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Emiliano De Cristofaro
Researcher at University College London
Publications - 262
Citations - 9897
Emiliano De Cristofaro is an academic researcher from University College London. The author has contributed to research in topics: Social media & Computer science. The author has an hindex of 47, co-authored 251 publications receiving 7263 citations. Previous affiliations of Emiliano De Cristofaro include Boston University & Nokia.
Papers
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Journal ArticleDOI
From risk factors to detection and intervention: a practical proposal for future work on cyberbullying
Andri Ioannou,Jeremy Blackburn,Gianluca Stringhini,Emiliano De Cristofaro,Nicolas Kourtellis,Michael Sirivianos +5 more
TL;DR: It is argued that a multidisciplinary approach is needed, with expertise from human–computer interaction, psychology, computer science, and sociology, for current challenges to be addressed and significant progress to be made on cyberbullying.
Journal ArticleDOI
Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes
Chen Ling,Ihab AbuHilal,Jeremy Blackburn,Emiliano De Cristofaro,Savvas Zannettou,Gianluca Stringhini +5 more
TL;DR: In this article, the authors investigate what visual elements distinguish image memes that are highly viral on social media from those that do not get re-shared, across three dimensions: composition, subjects, and target audience.
Posted Content
Fast and Private Computation of Set Intersection Cardinality.
Proceedings Article
The Evolution of the Manosphere Across the Web
Manoel Horta Ribeiro,Jeremy Blackburn,Barry Bradlyn,Emiliano De Cristofaro,Gianluca Stringhini,Summer Long,Stephanie Greenberg,Savvas Zannettou +7 more
TL;DR: In this paper, a large-scale characterization of the Manosphere, a conglomerate of Web-based misogynist movements roughly focused on "men's issues", is presented, by gathering and analyzing 28.8M posts from 6 forums and 51 subreddits, showing the links between its different communities over the years.
Proceedings ArticleDOI
Detecting Aggressors and Bullies on Twitter
Despoina Chatzakou,Nicolas Kourtellis,Jeremy Blackburn,Emiliano De Cristofaro,Gianluca Stringhini,Athena Vakali +5 more
TL;DR: This work analyzes user behavior on Twitter in an effort to detect cyberbullies and cuber-aggressors by considering specific attributes of their online activity using machine learning classifiers.