Showing 9-16 of 40 results
2012-2016 Facebook Posts

2012-2016 Facebook Posts

Stefan
Open Dataset
Creators: Martinchek, Patrick
Publication Date: 2016
Creators: Martinchek, Patrick

This dataset comprises Facebook posts from the 15 mainstream media sources during the years 2012 to 2016. It includes posts from the top mainstream media outlets, offering insights into their social media strategies and audience engagement during a significant period in digital media evolution.The dataset is structured to include various fields such as post content, timestamps, and engagement metrics like likes, shares, and comments. Each record represents a single Facebook post, allowing for detailed analysis of individual entries. It has a size of 861,17 MB.

Huge Collection of Reddit Votes

Huge Collection of Reddit Votes

Stefan
Open Dataset
Creators: Leake, Joseph
Publication Date: 2020
Creators: Leake, Joseph

The dataset covers data of over 44 million upvotes and downvotes cast by Reddit users between 2007 and 2020. This is a tab-delimited list of votes cast by reddit users who have opted-in to make their voting history public. Each row contains the submission id for the thread being voted on, the subreddit the submission was located in, the epoch timestamp associated with the vote, the voter’s username, and whether it was an upvote or a downvote. The votes included are from users who have chosen to make their voting history public, ensuring compliance with privacy preferences. There’s a separate file containing information about the submissions that were voted on. The dataset contains over 44 million voting records and has a size of 21,9 kB. Structurally, the dataset is organized into two main components:​

  1. Votes Data: A tab-delimited file where each row represents a vote with the following fields:​

    • submission_id: Identifier of the Reddit submission that received the vote.​

    • subreddit: Name of the subreddit where the submission was posted.​

    • created_time: Epoch timestamp indicating when the vote was cast.​

    • username: Reddit username of the voter.​

    • vote: Type of vote, either ‘upvote’ or ‘downvote’.​

  2. Submissions Data: A separate file containing information about the submissions that received votes, including details such as submission titles, authors, and timestamps.

Characterizing Online Discussion Using Coarse Discourse Sequences

Characterizing Online Discussion Using Coarse Discourse Sequences

Stefan
Open Dataset
Creators: Zhang, Amy; Culbertson, Brian; Paritosh, Praveen
Publication Date: 2017
Creators: Zhang, Amy; Culbertson, Brian; Paritosh, Praveen

In this work, we present a novel method for classifying comments in online discussions into a set of coarse discourse acts towards the goal of better understanding discussions at scale. To facilitate this study, we devise a categorization of coarse discourse acts designed to encompass general online discussion and allow for easy annotation by crowd workers. We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit. Using our corpus, we demonstrate how the analysis of discourse acts can characterize different types of discussions, including discourse sequences such as Q&A pairs and chains of disagreement, as well as different communities. Finally, we conduct experiments to predict discourse acts using our corpus, finding that structured prediction models such as conditional random fields can achieve an F1 score of 75%. We also demonstrate how the broadening of discourse acts from simply question and answer to a richer set of categories can improve the recall performance of Q&A extraction.

Tracking Mastodon user numbers over time

Tracking Mastodon user numbers over time

Stefan
Open Dataset
Creators: Willison, Simon
Publication Date: 2022
Creators: Willison, Simon

Mastodon is definitely having a moment. User growth is skyrocketing as more and more people migrate over from Twitter. I’ve set up a new git scraper to track the number of registered user accounts on known Mastodon instances over time. The dataset collects data from numerous Mastodon instances, providing a holistic view of user distribution across the network. This approach captures the decentralized nature of Mastodon, offering insights into individual server growth and overall network expansion. By recording user numbers at regular intervals, the dataset enables the analysis of growth patterns over time, identifying trends and significant adoption milestones. The dataset includes user counts from approximately 1,830 Mastodon instances, with data points collected approximately every 20 minutes. This frequency allows for detailed temporal analysis of user growth. Data collection began on November 20, 2022, and has continued since then, capturing the rapid growth of Mastodon following significant events such as changes in other social media platforms.

The dataset is structured with each record representing a snapshot of user numbers across various Mastodon instances at a specific timestamp. Key fields include:​

  • Instance Name: The domain name of the Mastodon instance.​

  • User Count: The number of registered users on the instance at the time of data collection.​

  • Timestamp: The date and time when the data was collected.​

Stack Exchange Data

Stack Exchange Data

Stefan
Open Dataset
Creators: Stack Exchange Inc.
Publication Date: 2014
Creators: Stack Exchange Inc.

This is an anonymized dump of all user-contributed content on the Stack Exchange network. Each site is formatted as a separate archive consisting of XML files zipped via 7-zip using bzip2 compression. Each site archive includes Posts, Users, Votes, Comments, PostHistory and PostLinks. The dataset covers detailed records of questions, answers, comments, user profiles, and other related metadata from numerous Stack Exchange communities. This breadth allows for in-depth analysis of community interactions, content evolution, and knowledge dissemination patterns. The dataset has a size of 92,3 GB and captures content from the inception of each Stack Exchange site up to the date of the specific data dump. For example, the September 2023 release includes data up to that month. Structurally, the database is organized into individual archives for each Stack Exchange community. Each archive contains several XML files representing different data tables:​

  • Posts.xml: Contains both questions and answers, with fields detailing post ID, creation date, score, body content, and related metadata.​

  • Users.xml: Includes user information such as user ID, reputation, creation date, and profile details.​

  • Comments.xml: Encompasses comments made on posts, including comment ID, post ID, user ID, and content.​

  • Votes.xml: Records voting data on posts, detailing vote type, user ID, and timestamps.

3 Million Russian troll tweets

3 Million Russian troll tweets

Stefan
Open Dataset
Creators: FiveThirtyEight; Warren, Patrick ;Linvill, Darren
Publication Date: 2018
Creators: FiveThirtyEight; Warren, Patrick ;Linvill, Darren

This directory contains data on nearly 3 million tweets sent from Twitter handles connected to the Internet Research Agency, a Russian “troll factory” and a defendant in an indictment filed by the Justice Department in February 2018, as part of special counsel Robert Mueller’s Russia investigation. The tweets in this database were sent between February 2012 and May 2018, with the vast majority posted from 2015 through 2017. Each entry includes detailed information such as the tweet’s content, author handle, language, publication date, and engagement metrics (e.g., number of followers, following count). The dataset provides classifications for each account, indicating the thematic focus (e.g., Right Troll, Left Troll, News Feed), as coded by researchers Darren Linvill and Patrick Warren.​ It has a total size of 507,2 kB.

Facebook Social Connectedness Index

Facebook Social Connectedness Index

Stefan
Open Dataset
Creators: Meta
Publication Date: 2021
Creators: Meta

We use an anonymized snapshot of all active Facebook users and their friendship networks to measure the intensity of connectedness between locations. The Social Connectedness Index (SCI) is a measure of the social connectedness between different geographies. Specifically, it measures the relative probability that two individuals across two locations are friends with each other on Facebook. Each entry represents a pair of locations, detailing the strength of social connectedness between them. By doing so, the SCI provides a measure of the relative probability that two individuals from different locations are Facebook friends, offering insights into social ties across regions. The dataset has a a size of 3,9 kB and reflects a specific snapshot in time, with the latest available data from October 2021. The dataset is organized into multiple sub-datasets, each detailing social connectedness at different geographic levels:​

  1. Country-Country Pairs:

    • user_loc: ISO2 code of the first country.​

    • fr_loc: ISO2 code of the second country.​

    • scaled_sci: Scaled Social Connectedness Index between the two countries.​

  2. US County-Country Pairs:

    • user_loc: 5-digit FIPS code of the U.S. county.​

    • fr_loc: ISO2 code of the country.​

    • scaled_sci: Scaled Social Connectedness Index between the U.S. county and the country.

​

Third Eye Data: TV News Archive chyrons

Third Eye Data: TV News Archive chyrons

Stefan
Open Dataset
Creators: TV News Archive
Publication Date: 2017
Creators: TV News Archive

The Third Eye: TV News Archive Chyrons dataset captures and analyzes the “lower third” text, known as chyrons, displayed during live TV news broadcasts. This dataset provides a unique look into the real-time editorial choices of major news networks, offering insights into how different media outlets frame news stories. Using Optical Character Recognition (OCR) technology, chyrons are extracted and archived continuously, making it possible to track how key topics are covered over time.

At its inception in September 2017, the dataset collected chyrons from four major news networks: BBC News, CNN, Fox News, and MSNBC. Within just two weeks of its launch, over four million chyrons had already been captured, highlighting the vast amount of real-time data available. The dataset has been continuously updated since, allowing for longitudinal studies of media framing and news presentation trends. It’s size is approximately 12.5 kB in TSV format.

The dataset is structured into several key components. Each chyron entry includes:

  • The exact chyron text, showing the wording used by the network.
  • Timestamps, allowing analysis of how frequently specific topics appear.
  • Channel identifiers, enabling comparisons between different networks.
  • Duration data, indicating how long a chyron remained on screen, which can suggest emphasis or prioritization of certain stories.

By leveraging this dataset, researchers, journalists, and media analysts can examine bias in news presentation, media influence on public perception, and breaking news coverage trends. It serves as a powerful tool for studying news framing, editorial strategies, and the evolution of televised news narratives across competing networks.

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