| Limiting the spread of misinformation in social networks C Budak, D Agrawal, A El Abbadi Proceedings of the 20th international conference on World wide web, 665-674, 2011 | 1099 | 2011 |
| Fair and balanced? Quantifying media bias through crowdsourced content analysis C Budak, S Goel, JM Rao Public Opinion Quarterly 80 (S1), 250-271, 2016 | 443 | 2016 |
| A first look at COVID-19 information and misinformation sharing on Twitter L Singh, S Bansal, L Bode, C Budak, G Chi, K Kawintiranon, C Padden, ... arXiv preprint arXiv:2003.13907, 2020 | 373 | 2020 |
| Solving path problems on the GPU A Buluç, JR Gilbert, C Budak Parallel Computing 36 (5-6), 241-253, 2010 | 169 | 2010 |
| Structural trend analysis for online social networks C Budak, D Agrawal, A El Abbadi Proceedings of the VLDB Endowment 4 (10), 646-656, 2011 | 133 | 2011 |
| Quick, community-specific learning: How distinctive toxicity norms are maintained in political subreddits A Rajadesingan, P Resnick, C Budak Proceedings of the International AAAI Conference on Web and Social Media 14 …, 2020 | 108 | 2020 |
| Tackling misinformation: What researchers could do with social media data IV Pasquetto, B Swire-Thompson, MA Amazeen, F Benevenuto, ... The Harvard Kennedy School Misinformation Review, 2020 | 100 | 2020 |
| Understanding high-and low-quality URL Sharing on COVID-19 Twitter streams L Singh, L Bode, C Budak, K Kawintiranon, C Padden, E Vraga Journal of computational social science 3, 343-366, 2020 | 92 | 2020 |
| Modeling framing in immigration discourse on social media J Mendelsohn, C Budak, D Jurgens arXiv preprint arXiv:2104.06443, 2021 | 87 | 2021 |
| Dissecting the spirit of Gezi: Influence vs. selection in the Occupy Gezi movement C Budak, DJ Watts Sociological Science 2, 370-397, 2015 | 87 | 2015 |
| Geoscope: Online detection of geo-correlated information trends in social networks C Budak, T Georgiou, D Agrawal, A El Abbadi Proceedings of the VLDB Endowment 7 (4), 229-240, 2013 | 86 | 2013 |
| What happened? the spread of fake news publisher content during the 2016 us presidential election C Budak The World Wide Web Conference, 139-150, 2019 | 83 | 2019 |
| Toward a better performance evaluation framework for fake news classification L Bozarth, C Budak Proceedings of the international AAAI conference on web and social media 14 …, 2020 | 79 | 2020 |
| On participation in group chats on twitter C Budak, R Agrawal Proceedings of the 22nd international conference on World Wide Web, 165-176, 2013 | 68 | 2013 |
| Information diffusion in social networks: Observing and influencing societal interests D Agrawal, C Budak, AE Abbadi Proceedings of the VLDB Endowment 4 (12), 1512-1513, 2011 | 68 | 2011 |
| Neural embeddings of scholarly periodicals reveal complex disciplinary organizations H Peng, Q Ke, C Budak, DM Romero, YY Ahn Science Advances 7 (17), eabb9004, 2021 | 65 | 2021 |
| Big data in online social networks: user interaction analysis to model user behavior in social networks D Agrawal, C Budak, A El Abbadi, T Georgiou, X Yan Databases in Networked Information Systems: 9th International Workshop, DNIS …, 2014 | 56 | 2014 |
| Words that matter: How the news and social media shaped the 2016 Presidential campaign L Bode, C Budak, JM Ladd Brookings Institution Press, 2020 | 48 | 2020 |
| Higher ground? How groundtruth labeling impacts our understanding of fake news about the 2016 US presidential nominees L Bozarth, A Saraf, C Budak Proceedings of the International AAAI Conference on Web and Social Media 14 …, 2020 | 45 | 2020 |
| Guangqing Chi, Kornraphop Kawintiranon, Colton Padden, Rebecca Vanarsdall, Emily Vraga, and Yanchen Wang. 2020. A first look at COVID-19 information and misinformation sharing … L Singh, S Bansal, L Bode, C Budak arXiv preprint arXiv:2003.13907 1, 1, 2020 | 43 | 2020 |
