| Covid-twitter-bert: A natural language processing model to analyse covid-19 content on twitter M Müller, M Salathé, PE Kummervold Frontiers in Artificial Intelligence 6, 1023281, 2023 | 327 | 2023 |
| Crowdbreaks: tracking health trends using public social media data and crowdsourcing MM Müller, M Salathé Frontiers in public health 7, 81, 2019 | 62 | 2019 |
| COVID-19 misinformation spread in eight countries: exponential growth modeling study EO Nsoesie, N Cesare, M Müller, A Ozonoff Journal of medical Internet research 22 (12), e24425, 2020 | 43 | 2020 |
| Keep calm and carry on vaccinating: Is anti-vaccination sentiment contributing to declining vaccine coverage in England? M Edelstein, M Müller, S Ladhani, J Yarwood, M Salathé, M Ramsay Vaccine 38 (33), 5297-5304, 2020 | 29 | 2020 |
| Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic F Durazzi, M Müller, M Salathé, D Remondini Scientific reports 11 (1), 19655, 2021 | 22 | 2021 |
| Assessing Public Opinion on CRISPR-Cas9: Combining Crowdsourcing and Deep Learning M Muller, M Schneider, M Salathé, E Vayena BioRxiv, 802454, 2019 | 18* | 2019 |
| Addressing machine learning concept drift reveals declining vaccine sentiment during the COVID-19 pandemic M Müller, M Salathé arXiv preprint arXiv:2012.02197, 2020 | 17 | 2020 |
| Wet markets and food safety: TripAdvisor for improved global digital surveillance NE Kogan, I Bolon, N Ray, G Alcoba, JL Fernandez-Marquez, MM Müller, ... JMIR public health and surveillance 5 (2), e11477, 2019 | 17 | 2019 |
| Citizen science and online data: Opportunities and challenges for snake ecology and action against snakebite AM Durso, RR de Castañeda, C Montalcini, MR Mondardini, ... Toxicon: X 9, 100071, 2021 | 15 | 2021 |
| Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis K Gligorić, MH Ribeiro, M Müller, O Altunina, M Peyrard, M Salathé, ... arXiv preprint arXiv:2008.08364, 2020 | 9 | 2020 |
| COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter.(2020) M Müller, M Salathé, PE Kummervold arXiv preprint cs.CL/2005.07503, 2020 | 7 | 2020 |
| Cedille: A large autoregressive French language model M Müller, F Laurent arXiv preprint arXiv:2202.03371, 2022 | 6 | 2022 |
| Covid-twitter-bert: a natural language processing model to analyse COVID-19 content on twitter. CoRR M Müller, M Salathé, PE Kummervold | 4 | 2005 |
| Summary of tutorials at the Web Conference 2021 R West, S Bhagat, P Groth, M Zitnik, FM Couto, P Lisena, ... Companion Proceedings of the Web Conference 2021, 727-733, 2021 | 3 | 2021 |
| International expert communities on Twitter become more isolated during the COVID-19 pandemic F Durazzi, M Müller, M Salathé, D Remondini | 2 | 2020 |
| ORF3c is expressed in SARS-CoV-2 infected cells and suppresses immune activation by inhibiting innate sensing M Mueller, A Herrmann, S Fujita, K Uriu, C Kruth, A Strange, JE Kolberg, ... bioRxiv, 2023.02. 27.530232, 2023 | 1 | 2023 |
| On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology MM Müller EPFL, 2021 | 1 | 2021 |
| Underlying and Resulting Data of Sentiment Analysis on Tweets about CRISPR/Cas9 M Schneider, M Müller ETH Zurich, 2019 | | 2019 |
| Group ID U13083 Affiliated authors Allémann, Chloé O Altunina, O Balakiriev, S Bernard, P Borg, VG Boulanger, C Burger, ... | | |
