Utilizing curated #BlackLivesMatter (BLM) Twitter data and motivated by previous work that identified and examined interactions between sub-communities in the BLM twittersphere, the Twitter Time Machine integrates network analysis, visualization, multi-channel Twitter stream presentation and content study to create a dashboard to help researchers identify and understand the influence of marginalized counterpublics during event-triggered networked public discourse and debate.
TTM runs on the Rensselaer IDEA Campfire, a multi-user, collaborative, immersive computing interface. The Campfire is a desk-height, 10-foot panoramic screen (the Wall) and floor projection (Floor) that users gather around and look into, maintaining contact with one another with no artificial or virtual barriers between themselves as they observe and engage with presentations and applications. Two large monitors adjacent to the Campfire complement the integrated Wall and Floor visualizations with appropriate content, enabling investigators to be fully immersed in their exploratory tasks.
Twitter Time Machine utilizes a corpus of over 250GB of Twitter data based on curated lists of Tweet IDs identified by the BTH research for the years 2014-2015 and from Internet Archive curation (2016-2017). In-depth exploration is provided for nine time periods (2014-2015) identified by the BTH research and additional periods in 2016 based on events the BLM movement was known to have responded to. The "re-hydrated" Twitter data was stored in a private Elasticsearch document database instance hosted on the IDEA compute cluster; queries into the Elasticsearch-hosted corpus originated directly from the R environment using the elasticsearchr R package
- Data INCITE Participants: Alex Mankowski, Sarah McRae, Brian Maher
- The Rensselaer IDEA Staff: John S. Erickson, Kristin P. Bennett
- IDEA Visiting Scholar and team mentor: Brooke Foucault Welles, Northeastern University