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Inside Airbnb — Listing Data

Inside Airbnb — Listing Data

weimiao0112
Open Dataset
Creators: Murray Cox
Publication Date: Ongoing since February 2015
Creators: Murray Cox

Inside Airbnb provides listing-level data scraped from publicly available information on Airbnb.com for cities worldwide. Each snapshot contains the listing title and full-text description, host attributes (e.g., Superhost status, verifications, experience), property characteristics (room/property type, capacity, amenities, price and fees, cancellation policy), quality ratings, and review counts. The data is well suited to text/emoji analysis and to modelling how listing content relates to demand and electronic word-of-mouth.

Crowdfunding datasets

Crowdfunding datasets

david-stemper
Open Dataset
Creators: Web Robots
Publication Date: 2016-03-01
Creators: Web Robots

This dataset consists of large-scale web scraping projects that provide publicly available datasets of e-commerce product listings, reviews, pricing, and other related data from various sources such as Kickstarter and Indiegogo. These datasets are used for data mining, analysis, and machine learning applications, enabling users to explore trends in product performance, customer sentiment, and pricing strategies across multiple industries. Data collection is performed monthly, ensuring that the datasets remain up-to-date with the latest project information. ​The initial dataset release included data on approximately 91,500 Indiegogo projects. The dataset has been updated monthly since May 2016. ​As of the latest update, the Kickstarter dataset includes information on all current and historic projects, starting in March 2016. Each dataset entry includes the following key variables:

  • Project ID: A unique identifier for each project.​

  • Title: The name of the project.​

  • Category: The category under which the project is listed (e.g., Technology, Art).​

  • Creator: The name of the project creator.​

  • Goal: The funding goal set by the creator.​

  • Pledged Amount: The total amount pledged by backers.​

  • Backers: The number of backers supporting the project.​

  • Launch Date: The date when the project was launched.​

  • Deadline: The funding deadline for the project.​

  • Status: The current status of the project (e.g., active, successful, failed).

Twitter datasets

Twitter datasets

david-stemper
Open Dataset
Creators: GitHub
Publication Date: 2024-10-23
Creators: GitHub

This dataset collects data related to Twitter. It includes various publicly available Twitter datasets for research and analysis, covering a wide range of topics such as user behavior, tweets, hashtags, network analysis, and sentiment analysis. Researchers can utilize these datasets for tasks like sentiment analysis, trend detection, network analysis, and studying information diffusion on social platforms. The datasets are typically structured to include variables such as:​

  • tweet_ID: Unique identifier for each tweet.​

  • text: Content of the tweet.​

  • date: Timestamp indicating when the tweet was posted.​

  • user_ID: Identifier for the user who posted the tweet.​

  • propositions: Extracted linguistic propositions or predicate phrases within the tweet.​

  • coreference Information: Identifies predicates that refer to the same entities in context.​

  • source_URL: URL link to the original tweet or associated content, if available.​

Meta content library

Meta content library

david-stemper
Open Dataset
Creators: Meta
Publication Date: 2024-10-23
Creators: Meta

​The Meta Content Library and its associated API, provided by Meta, offer comprehensive access to public content from Facebook, Instagram, and Threads. These tools are designed to facilitate in-depth research and analysis of social media content by providing structured data on public posts and associated metrics. The library provides near real-time access to public content across Meta’s platforms, including posts from Pages, groups, events, business and creator accounts, and widely-known individuals and organizations. Each post includes information about the creator (e.g., user or account type), the number of reactions (likes, loves, haha, wow, sad, angry), shares, and views. This detailed metadata enables nuanced analysis of user engagement and content dissemination patterns.

The dataset is structured to include the following key variables:​

  • Post Creator: Information about the user or account that created the post, including the account type (e.g., individual, public figure, business).​

  • Reactions: Total number of reactions the post has received, categorized by type (likes, loves, haha, wow, sad, angry).​

  • Shares: Number of times the post has been shared by other users.​

  • Views: Total number of views the post has received.

Clubhouse data

Clubhouse data

david-stemper
Open Dataset
Creators: Kaggle
Publication Date: 2024-10-23
Creators: Kaggle

The Clubhouse dataset on Kaggle provides data related to the social audio app Clubhouse. It contains information such as user demographics, room activity, user engagement metrics, and discussions held on the platform. The dataset is approximately 9.7 MB in size and comprises 1,300,515 user profiles. Each profile represents an individual observation, offering a substantial sample for analysis. It is particularly valuable for analyzing user demographics, social networking patterns, and engagement metrics within the platform. Structurally, the dataset is organized as a CSV file, with each row corresponding to a user’s profile and columns representing various attributes. The key variables included are:​

  • user_ID: A unique identifier for each user on Clubhouse.​

  • name: The display name of the user.​

  • photo_url: URL of the user’s profile photo.​

  • username: The username chosen by the user on Clubhouse.​

  • Twitter: The user’s Twitter handle or linked Twitter account.​

  • Instagram: The user’s Instagram handle or linked Instagram account.​

  • num_followers: The number of followers the user has on Clubhouse.​

  • num_following: The number of accounts the user is following on Clubhouse.​

  • time_created: The date and time when the user’s account was created.​

  • invited_by_user_profile: Profile information of the user who invited this user to Clubhouse.

​

Reddit datasets

Reddit datasets

david-stemper
Open Dataset
Creators: Conversational Analysis Toolkit (ConvoKit)
Publication Date: n.a
Creators: Conversational Analysis Toolkit (ConvoKit)
The ConvoKit Subreddit Corpus is a collection of user comments from various subreddits on Reddit, gathered over time to facilitate research in conversational analysis and sociolinguistics. It encompasses posts and comments from 948,169 individual subreddits, each from its inception until October 2018. This dataset is organized into individual corpora for each subreddit, facilitating targeted analysis of specific communities. Each corpus includes detailed information at multiple levels: speaker-level, where speakers are identified by their Reddit usernames; utterance-level, where each post or comment is treated as an utterance with attributes such as unique ID, author, conversation ID, reply relationships, timestamp, and text content; conversation-level, where each post and its corresponding comments are considered a conversation, with metadata including the post’s title, number of comments, domain, subreddit, and author flair; and corpus-level, which aggregates data such as the list of subreddits included, total number of posts and comments, and the number of unique speakers.
Instagram Posts from Football Players

Instagram Posts from Football Players

Stefan
Open Dataset
Creators: Klostermann, Jan
Publication Date: 2023
Creators: Klostermann, Jan

This dataset includes information on 334,071 Instagram posts from 1,435 male professional football players that were under contract at any of the 56 clubs in the English Premier League, the Spanish La Liga, and the German Bundesliga. The data was colleced December 31th, 2019 and includes the whole history of Instagram posts up to that point in time.

The information provided in the dataset are the following:

  • Player information: Information on each of the football player in the dataset is collected from http://www.transfermarkt.de and includes club, position, market value (at the time of collecting the data), highest market value, and the year in which highest market value was observed. Further, the Instagram account name is provided.
  • Instagram post information: Information on the Instagram posts including the shortcode (which can be used to open the post on instagram.com), date, caption text, number of likes, number of comments, post type (image, sidecar, video).
  • Instagram post images: For each post, we analyzed the content of the image (first image for sidecar posts, first frame for video posts) using Google Vision and extract the number of persons, their age, and their gender. Further, we extract all tags that are included in the image, such as “soccer” or “car”.
  • Additional information: Additional information such as the images of the posts can be requested from the authors.

The dataset has been used in the following paper:

Klostermann, J., Meißner, M., Max, A., & Decker, R. (2023). Presentation of celebrities’ private life through visual social media. Journal of Business Research, 156, 113524.

Please cite the paper when using the dataset for your own research. It is recommended to read the paper for further information on the dataset.

Fact-Checking Facebook Politics Pages

Fact-Checking Facebook Politics Pages

Stefan
Open Dataset
Creators: Silverman Craig; Strapagiel, Lauren; Shaban, Hamza; Hall, Ellie; Singer-Vine, Jeremy
Publication Date: 2016
Creators: Silverman Craig; Strapagiel, Lauren; Shaban, Hamza; Hall, Ellie; Singer-Vine, Jeremy

This repository contains the data and analysis for the BuzzFeed News article, “Hyperpartisan Facebook Pages Are Publishing False And Misleading Information At An Alarming Rate,” published October 20, 2016. The dataset specifically examines content from hyperpartisan Facebook pages, providing insights into the spread of false and misleading information within polarized political communities. It includes Facebook engagement figures—such as shares, reactions, and comments—offering a perspective on how users interact with content of varying accuracy. The dataset has a total size of 364,8 kB and contains over 1,000 posts from hyperpartisan political Facebook pages. Data was collected up to October 11, 2016, capturing a snapshot of political content leading up to the 2016 U.S. presidential election. Structurally, the dataset is organized into a spreadsheet with columns representing:​

  • Post Content: Text of the Facebook post.​

  • Fact-Check Rating: Assessment of the post’s accuracy.​

  • Engagement Metrics: Counts of shares, reactions, and comments.

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