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pro vyhledávání: '"Krishnan, Siddharth A."'
Autor:
Kumar, Avisha, Kotkar, Kunal, Jiang, Kelly, Bhimreddy, Meghana, Davidar, Daniel, Weber-Levine, Carly, Krishnan, Siddharth, Kerensky, Max J., Liang, Ruixing, Leadingham, Kelley Kempski, Routkevitch, Denis, Hersh, Andrew M., Ashayeri, Kimberly, Tyler, Betty, Suk, Ian, Son, Jennifer, Theodore, Nicholas, Thakor, Nitish, Manbachi, Amir
While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning
Externí odkaz:
http://arxiv.org/abs/2409.16441
Autor:
Rashid, Rezaur, Melton, Joshua, Ghorbani, Ouldouz, Krishnan, Siddharth, Reid, Shannon, Terejanu, Gabriel
In today's digital age, social media platforms play a crucial role in shaping public opinion. This study explores how discussions led by influencers on Twitter, now known as 'X', affect public sentiment and contribute to online polarization. We devel
Externí odkaz:
http://arxiv.org/abs/2405.15893
The high volume and rapid evolution of content on social media present major challenges for studying the stance of social media users. In this work, we develop a two stage stance labeling method that utilizes the user-hashtag bipartite graph and the
Externí odkaz:
http://arxiv.org/abs/2404.10228
Autor:
Kapoor, Maya, Melton, Joshua, Ridenhour, Michael, Sriram, Mahalavanya, Moyer, Thomas, Krishnan, Siddharth
Complex heterogeneous dynamic networks like knowledge graphs are powerful constructs that can be used in modeling data provenance from computer systems. From a security perspective, these attributed graphs enable causality analysis and tracing for an
Externí odkaz:
http://arxiv.org/abs/2203.02744
Graph Convolutional Neural Networks (GCNs) have become effective machine learning algorithms for many downstream network mining tasks such as node classification, link prediction, and community detection. However, most GCN methods have been developed
Externí odkaz:
http://arxiv.org/abs/2203.01903
Computational methods to model political bias in social media involve several challenges due to heterogeneity, high-dimensional, multiple modalities, and the scale of the data. Political bias in social media has been studied in multiple viewpoints li
Externí odkaz:
http://arxiv.org/abs/2109.09014
Autor:
Chew, Christine, Brand, Oliver J, Yamamura, Tomohiko, Lawless, Craig, Morais, Mychel Raony Paiva Teixeira, Zeef, Leo, Lin, I-Hsuan, Howell, Gareth, Lui, Sylvia, Lausecker, Franziska, Jagger, Christopher, Shaw, Tovah N, Krishnan, Siddharth, McClure, Flora A, Bridgeman, Hayley, Wemyss, Kelly, Konkel, Joanne E, Hussell, Tracy, Lennon, Rachel
Publikováno v:
In Matrix Biology March 2024 127:23-37
Online hate speech on social media has become a fast-growing problem in recent times. Nefarious groups have developed large content delivery networks across several main-stream (Twitter and Facebook) and fringe (Gab, 4chan, 8chan, etc.) outlets to de
Externí odkaz:
http://arxiv.org/abs/2011.01861
The ubiquity of social media has transformed online interactions among individuals. Despite positive effects, it has also allowed anti-social elements to unite in alternative social media environments (eg. Gab.com) like never before. Detecting such h
Externí odkaz:
http://arxiv.org/abs/2007.12724
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