C
Ceren Budak
Researcher at University of Michigan
Publications - 70
Citations - 2337
Ceren Budak is an academic researcher from University of Michigan. The author has contributed to research in topics: Social media & Computer science. The author has an hindex of 17, co-authored 62 publications receiving 1810 citations. Previous affiliations of Ceren Budak include Microsoft & University of California, Santa Barbara.
Papers
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Proceedings ArticleDOI
Limiting the spread of misinformation in social networks
TL;DR: This work study the notion of competing campaigns in a social network and address the problem of influence limitation where a "bad" campaign starts propagating from a certain node in the network and use the concept of limiting campaigns to counteract the effect of misinformation.
Journal ArticleDOI
Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis
TL;DR: This article investigated the selection and framing of political issues in fifteen major US news outlets and found that news organizations are considerably more similar than generally believed, with news organizations presenting topics in a largely nonpartisan manner, casting neither Democrats nor Republicans in a particularly favorable or unfavorable light.
Posted Content
A first look at COVID-19 information and misinformation sharing on Twitter
Lisa Singh,Shweta Bansal,Leticia Bode,Ceren Budak,Guangqing Chi,Kornraphop Kawintiranon,Colton Padden,Rebecca Vanarsdall,Emily K. Vraga,Yanchen Wang +9 more
TL;DR: It is suggested that a meaningful spatio-temporal relationship exists between information flow and new cases of COVID-19, and while discussions about myths and links to poor quality information exist, their presence is less dominant than other crisis specific themes.
Journal ArticleDOI
Solving path problems on the GPU
TL;DR: This work implemented a recursively partitioned all-pairs shortest-paths algorithm that harnesses the power of GPUs better than existing implementations and provides evidence that programmers should rethink algorithms instead of directly porting them to GPU.
Journal ArticleDOI
Structural trend analysis for online social networks
TL;DR: This work proposes two novel structural trend definitions the authors call coordinated and uncoordinated trends that use friendship information to identify topics that are discussed among clustered and distributed users respectively and provides new insights into the way people share information online.