July 12, 2018
Illuminating team members Patricia Rossini, Jennifer Stromer-Galley, Kate Kenski, Jeff Hemsley, Feifei Zhang, and Brian Dobreski were recently published in the Journal of Information Technology and Politics. The article is titled "The relationship between race competitiveness, standing in the polls, and social media communication strategies during the 2014 U.S. gubernatorial campaigns".
The article abstract states:
"Political campaigns have been systematically using social media for strategic advantage. However, little is known about how competitiveness affects the ways candidates communicate online. Our study analyzes how race competitiveness as measured by polling performance influences candidates’ strategies on Twitter and Facebook. We analyze all social media messages of Republican and Democratic candidates in states that held gubernatorial elections in 2014 using supervised automated content analysis. We find that position in the polls and that race competitiveness are correlated with the ways candidates communicate on social media, and that candidates use Twitter and Facebook in different ways to communicate with the public."
Citation: Rossini, P., Stromer-Galley, J., Kenski, K., Hemsley, J., Zhang, F., & Dobreski, B. (2018). The relationship between race competitiveness, standing in the polls, and social media communication strategies during the 2014 U ...
April 19, 2018
CCDS is excited to welcome Brian McKernan as a new Research Assistant Professor within the iSchool beginning in June!
Prior to joining the School of Information Studies at Syracuse University, Brian was an assistant professor of sociology at The Sage Colleges. Brian has also taught courses at New York University, Mount Holyoke College, and SUNY Albany.
"From 2013 to 2015, I worked on the CYCLES project with Professor Stromer-Galley from the School of Information Studies as well as scholars and designers from a variety of different institutions. CYCLES was a federally funded research program to design an educational video game that can teach players about cognitive biases and reduce the likelihood that they will commit these biases in the future. I found the project to be immensely valuable and my work on the project to be very fulfilling. I have been eager to participate in similar projects ever since.
My TRACE work:
I am looking forward to meaningfully contributing to every facet of the TRACE project. Much of my work so far has focused on helping to design the TRACE application, particularly how we can incorporate insights from literature in communication and information studies as well as research ...
April 5, 2018
The Center for Computational and Data Science brings you a second Seed Funding Highlight!
The Center offers funding for proposals that align with our mission: working to advance theoretical or applied research in the social sciences using advanced computational approaches, including human language technologies and data science. The goal of seed funding is to support pilot research that will lead to future grant proposals or research publications, as well as to support dissertation research that advances CCDS goals.
We are excited to showcase the research of iSchool Assistant Professor Jeff Hemsley! Jeff received a PhD from the University of Washington’s Information School in 2014. Prior to that Jeff graduated with a BS in Economics with a minor in Math and Statistics. Jeff’s previous work experience includes working in the software industry for nearly 18 years. He held positions as a software engineer, a manager, a project manager, and as a test engineer.
Jeff Hemsley’s research interests center on understanding information diffusion – or viral events, particularly in the context of politics in social media. Jeff writes that “What has inspired me about studying viral events is that they can bring alternate perspectives and the grievances of marginalized groups ...
April 4, 2018
CCDS Seed Funding Highlights
The Center for Computational and Data Science offers seed funding for proposals that align with the mission of the Center: working to advance theoretical or applied research in the social sciences using advanced computational approaches, including human language technologies and data science. The goal of seed funding is to support pilot research that will lead to future grant proposals or research publications, as well as to support dissertation research that advances CCDS goals.
We are excited to showcase the research of iSchool faculty and PhD students, and how CCDS seed funding has impacted their work. Our first seed funding follow up features Mook Sikana Tanupabrungsun, a 4th year PhD student at the iSchool, who received seed funding from CCDS to support her PhD work. Mook received a Bachelor’s and Master’s degree in Computer Engineering from King Mongkut’s University of Technology Thonburi in Thailand. She recently defended her dissertation and will graduate in the Spring of 2018, and will be working for Microsoft, Redmond following graduation.
Mook’s dissertation, titled “Microcelebrity Practices: A Cross-Platform Study Through a Richness Framework”, examined the uses of social media by celebrities for growing and maintaining ...
April 2, 2018
On Tuesday, March 27th, CCDS’ Jenny Stromer-Galley and Alexandra Sargent attended the 2018 Summit of the Northeast Big Data Innovation Hub (“the Hub”) in NYC at the Columbia Journalism School. The Hub hosts an annual summit to convene the data science stakeholder community of the Northeast United States, offer updates on the Hub’s continued work, host panel discussions and multi-sector input, and provide opportunities for discussion and collaboration with stakeholders during breakout sessions.
This years’ keynote speaker was Corinna Cortes, the head of Google Research in NYC, who spoke about her team’s data-driven approach to fighting fake news. Cortes opened by stating that “Google is a search tool, not the ledger of absolute truth”- a statement which prompted discussion about advertising and interests among many of the attendees. Cortes went on to explain how Google uses relevance and authoritativeness to produce search results, and explained how these can be used to produce fake news as a primary search result. Cortes’ examples of how these two search result factors may produce fake news were from the 2016 Presidential election, during which a top search result gave incorrect election numbers, and recent Cambridge Analytica claims and misinformation. Cortes explained how ...
March 30, 2018
The Hawai’i International Conference on Systems Sciences (HICSS-52) has posted a call for papers!
The purpose of HICSS is to provide a forum for the interchange of ideas, research results, development activities, and applications among academicians and practitioners in computer-based systems sciences. The conference consists of tutorials, advanced seminars, presentations of accepted papers, open forum, tasks forces, and plenary and distinguished guest lectures. There is a high degree of interaction and discussion among the conference participants because the conference is conducted in a workshop-like setting.
Papers are invited for the mini-track on “Crowd-Enhanced Technologies for Improving Reasoning and Solving Complex Problems” as part of the Collaboration Systems and Technology Track at the Hawaii International Conference on System Sciences (HICSS).
CCDS will be there to discuss the TRACE Project. For others that are interested, the deadline for submissions is June 15th, 2019 and the conference will be held January 8-11, 2019.
For more information, please visit https://alt.colostate.edu/hicss52-minitrack/ or contact James Folkestad at email@example.com.
Feb. 12, 2018
By Hana Maeda
In the information age of rapid news sharing, iSchool professor Bei Yu has started a project about misinformation to better understand scientific research. Yu, whose research area is in applied Natural Language Processing (NLP), is taking an NLP approach to analyze exaggerated claims in science communications. Specifically, Yu and her team is building a computational model on the language of certainty, gathering and comparing science claims from prior research. Their findings will help the public understand how people describe similar topics in different ways. For instance, how news stories and social media communication might be different from what scientists are saying. “I think [our findings] can be training materials for science education, so the public can get more familiar with scientists’ language as a primary source,” Yu says.
Ultimately, her goal is to monitor the quality of science communications in society with this automated tool. Yu feels that original findings from scientists aren’t directly presented to the public, whether the findings are hard to understand or difficult to locate. Now that anyone can report science claims online, she finds that there’s more public information. For Yu, the challenge is to develop better methods of accessing ...
Nov. 7, 2017
By Hana Maeda
Working in teams can bring many unexpected challenges, but one professor is looking into making that process a little easier. As part of an ongoing research project, School of Information Studies (iSchool) Associate Professor Jeffrey Saltz is continuing experiments on how teams can best work together on data science projects. Saltz is testing these experiments in his data science course, Applied Data Science, where he has teams work on different process methodologies each semester. This semester, Saltz is working on applying the Kanban methodology, a type of agile technique, to analyze how well teams work in a data science environment. And Saltz’s initial experiments suggests that Kanban “is one of the better project methodologies for data science teams.”
Saltz was inspired to take on this project from his own experiences working in data science teams for 20 years. He said that teams “would always struggle with getting all the different stakeholders on the same page and communicating and coordinating a large, diverse group of people.” For Saltz, coming to Syracuse University was the opportunity to address this challenge.
This project, Saltz said, is important because up until recently, most data scientists have worked individually. Being a ...
Dec. 17, 2016
The Center for Computational and Data Sciences in the ISchool hosted the following talk:
Title: Deep Learning for NLP
Speaker: Dr. Nancy McCracken
Date: Wednesday, December 14th
Time: 3:00 PM - 4:00 PM
Location: Katzer Room (Hinds Hall)
Recorded presentation: Click here
Presentation slides: Click here
Description: In this talk, I introduce Deep Learning as a technique in machine learning that automatically learns classification features. Deep learning tasks will be surveyed, including those for NLP. The main focus of the talk will be on the NLP task of learning dense word similarity vectors, such as those popularized by the word2vec software. We will show examples with both word2vec (the gensim implementation) and Deep Tensor software for this task in python.
Bio: Dr. McCracken is a Research Associate Professor in the iSchool. Her general research interests are in applying the principles and tools of computational linguistics to making information accessible and understandable for people. Recent research projects include an NSF funded project for using natural language processing and machine learning to assist social scientists in content analysis of text, an NSF funded project for building a qualitative data repository and working with the campaign research project group in using social ...
Sept. 17, 2016
By Jerry Robinson
As one of the iSchool's newest data science hires, Daniel Acuna has an affiliation with the Center for Computational and Data Science (CCDS). Acuna is an Assistant Professor at the Syracuse University School of Information Studies. He earned his Ph.D. in Computer Science from the University of Minnesota, Twin Cities, and completed a postdoc at the Rehabilitation Institute of Chicago and Northwestern University before joining the iSchool this fall.
Acuna's work crosses multiple disciplines including cognitive science, computer science, machine learning, and neuroscience. Currently, he most identifies with the relatively new but growing field of Science of Science.
Acuna research and tools help scientists and funding agencies to make high-quality decisions around hiring, promotion, literature search, and expertise matching. He believes his work is possible because of data science: "Data science is a fundamental area that allows me to enter a number of fields and use data to answer critical questions in those fields."
Acuna regularly relies on his background knowledge to design useful computational tools that help scientists and funders make sense of data and answer important questions in their specific domain. For instance, cognitive science helps him to understand how uncertainty factors ...