publications

2024

1. 

   The diminishing state of shared reality on US television news

   Homa Hosseinmardi, Samuel Wolken, David Rothschild, and Duncan J Watts

   Proceedings of the National Academy of Sciences (R&R), 2024

2. 

   Causally estimating the effect of YouTube’s recommender system using counterfactual bots

   Homa Hosseinmardi, Amir Ghasemian, Miguel Rivera-Lanas, Manoel Horta Ribeiro, Robert West, and Duncan J Watts

   Proceedings of the National Academy of Sciences, 2024

   Bib

   @article{hosseinmardi2023causally,
     title = {Causally estimating the effect of YouTube’s recommender system using counterfactual bots},
     author = {Hosseinmardi, Homa and Ghasemian, Amir and Rivera-Lanas, Miguel and Horta Ribeiro, Manoel and West, Robert and Watts, Duncan J},
     journal = {Proceedings of the National Academy of Sciences},
     volume = {121},
     number = {8},
     pages = {e2313377121},
     year = {2024},
     publisher = {National Acad Sciences},
   }

2023

1. 

   Deplatforming did not decrease Parler users’ activity on fringe social media

   Manoel Horta Ribeiro, Homa Hosseinmardi, Robert West, and Duncan J Watts

   PNAS nexus, 2023

   Bib

   @article{horta2023deplatforming,
     title = {Deplatforming did not decrease Parler users’ activity on fringe social media},
     author = {Horta Ribeiro, Manoel and Hosseinmardi, Homa and West, Robert and Watts, Duncan J},
     journal = {PNAS nexus},
     volume = {2},
     number = {3},
     pages = {pgad035},
     year = {2023},
     publisher = {Oxford University Press US},
   }

2. 

   Tensor embedding: a supervised framework for human behavioral data mining and prediction

   Homa Hosseinmardi, Amir Ghasemian, Kristina Lerman, Shrikanth Narayanan, and Emilio Ferrara

   In 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI), 2023

   Bib

   @inproceedings{hosseinmardi2023tensor,
     abstract_show = {true},
     html_show = {true},
     title = {Tensor embedding: a supervised framework for human behavioral data mining and prediction},
     author = {Hosseinmardi, Homa and Ghasemian, Amir and Lerman, Kristina and Narayanan, Shrikanth and Ferrara, Emilio},
     booktitle = {2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)},
     pages = {91--100},
     year = {2023},
     organization = {IEEE},
   }

2022

1. 

   Quantifying partisan news diets in Web and TV audiences

   Daniel Muise, Homa Hosseinmardi, Baird Howland, Markus Mobius, David Rothschild, and Duncan J Watts

   Science advances, 2022

   Bib

   @article{muise2022quantifying,
     title = {Quantifying partisan news diets in Web and TV audiences},
     author = {Muise, Daniel and Hosseinmardi, Homa and Howland, Baird and Mobius, Markus and Rothschild, David and Watts, Duncan J},
     journal = {Science advances},
     volume = {8},
     number = {28},
     pages = {eabn0083},
     year = {2022},
     publisher = {American Association for the Advancement of Science},
   }

2021

1. 

   Examining the consumption of radical content on YouTube

   Homa Hosseinmardi, Amir Ghasemian, Aaron Clauset, Markus Mobius, David M Rothschild, and Duncan J Watts

   Proceedings of the National Academy of Sciences, 2021

   Bib

   @article{hosseinmardi2021examining,
     title = {Examining the consumption of radical content on YouTube},
     author = {Hosseinmardi, Homa and Ghasemian, Amir and Clauset, Aaron and Mobius, Markus and Rothschild, David M and Watts, Duncan J},
     journal = {Proceedings of the National Academy of Sciences},
     volume = {118},
     number = {32},
     pages = {e2101967118},
     year = {2021},
     publisher = {National Acad Sciences},
   }

2020

1. 

   Stacking models for nearly optimal link prediction in complex networks

   Amir Ghasemian, Homa Hosseinmardi, Aram Galstyan, Edoardo M Airoldi, and Aaron Clauset

   Proceedings of the National Academy of Sciences, 2020

   Bib

   @article{ghasemian2020stacking,
     title = {Stacking models for nearly optimal link prediction in complex networks},
     author = {Ghasemian, Amir and Hosseinmardi, Homa and Galstyan, Aram and Airoldi, Edoardo M and Clauset, Aaron},
     journal = {Proceedings of the National Academy of Sciences},
     volume = {117},
     number = {38},
     pages = {23393--23400},
     year = {2020},
     publisher = {National Acad Sciences},
   }

2019

1. 

   Evaluating overfit and underfit in models of network community structure

   Amir Ghasemian, Homa Hosseinmardi, and Aaron Clauset

   IEEE Transactions on Knowledge and Data Engineering, 2019

   Bib

   @article{ghasemian2019evaluating,
     title = {Evaluating overfit and underfit in models of network community structure},
     author = {Ghasemian, Amir and Hosseinmardi, Homa and Clauset, Aaron},
     journal = {IEEE Transactions on Knowledge and Data Engineering},
     volume = {32},
     number = {9},
     pages = {1722--1735},
     year = {2019},
     publisher = {IEEE},
   }