Illuminating is a computational journalism project that is meant to empower journalists covering presidential and midterm campaigns. Political reporters must cover not only stump speeches, campaign events, and TV ads, but also what is happening on social media. Covering it increases transparency and accountability of the campaigns, and is a way to take the pulse of the electorate. The sheer volume of information that flows through social media, however, makes it challenging to report accurately and comprehensively. Our goal is to help journalists in that important work by providing a usable yet comprehensive summary of the content of messages that goes beyond counting likes or retweets. Illuminating will enable political journalists an insightful yet accessible summation of the important political conversation online. Illuminating has covered the 2016 and 2018 elections, and is currently covering the 2020 elections.
This project is supported by the Center for Computational and Data Science and the BITS Lab at the School of Information Studies at Syracuse University.
This project, led by Center Co-Director Jeff Hemsley, studies the shift in information diffusion from large social media sites to niche social media sites.
This project, led by Center affiliate Yang Wang, aims to provide people with disabilities, particularly those with visual impairments, better privacy tools when working with computers.
CCDS to host SU Research Un-Conference in September around key themes: Democracy, Digital Media, Decision Making, and Data Analytics
The TRACE (Trackable Reasoning and Analysis for Collaboration and Evaluation) Project aims at improve reasoning and intelligence analysis through the development of a web-based application that will leverage the use of structured techniques, crowdsourcing and smart nudging to enhance analysts' problem-solving abilities and foster creative thinking.
On April 13th, CCDS had the pleasure of hosting Roc Myers to give a TensorFlow talk with interested graduate students and faculty.
The CORA (Citation Opinion Retrieval and Analysis) project aims to build an automated tool that can plug into a full-text bibliographic database, extract citation statements toward a cited article, separate substantial citations from perfunctory ones, and categorize substantial citation opinions by their purposes, subject aspects, tones, and holders and targets of the opinions.