Candidata: U.S. 2024 Elections Candidates and Social Media Posts (ICPSR 300490)
These data are restricted and require an application. To apply, see SOMAR’s Application Portal and Application Guide.
Candidata is a dataset of social media handles and posts for candidates for primary and general federal elections in the United States in 2024 across multiple social media platforms. The dataset spans Facebook, Instagram, Threads, TikTok, X (Twitter), YouTube, Gettr, Rumble, Telegram, and Truth Social. For interoperability, we provide FEC IDs and ICPSR IDs where available.
The Shapes of the Fourth Estate During the Pandemic: Profiling COVID-19 News Consumption in Eight Countries (ICPSR 300528)
COVID2020 dataset provides a new, high-volume COVID-19 tweet dataset. It was collected from March 2020 to November 2020, covering eight months in the first year of the pandemic. The list of tracked COVID-19 keywords is obtained from "Emily Chen, Kristina Lerman, and Emilio Ferrara. 2020. Tracking Social Media Discourse about the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Health and Surveillance (2020)". Those keywords include not only generic terms such as "corona virus", "covid", but also non-pharmaceutical interventions such as "lockdown", "n95", and "social distancing."
This dataset is comprised of tweet IDs.
Beyond the Hashtags: #Ferguson, #Blacklivesmatter, and the Online Struggle for Offline Justice (ICPSR 300497)
These data are restricted and require an application. To apply, see SOMAR’s Application Portal and Application Guide.
This is a dataset of tweets purchased from Twitter as part of the Beyond the Hashtags study. The dataset includes a year of tweets that mention one or more of 45 keywords associated with the BlackLivesMatter movement. This period covers a critical time in which social media was used to raise awareness about police killings of unarmed Black citizens in the United States.
Replication data for "Emergent structures of attention on social media are driven by amplification and triad transitivity" (ICPSR 300481)
These data are restricted and require an application. To apply, see SOMAR’s Application Portal and Application Guide.
As they evolve, social networks tend to form transitive triads more often than random chance and structural constraints would suggest. However, the mechanisms by which triads in these networks become transitive are largely unexplored. We leverage a unique combination of data and methods to demonstrate a causal link between amplification and triad transitivity in a directed social network. Additionally, we develop the concept of the "attention broker," an extension of the previously theorized tertius iungens (or "third who joins").
We use a novel technique to identify time-bounded Twitter/X following events, and then use difference-in-differences to show that attention brokers cause triad transitivity by amplifying content. Attention brokers intervene in the evolution of any sociotechnical system where individuals can amplify content while referencing its originator.
The full dataset consists of the time-bounded follower counts, and their associated timings, for each retweeted account and attention broker followers and non-followers. All retweeted accounts' usernames and followers' user IDs are hashed using a non-reversible hash function for privacy.