Social scientists are increasingly drawing on web data to analyze social behavior, opinion formation, cultural preferences, or political polarization. Collecting social media data and other digital behavioral data (DBD) up to the standards of social science research is a non-trivial task and often a challenge to individual researchers. GESIS develops innovative methods for the collection of digital behavioral data in the social sciences. In accordance with the proprietary and privacy restrictions that apply, we provide the resulting data for scientific re-use. GESIS offers a range of collected, curated, and augmented datasets; these data are transparent, ready-to-use and often accompanied by additional materials or tools. We concentrate on topical data relevant for the social sciences, training data – e.g., for attribute or opinion detection – or large datasets that can be further mined for individual research purposes.
With the "Total Error Sheets for Datasets" (TES-D) we propose a template for documenting datasets that have been collected from online platforms for research purposes; the Total Error Sheets for Datasets are based on our "Total Error Framework for Digital Traces of Human Behavior on Online Platforms" (TED-On).
Source: Twitter, Facebook
These datasets present results from the social media monitoring of Facebook and Twitter for the German federal election campaigns 2013, 2017, and 2021. The project collects the tweets and Facebook posts of political candidates and organizations and the engagement of users with these contents.
Semantically annotated corpus of tweets related to the COVID-19 pandemic capturing online discourse about various aspects of the pandemic and its societal impact from October 2019 onwards.
The dataset contains precomputed entity and sentiment annotations and extracted tweet metadata. The data are publicly available.
Topical Collection, Training Data
Source: Twitter, Crowdsourced
The 'Call me sexist but' dataset (CMSB) is part of our work to analyze different dimensions of sexism in social media, including overt hostile sexism, 'benevolent' sexism, or more subtle forms that pose a particular challenge for automatic detection techniques. With this we aim at improving methods for, e.g., addressing sexism on online platforms.
Platform Data, Baseline Data
The dataset "Just another day on Twitter" presents a complete dump of one day on Twitter (September 20-21, 2022), generated by a globally co-ordinated effort from 80 scholars. Being "just another day" the 24 hours covered fall into a turbulent period with Elon Musk about to acquire the platform.
Topical Data, Natural Language Data
The natural language query dataset (VACOS-NLQ) is a collection of 3540 written queries for e-commerce product search (laptops and jackets). The queries are enriched with information about age, gender, and domain knowledge of the participants.
Topical Data, Platform Data
The dataset consists of all publicly visible posts and the data that comes with each post of the online forum incels.is during one week in Nov 2022. The data allows to investigate questions surrounding involuntary celibates, hate-speech, communication in online forums, the emergence of acts of terrorism, and suicide prevention.
Social Network Data
RFID Sensor Data, Sociodemographic Data
The SocioPatterns infrastructure enables the collection of data on face-to-face interactions in social contexts via wearable sensors. GESIS has used this to obtain contact data at academic conferences and in other social settings. Data access is restricted due to anonymity reasons. However, limited sets of conference contact data and sociodemographic meta data have been made available.
A dataset of raw tweets collected via the Twitter Streaming API in the context of the onset of the war. In total, we gathered 8.7 million original tweets between February 17 and March 3, 2022, produced by 2.3 million individual user accounts.
In addition, the data has been annotated with availability tags, resulting from rehydration. This may provide information on Twitter moderation policies.
TweetsKB is a public corpus of semantically annotated tweets based on a permanent Twitter crawl. The dataset currently contains data for more than 2.0 billion tweets, spanning from February 2013 to now.
Metadata about the tweets, extracted entities, sentiments, hashtags, and user mentions are shared as a public knowledge base.
Source: Wikipedia, DBPedia
The dataset contains information about international politicians from DBpedia, including name, gender, nationality, and for many also their political party affiliation. The dataset is based on the English DBpedia dump from October 2015.
The data was used to create an interactive visualization of politicians' networks.
This dataset contains every instance of all tokens (≈ words) ever written in undeleted, non-redirect English Wikipedia articles until October 2016, in total 13,545,349,787 instances. We also offer "WikiWho" – a service tool for tracking collaborative knowledge production on Wikipedia.
This dataset includes the historical versions of all individual references per article in the English Wikipedia until June 2019. Each reference object also contains information about its original creating editor, editors implementing changes to it, and timestamps of all actions. The dataset contains 55,503,998 references with 164,530,374 actions.