Georgia Tech Shapes Research in Computer-Supported Cooperative Work as ACM Conference Turns 20

Georgia Tech computing faculty, students and alumni will play a central part in the Association for Computing Machinery’s Conference on Computer-Supported Cooperative Work and Social Computing in Portland, Ore., where the main program runs Feb. 27 – March 1.

Six faculty from the School of Interactive Computing have a combined eight papers accepted at CSCW 2017, including two of six best papers at the conference. These Atlanta-based researchers’ work covers a range of challenge areas, including privacy for social media, fake news, online movements, health tracking and digital self-harm.

Georgia Tech alumni are also making considerable contributions to the field, with 17 papers, including 3 honorable mention papers, by 13 authors.

CSCW convenes its 20th conference this year – it took place biannually from 1986-2010 and annually since 2010 – having become the premier venue for research in the design and use of technologies that affect groups, organizations, communities, and networks. The conference explores the technical, social, material, and theoretical challenges of designing technology to support collaborative work and life activities.


Research Highlights

Likelihood of Dieting Success Lies Within Your Tweets

There is a direct link between a person’s attitude on social media and the likelihood that their dieting efforts will succeed.

In fact, Georgia Institute of Technology researchers have determined that dieting success ­– or failure – can be predicted with an accuracy rate of 77 percent based on the sentiment of the words and phrases one uses on Twitter.

“We see that those who are more successful at sticking to their daily dieting goals express more positive sentiments and have a greater sense of achievement in their social interactions,” said Assistant Professor Munmun De Choudhury, who is lead researcher on the project. “They are focused on the future, generally more social and have larger social networks.”

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Finding Credibility Clues on Twitter

By scanning 66 million tweets linked to nearly 1,400 real-world events, Georgia Institute of Technology researchers have built a language model that identifies words and phrases that lead to strong or weak perceived levels of credibility on Twitter.  Their findings suggest that the words of millions of people on social media have considerable information about an event’s credibility – even when an event is still ongoing.

“There have been many studies about social media credibility in recent years, but very little is known about what types of words or phrases create credibility perceptions during rapidly unfolding events,” said Tanushree Mitra, the Georgia Tech Ph.D. candidate who led the research.

The team looked at tweets surrounding events in 2014 and 2015, including the emergence of Ebola in West Africa, the Charlie Hebdo attack in Paris and the death of Eric Garner in New York City. They asked people to judge the posts on their credibility (from “certainly accurate” to “certainly inaccurate”). Then the team fed the words into a model that split them into 15 different linguistic categories. The classifications included positive and negative emotions, hedges and boosters, and anxiety.

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Most of Facebook is ‘Friends Only,’ But Public and Private Posts are Likely Similar

Social media content, while driving a sizable portion of today’s web traffic, is not all public, and according to a new study, about 75 percent of Facebook posts, or three in four, are shared only with friends or subsets of friends. This translates into billions of daily online conversations that are seen by only a few.

Researchers from the Georgia Institute of Technology enlisted almost 2,000 Facebook users – who shared their most recent posts – and used machine learning methods as well as qualitative hand coding to determine content types and topics for roughly 11,000 public and private posts. They analyzed patterns of choices for privacy settings and found, contrary to expectations, that content type is not a significant predictor of privacy settings. They did find however that some demographics such as gender and age are predictive, suggesting that privacy choices may be driven more by the attributes of the person rather than by the content of the posts.

A full look at Georgia Tech's work at CSCW 2017 can be found at


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  • CSCW 2017 faculty authors