I'm Andrew Beers. I am a 5th-year PhD candidate in the University of Washington's Human-Centered Design and Engineering program (HCDE), aiming to complete my PhD in the spring of 2024. I study political social media influencers, science communication, mis/disinformation, and other "bad things on the internet" using network science, machine learning, and mixed methods.
You can see a list of selected publications below, as well as some portfolio items. You can see a full publication list on my Google Scholar profile. my CV at this link, and some of my coding projects on my Github account.
Last updated September 18, 2023.
Andrew Beers, Joseph S. Schafer, Ian Kennedy, Morgan Wack, Emma S. Spiro, and Kate Starbird. 2023. Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election. In Proceedings of the International AAAI Conference on Web and Social Media, 59–71.
Andrew Beers, Tom Wilson, and Kate Starbird. 2022. The Demographics of an International Influence Operation Affecting Facebook Users in the United States. Journal of Online Trust and Safety 1
Joseph B. Bak-Coleman, Ian Kennedy, Morgan Wack, Andrew Beers, Joseph S. Schafer, Emma S. Spiro, Kate Starbird, and Jevin D. West. 2022. Combining interventions to reduce the spread of viral misinformation. Nature Human Behaviour 6, 10: 1372–1380.
Ian Kennedy, Morgan Wack, Andrew Beers, Joseph S. Schafer, Isabella Garcia-Camargo, Emma S. Spiro, and Kate Starbird. 2022. Repeat Spreaders and Election Delegitimization: A Comprehensive Dataset of Misinformation Tweets from the 2020 U.S. Election. Journal of Quantitative Description: Digital Media 2.
Andrew Beers. 2023. Two Years of Influence: Tracking Changes in Twitter Influencer Diets in United States Politics. International Conference on Computational Social Science (IC2S2)
Andrew Beers, Kennedy, Ian, Morgan Wack, Joseph S. Schafer, Emma S. Spiro, and Kate Starbird. 2022. Repeat Offenders: Frequent and Influential Misinformation Sources During the 2020 United States Election. International Studies Association
Andrew Beers. 2021. Misinterpretation and Ambiguity in Public-Facing Network Visualizations: A Case Study.
Andrew Beers, Sarah Nguyễn, Maya Sioson, Mariam Mayanja, Monica Ionescu, Emma S. Spiro, and Kate Starbird. 2021. The Firestarting Troll, and Designing for Abusability. International AAAI Conference on Web and Social Media, Information Credibility & Alternative Realities in Troubled Democracies Workshop
Andrew Beers, Sarah Nguyễn, Emma S. Spiro, and Kate Starbird. 2021. Rejecting Science with Science: Boundary-Work in Anti-Mask Twitter Reply Threads During COVID-19. AoIR Selected Papers of Internet Research.
Andrew Beers, Melinda McClure Haughey, Ahmer Arif, and Kate Starbird. 2020. Examining the digital toolsets of journalists reporting on disinformation. Computation + Journalism
Ken Chang, Andrew L. Beers, Laura Brink, Jay B. Patel, Praveer Singh, Nishanth T. Arun, Katharina V. Hoebel, Nathan Gaw, Meesam Shah, Etta D. Pisano, Mike Tilkin, Laura P. Coombs, Keith J. Dreyer, Bibb Allen, Sheela Agarwal, and Jayashree Kalpathy-Cramer. 2020. Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density. Journal of the American College of Radiology 17, 12: 1653–1662.
Ken Chang, Andrew L. Beers (first co-author), Harrison X. Bai, James M. Brown, K. Ina Ly, Xuejun Li, Joeky T. Senders, Vasileios K. Kavouridis, Alessandro Boaro, Chang Su, Wenya Linda Bi, Otto Rapalino, Weihua Liao, Qin Shen, Hao Zhou, Bo Xiao, Yinyan Wang, Paul J. Zhang, Marco C. Pinho, Patrick Y. Wen, Tracy T. Batchelor, Jerrold L. Boxerman, Omar Arnaout, Bruce R. Rosen, Elizabeth R. Gerstner, Li Yang, Raymond Y. Huang, and Jayashree Kalpathy-Cramer. 2019. Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement. Neuro-Oncology 21, 11: 1412–1422.
Andrew Beers, Ken Chang, James Brown, Elizabeth Gerstner, Bruce Rosen, and Jayashree Kalpathy-Cramer. 2018. Sequential neural networks for biologically informed glioma segmentation. In Medical Imaging 2018: Image Processing, 1057433.
Andrew Beers, James Brown, Ken Chang, J. Peter Campbell, Susan Ostmo, Michael F. Chiang, and Jayashree Kalpathy-Cramer. 2018. High-resolution medical image synthesis using progressively grown generative adversarial networks. arXiv:1805.03144 [cs].
Generating synthetic book pages using a progressively growing generative adversarial network (PGGAN).
A data explorer for the 3500+ ads bought by the Russian Internet Research Agency (IRA). Contains links to original data, and a dervied JSON dataset from PDF files containing ads. Made with JS and Bootstrap.
An open-source utility for segmenting glioblastoma in MRI. Currently in use in glioblastoma clinical trials at Mass General Hospital.
Down the rabbit hole of DCE-MRI, a 3D+Time MRI imaging modality used to assess tissue permeability in tumors.
134 years of iceberg sightings from shipboard sightings, aerial surveys, satellite data, and newspaper records in one interactive visualization.
Sharing the stories of those on the autism spectrum via shoeboxes and a distribution network with Design for America.
Designing print layouts, soliciting illustrations, and coordinating illustrators for a anti-oppressive publication in Providence. Making 'what does bluestockings think?' a relevant question in Brown University.
You can see a list of selected publications below, as well as some portfolio items. You can see a full publication list on my Google Scholar profile. my CV at this link, and some of my coding projects on my Github account.
Last updated September 18, 2023.
Peer-Reviewed Publications
Andrew Beers, Sarah Nguyễn, Kate Starbird, Jevin D. West, and Emma S. Spiro. 2023. Selective and deceptive citation in the construction of dueling consensuses. Forthcoming in Science Advances.
Andrew Beers, Joseph S. Schafer, Ian Kennedy, Morgan Wack, Emma S. Spiro, and Kate Starbird. 2023. Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election. In Proceedings of the International AAAI Conference on Web and Social Media, 59–71.
Andrew Beers, Tom Wilson, and Kate Starbird. 2022. The Demographics of an International Influence Operation Affecting Facebook Users in the United States. Journal of Online Trust and Safety 1
Joseph B. Bak-Coleman, Ian Kennedy, Morgan Wack, Andrew Beers, Joseph S. Schafer, Emma S. Spiro, Kate Starbird, and Jevin D. West. 2022. Combining interventions to reduce the spread of viral misinformation. Nature Human Behaviour 6, 10: 1372–1380.
Ian Kennedy, Morgan Wack, Andrew Beers, Joseph S. Schafer, Isabella Garcia-Camargo, Emma S. Spiro, and Kate Starbird. 2022. Repeat Spreaders and Election Delegitimization: A Comprehensive Dataset of Misinformation Tweets from the 2020 U.S. Election. Journal of Quantitative Description: Digital Media 2.
Lightly Reviewed Conference Publications
Andrew Beers. 2023. Negative Influencers in Social Networks: From Targeted Harassment to Controversy Cultivation. International Network for Social Network Analysis (Sunbelt).
Andrew Beers. 2023. Two Years of Influence: Tracking Changes in Twitter Influencer Diets in United States Politics. International Conference on Computational Social Science (IC2S2)
Andrew Beers, Kennedy, Ian, Morgan Wack, Joseph S. Schafer, Emma S. Spiro, and Kate Starbird. 2022. Repeat Offenders: Frequent and Influential Misinformation Sources During the 2020 United States Election. International Studies Association
Andrew Beers. 2021. Misinterpretation and Ambiguity in Public-Facing Network Visualizations: A Case Study.
Andrew Beers, Sarah Nguyễn, Maya Sioson, Mariam Mayanja, Monica Ionescu, Emma S. Spiro, and Kate Starbird. 2021. The Firestarting Troll, and Designing for Abusability. International AAAI Conference on Web and Social Media, Information Credibility & Alternative Realities in Troubled Democracies Workshop
Andrew Beers, Sarah Nguyễn, Emma S. Spiro, and Kate Starbird. 2021. Rejecting Science with Science: Boundary-Work in Anti-Mask Twitter Reply Threads During COVID-19. AoIR Selected Papers of Internet Research.
Andrew Beers, Melinda McClure Haughey, Ahmer Arif, and Kate Starbird. 2020. Examining the digital toolsets of journalists reporting on disinformation. Computation + Journalism
Selected Publications from Neuroimaging Career
Andrew Beers, James Brown, Ken Chang, Katharina Hoebel, Jay Patel, K. Ina Ly, Sara M. Tolaney, Priscilla Brastianos, Bruce Rosen, Elizabeth R. Gerstner, and Jayashree Kalpathy-Cramer. 2020. DeepNeuro: an open-source deep learning toolbox for neuroimaging. Neuroinformatics.
Ken Chang, Andrew L. Beers, Laura Brink, Jay B. Patel, Praveer Singh, Nishanth T. Arun, Katharina V. Hoebel, Nathan Gaw, Meesam Shah, Etta D. Pisano, Mike Tilkin, Laura P. Coombs, Keith J. Dreyer, Bibb Allen, Sheela Agarwal, and Jayashree Kalpathy-Cramer. 2020. Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density. Journal of the American College of Radiology 17, 12: 1653–1662.
Ken Chang, Andrew L. Beers (first co-author), Harrison X. Bai, James M. Brown, K. Ina Ly, Xuejun Li, Joeky T. Senders, Vasileios K. Kavouridis, Alessandro Boaro, Chang Su, Wenya Linda Bi, Otto Rapalino, Weihua Liao, Qin Shen, Hao Zhou, Bo Xiao, Yinyan Wang, Paul J. Zhang, Marco C. Pinho, Patrick Y. Wen, Tracy T. Batchelor, Jerrold L. Boxerman, Omar Arnaout, Bruce R. Rosen, Elizabeth R. Gerstner, Li Yang, Raymond Y. Huang, and Jayashree Kalpathy-Cramer. 2019. Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement. Neuro-Oncology 21, 11: 1412–1422.
Andrew Beers, Ken Chang, James Brown, Elizabeth Gerstner, Bruce Rosen, and Jayashree Kalpathy-Cramer. 2018. Sequential neural networks for biologically informed glioma segmentation. In Medical Imaging 2018: Image Processing, 1057433.
Andrew Beers, James Brown, Ken Chang, J. Peter Campbell, Susan Ostmo, Michael F. Chiang, and Jayashree Kalpathy-Cramer. 2018. High-resolution medical image synthesis using progressively grown generative adversarial networks. arXiv:1805.03144 [cs].
Portfolio Items
DeepZine
Generating synthetic book pages using a progressively growing generative adversarial network (PGGAN).
The Russian Ad Explorer
A data explorer for the 3500+ ads bought by the Russian Internet Research Agency (IRA). Contains links to original data, and a dervied JSON dataset from PDF files containing ads. Made with JS and Bootstrap.
DeepNeuro
An open-source utility for segmenting glioblastoma in MRI. Currently in use in glioblastoma clinical trials at Mass General Hospital.
DCE MRI Accuracy Research
Down the rabbit hole of DCE-MRI, a 3D+Time MRI imaging modality used to assess tissue permeability in tumors.
The Grand Banks Iceberg Mapper
134 years of iceberg sightings from shipboard sightings, aerial surveys, satellite data, and newspaper records in one interactive visualization.
Design for America: The Empathy Box
Sharing the stories of those on the autism spectrum via shoeboxes and a distribution network with Design for America.
Bluestockings Magazine
Designing print layouts, soliciting illustrations, and coordinating illustrators for a anti-oppressive publication in Providence. Making 'what does bluestockings think?' a relevant question in Brown University.