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39 results for “reddit”
Reddit's NetSec forum GitHub projects, typology, maturity and popularity indicators
<p>Dataset including information of GitHub projects shared on Reddit's forum /r/netsec. It includes information about the posts itselves, like the author, title, image, tags, number of upvotes and number of comments, as well as repository information, such as number of stars, forks, issues, collaborators and the repository URL and last modification date.</p>
Dataset — Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald
<p>Reddit contents and complementary data regarding the r/The_Donald community and its main moderation interventions, used for the corresponding article indicated in the title.</p> <p>An accompanying R notebook can be found in: <a href="https://github.com/amauryt/make_reddit_great_again">https://github.com/amauryt/make_reddit_great_again</a></p> <p><strong>If you use this dataset please cite the related article.</strong></p> <p>The dataset timeframe of the Reddit contents (submissions and comments) spans from 30 weeks before <em>Quarantine</em> (2018-11-28) to 30 weeks after <em>Restriction</em> (2020-09-23). The original Reddit content was collected from the <a href="https://files.pushshift.io/reddit/">Pushshift monthly data files</a>, transformed, and loaded into two SQLite databases.</p> <p>The first database, <em>the_donald.sqlite</em>, contains all the available content from r/The_Donald created during the dataset timeframe, with the last content being posted several weeks before the timeframe upper limit. It only has two tables: <em>submissions</em> and <em>comments</em>. It should be noted that the IDs of contents are on base 10 (numeric integer), unlike the original base 36 (alphanumeric) used on Reddit and Pushshift. This is for efficient storage and processing. If necessary, many programming languages or libraries can easily convert IDs from one base to another.</p> <p>The second database, <em>core_the_donald.sqlite</em>, contains all the available content from core users of r/The_Donald made platform-wise (i.e., within and without the subreddit) during the dataset timeframe. Core users are defined as those who authored either a submission or a comment a week in r/The_Donald during the 30 weeks prior to the subreddit's Quarantine. The database has four tables: <em>submissions</em>, <em>comments</em>, <em>subreddits</em>, and <em>perspective_scores</em>. The <em>subreddits</em> table contains the names of the subreddits to which submissions and comments were made (their IDs are also on base 10). The <em>perspective_scores</em> table contains comment toxicity scores.</p> <p>The <a href="https://www.perspectiveapi.com/">Perspective API</a> was used to score comments based on the attributes <em>toxicity</em> and <em>severe_toxicity</em>. It should be noted that not all of the comments in <em>core_the_donald</em> have a score because the comment body was blank or because the Perspective API returned a request error (after three tries). However, the percentage of missing scores is minuscule.</p> <p>A third file, <em>mbfc_scores.csv</em>, contains the bias and factual reporting accuracy collected in October 2021 from <a href="https://mediabiasfactcheck.com/">Media Bias / Fact Check</a> (MBFC). Both attributes are scored on a Likert-like manner. One can associate submissions to MBFC scores by doing a <em>join</em> by the <em>domain</em> column.</p>
Webis-Persuasive-Debaters-on-Reddit-CMV-2022
<p><strong>Analyzing Persuasion Strategies of Debaters on Social Media - Dataset</strong></p> <p>This dataset contains 3,801 debaters from Reddit, their comment, and their persuasion effectiveness. The debates originate from the subreddit <a href="https://www.reddit.com/r/cmv">Chage my View</a> and are extracted from the Webis CMV dataset (<a href="https://aclanthology.org/2020.acl-main.632/">Al Khatib et al., 2020</a>). The dataset consists of three files in <a href="https://jsonlines.org/">JSON Lines</a> (.jsonl) format.</p> <p><strong>Content of the Dataset</strong></p> <pre>+-- Reddit Debaters | +-- README.md # This information + file format description | +-- debaters.jsonl # Minimal dataset with only the (source) comment text and persuasiveness | +-- debaters-full.jsonl # All debater-level datapoints, computed an retrieved from Reddit | +-- comments.jsonl # All comments and comment-level datapoints for each debater </pre> <p><strong>Cite</strong></p> <pre>@inproceedings{wiegmann:2022, title = "Analyzing Persuasion Strategies of Debaters on Social Media", author = "Wiegmann, Matti and Al-Khatib, Khalid and Khanna, Vishal and Stein, Benno", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", } </pre>
Nofap image logo on reddit community
Nofap reddit logo. converted from image to .obj file using https://cs-pattern.com/viewtopic.php?t=409 Source: Objaverse 1.0 / Sketchfab
NGI related Reddit comments dataset - With unzipped monthly files
<p>Reddit comments from January 2017 to August 2018, related to the posts including in their title some keywords related to NGI topics.</p> <p>Note that the month of March 2017 is not available. </p>
reddit Dataset
<p>The WE1S <code>reddit</code> dataset contains 1,034,174 Reddit comments containing the terms "humanities", "liberal arts", or "the arts", downloaded by Raymond Steding using <a href="https://pushshift.io/">pushshift.io</a>. Initially, comments posted between 2006 and 2018 were collected. Comments from 2019 were later added.</p> <p>This data has been processed using the WhatEvery1Says preprocessor, and, in addition to metadata downloaded from Reddit, sentiment scores generated with <a href="https://textblob.readthedocs.io/en/dev/">Textblob</a> have been recorded.</p> <p>A description of the process at an early stage in the production of this dataset can be found in Steding's blog post <a href="https://we1s.ucsb.edu/research_post/a-digital-humanities-study-of-reddit-student-discourse-about-the-humanities/">"A Digital Humanities Study of Reddit Student Discourse about the Humanities"</a>.</p> <p><em>(See <a href="https://we1s.ucsb.edu/research/we1s-materials/">WE1S Research Materials Overview</a> for the relation between the project's "datasets" and "collections.")</em></p>
Reddit Comments Dataset for Text Style Transfer Tasks
<p><strong>Reddit Comments Dataset for Text Style Transfer Tasks</strong></p> <p>A dataset of Reddit comments prepared for Text Style Transfer Tasks.</p> <p>The dataset contains Reddit comments translated into a formal language. For the translation of Reddit comments into a formal language text-davinci-003 was used. To make text-davinci-003 translate the comments into a more formal version, the following prompt was used:<br> "Here is some text: {original_comment} Here is a rewrite of the text, which is more neutral: {"<br> This prompting technique was taken from <a href="https://arxiv.org/abs/2109.03910">A Recipe For Arbitrary Text Style Transfer with Large Language Models</a>.</p> <p>The dataset contains comments from the following Subreddits: antiwork, atheism, Conservative, conspiracy, dankmemes, gaybros, leagueoflegends, lgbt, libertarian, linguistics, MensRights, news, offbeat, PoliticalCompassMemes, politics, teenagers, TrueReddit, TwoXChromosomes, wallstreetbets, worldnews.</p> <p>The quality of formal translations was assessed with BERTScore and chrF++:</p> <ul> <li>BERTScore: F1-Score: 0.89, Precision: 0.90, Recall: 0.88</li> <li>chrF++: 37.16</li> </ul> <p>The average perplexity of the generated formal texts was calculated using GPT-2 and is 123.77</p> <p><br> The dataset consists of 3 components.</p> <p><strong>reddit_commments.csv</strong></p> <p>This file contains a collection of randomly selected comments from 20 Subreddits. For each comment, the following information was collected:<br> - subreddit (name of the subreddit in which the comment was posted)<br> - id (ID of the comment)<br> - submission_id (ID of the submission to which the comment was posted)<br> - body (the comment itself)<br> - created_utc (timestamp in seconds)<br> - parent_id (The ID of the comment or submission to which the comment is a reply)<br> - permalink (The URL to the original comment)-<br> - token_size (How many tokens the comment will be split into by the standard GPT-2 tokenizer)<br> - perplexity (What perplexity does GPT-2 calculate for the comment)</p> <p>The comments were filtered. This file contains only comments that:<br> - have been split by GPT-2 Tokenizer into more than 10 tokens but less than 512 tokens.<br> - are not [removed] or [deleted]<br> - do not contain URLs</p> <p>This file was used as a source for the other two file types.</p> <p><strong>Labeled Files</strong> (training_labeled.csv and eval_labeled.csv)</p> <p>These files contain the formal translations of the Reddit comments.</p> <p>The 150 comments with the highest calculated perplexity of GPT-2 from each Subreddit were translated into a formal version. This filter was used to translate as many comments as possible that have large stylistic salience.</p> <p>They are structured as follows:<br> - Subreddit (name of the subreddit where the comment was posted).<br> - Original Comment<br> - Formal Comment</p> <p><strong>Labeled Files with Style Examples </strong>(training_labeled_with_style_samples.json and eval_labeled_with_style_samples.json)</p> <p>These files contain an original Reddit comment, three sample comments from the same subreddit, and the formal translation of the original Reddit comment.</p> <p>These files can be used to train models to perform style transfers based on given examples.<br> The task is to transform the formal translation of the Reddit comment, using the three given examples, into the style of the examples.</p> <p>An entry in this file is structured as follows:</p> <p>"data":[<br> {<br> "input_sentence":"The original Reddit comment",<br> "style_samples":[<br> "sample1",<br> "sample2",<br> "sample3"<br> ],<br> "results_sentence":"The formal translated input_sentence",<br> "subreddit":"The subreddit from which the comments originated"<br> },<br> "..."<br> ]</p> <p> </p>
nathanaelpganata/dataset-vtuber-reddit-comments: Dataset VTuber Reddit Comments
<p>Dataset of Reddit comments regarding VTubers Rushia and Mafumafu controversies, retrieved using web scraping tools.</p>
Smart Contracts Posts and Topic on Reddit
<p>A dataset including two CSVs collecting Reddit posts including respectively:<br>- Permalink: The post's link<br>- Title: The post's title<br>- author: The author's name.<br>- authorUrl: The URL to the author's profile<br>- commentCount: The number of comments related to the post<br>- id: The id of the post<br>-createdDate: The creation date of the reddit post<br>- query: Contains the URL of the board<br>- category: The category related to the subreddit post<br>- score: post score<br>- awardCount: number of awards<br>- silverCount: number of silver gildings<br>- goldCount: number of gold gildings<br>- platinumCount: number of platinum gildings<br>- upvoteRatio: the upvote ratio</p>
Reddit blackout announcements: 2023 API protest
<p>Starting June 12, 2023, <a href="https://www.reddit.com/r/ModCoord/comments/1401qw5/incomplete_and_growing_list_of_participating">many Reddit communities</a> (subreddits) began a protest where they "went dark" - by changing to private mode - as a protest in response to Reddit's plans to <a href="https://www.reddit.com/r/reddit/comments/12qwagm/an_update_regarding_reddits_api/">change its API access policies and fee structure</a>. Supporters of the protest criticize the planned changes for being <a href="https://www.reddit.com/r/apolloapp/comments/13ws4w3/had_a_call_with_reddit_to_discuss_pricing_bad/">prohibitively expensive for 3rd party apps</a>. Beyond 3rd party apps, there is significant concern that the API changes are a move by the platform to increase monetization, degrade the user experience, and eventually kill off other custom features such as the old.reddit.com interface, the Reddit Enhancement Suite browser extension, and more. Additionally, there are concerns that the API changes will impede the ability of subreddit moderators (who are all unpaid users) to access tools to keep their communities on-topic and free of spam.</p> <p>This dataset includes the "stickied" posts that appeared on 5,351 subreddits on June 11, 2023 and June 12, 2023 - including many subreddits announcing their plans to participate in the protest. These posts were scraped using a custom Python script that was written specifically for this purpose. Ironically, the script uses the PRAW (Python Reddit API Wrapper) library, requiring a valid Reddit API key. Accordingly, after the platform's new API pricing policy went into effect, it is no longer feasible for researchers to perform this type of web scraping without external funding support.</p>
Reddit r/TheRedPill
<p>dataset for 221</p>
NGI related Reddit comments dataset
<p>Reddit comments from January 2017 to August 2018, related to the posts including in their title some keywords related with NGI topics.</p>
Bundesliga Reddit Posts
<p>All posts in the <a href="https://www.reddit.com/r/Bundesliga/">German Bundesliga: News & Highlights</a> subreddit, ordered by team</p>
Reddit's WallStreetBets Posts
<p>A DataSet with a collection of Posts published in Reddit forum called WallStreetBets</p>
Reddit blackout announcements: 2023 API protest
Open the record for dataset details and reuse information.
Portadas de Meneame.com y Reddit España
<p>Se trata de tres datasets, provenientes de tres fuentes diferentes, Meneame, Twitter, y Reddit. Para los tres datasets se ha capturado el máximo de información relevante con el proposito de realizar un estudio de la relevancia de la noticia.</p>
Reddit SuicideWatch and Mental Health Collection (SWMH) for Suicidal Ideation and Mental Disorder Detection
<p>We collect this dataset from some mental health-related subreddits in https://www.reddit.com/ to further the study of mental disorders and suicidal ideation. We name this dataset as Reddit SuicideWatch and Mental Health Collection, or SWMH for short, where discussions comprise suicide-related intention and mental disorders like depression, anxiety, and bipolar. We use the Reddit official API and develop a web spider to collect the targeted forums. This collection contains a total of 54,412 posts. Specific subreddits are listed in Table 4 of the below paper, as well as the number and the percentage of posts collected in the train-val-test split.</p> <p><strong>This dataset is only for research. Please request with your institutional email. </strong></p> <p>If you use this dataset, please cite the paper as:</p> <p>Ji, S., Li, X., Huang, Z. <em>et al.</em> Suicidal ideation and mental disorder detection with attentive relation networks. <em>Neural Comput & Applic</em> (2021). https://doi.org/10.1007/s00521-021-06208-y</p> <pre>@article{ji2021suicidal, title={Suicidal ideation and mental disorder detection with attentive relation networks}, author={Ji, Shaoxiong and Li, Xue and Huang, Zi and Cambria, Erik}, journal={Neural Computing and Applications}, year={2021}, publisher={Springer} }</pre>
Reddit photo Critique Dataset
<p><strong>IMPORTANT</strong>: Dataset is now completely accessible from Github: <a href="https://github.com/mediatechnologycenter">https://github.com/mediatechnologycenter</a>/aestheval</p> <p>Post IDs available here: <a href="https://zenodo.org/record/6985507">https://zenodo.org/record/6985507</a></p> <p> </p> <p>The Reddit Photo Critique Dataset (RPCD) contains tuples of image and photo critiques. RPCD consists of 74K images<br> and 220K comments and is collected from a Reddit community used by hobbyists and professional photographers to improve their photography skills by leveraging constructive community feedback. The proposed dataset differs from previous aesthetics datasets mainly in three aspects, namely (i) the large scale of the dataset and the extension of the comments criticizing different aspects of the image, (ii) it contains mostly UltraHD images, and (iii) it can easily be extended to new data as it is collected through an automatic pipeline.</p> <p> </p> <p>More info about the dataset can be found at the Github repo: https://github.com/mediatechnologycenter/aestheval</p>
Detecting Symptoms of Depression on Reddit
<p>Depression is known to have heterogeneous symptom manifestations. Investigating various symptoms of depression is essential to understanding underlying mechanisms and personalizing treatments. </p> <p>We use Reddit posts from depression and mental health-related subreddits to detect symptoms of depression in a distantly supervised manner. Specifically:</p> <p>- We identified the online language markers of 13 symptoms of depression using 1,318,749 posts from 43 subreddit communities.<br> - We built 13 prediction models (based on RoBERTa embeddings) that can detect specific symptom discourse vs. posts from control subreddits contributed by the same Reddit users.<br> - We validated the prediction models on a sample who shared their Facebook posts and also took self-report depression (PHQ-9), anxiety (GAD-7), and loneliness (UCLA-3) surveys.</p> <p>The description of the data and models is part of our paper published at the 15th ACM Web Science Conference 2023 as a full paper.</p> <p><br> <strong>Brief Description of the Lexica </strong></p> <p>We employed happierfuntokenizer from the DLATK Python library to tokenize all posts. Using Latent Dirichlet Allocation (LDA) with MALLET implementation, we generated 200 topics at an alpha level of 5. We then analyzed the topic distribution of all posts in the Reddit dataset for each depression symptom. </p> <p>The current data includes: </p> <ul> <li>symptom vs. control</li> <li>symptom vs. control + all other symptoms</li> </ul> <p>To learn more about how to utilize the lexica, please refer to this link: <a href="https://github.com/sjgiorgi/dlatk-lexica">https://github.com/sjgiorgi/dlatk-lexica</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.