Music and the Coronavirus subreddits. Another advantage of focusing on Reddit post titles is that Twitter has increased the available character space from 140 to 280 characters since November 2017, which is very similar to Reddit's 300 characters limitation of the titles. This provides an opportunity for linguistic comparisons between tweets and Reddit titles. It is for these reasons that we focused our analysis on the titles of all posts extracted from r/Coronavirus. A word embedding is a vector-based representation of a word. The vector representing a word can be understood as the coordinates of a word's position within a multi-dimensional feature space (where the dimensions of the feature space are equal to the size of the vector). Within the vector-based representation, the meaning of a word is encoded by its position within the feature space relative to other words in the space. From a linguistic semantics perspective, the concept of word embedding is related to the distributional hypothesis for Firth [10], which can be paraphrased as \"you shall know the meaning of a word by the company it keeps.\" The relationship between the distributional hypothesis and word embeddings is that in well-trained word embedding models, words that occur in similar contexts (i.e., that keep the same company) are positioned close to each other in the feature space (i.e., they have similar vector representations). Word2 vec was created, patented, and published in two papers in 2013 by a team of researchers led by [PERSON] at Google to learn word embeddings from a corpus of language [12]. It creates embeddings for the words in a corpus by training a neural network to predict words that co-occur with other words in the corpus. Word2 Vec includes two alternative strategies for training the neural network: Continuous Bag of Words (CBOW) and Skip-gram. In both of them, a preset length window is moved along the corpus. Using the CBOW strategy, at each step, the network is trained to predict the word in the center of the window based on the surrounding words.