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Eric Gilbert
Researcher at University of Michigan
Publications - 82
Citations - 10006
Eric Gilbert is an academic researcher from University of Michigan. The author has contributed to research in topics: Social media & Social network. The author has an hindex of 37, co-authored 82 publications receiving 7631 citations. Previous affiliations of Eric Gilbert include Georgia Institute of Technology & University of Valladolid.
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Proceedings Article
VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text
Clayton J. Hutto,Eric Gilbert +1 more
TL;DR: Interestingly, using the authors' parsimonious rule-based model to assess the sentiment of tweets, it is found that VADER outperforms individual human raters, and generalizes more favorably across contexts than any of their benchmarks.
Proceedings ArticleDOI
Predicting tie strength with social media
Eric Gilbert,Karrie Karahalios +1 more
TL;DR: A predictive model that maps social media data to tie strength is presented, which performs quite well and is illustrated by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization.
Proceedings ArticleDOI
Faces engage us: photos with faces attract more likes and comments on Instagram
TL;DR: The first results on how photos with human faces relate to engagement on large scale image sharing communities are presented, finding that the number of faces, their age and gender do not have an effect.
Proceedings Article
Widespread Worry and the Stock Market
TL;DR: It is demonstrated that estimating emotions from weblogs provides novel information about future stock market prices, and how the mood of millions in a large online community, even one that primarily discusses daily life, can anticipate changes in a seemingly unrelated system.
Proceedings ArticleDOI
The language that gets people to give: phrases that predict success on kickstarter
Tanushree Mitra,Eric Gilbert +1 more
TL;DR: The factors which lead to successfully funding a crowdfunding project are explored, and the language used in the project has surprising predictive power accounting for 58.56% of the variance around successful funding.