People

@article{nokey,

title = {Deplatforming did not decrease Parler users’ activity on fringe social media},

author = {Ribeiro, Manoel Horta and Hosseinmardi, Homa and West, Robert and Watts, Duncan J.},

url = {https://academic.oup.com/pnasnexus/article/2/3/pgad035/7081430?login=false},

doi = {10.1093/pnasnexus/pgad035},

year  = {2023},

date = {2023-03-21},

journal = {PNAS Nexus},

volume = {2},

issue = {3},

abstract = {Online platforms have banned (“deplatformed”) influencers, communities, and even entire websites to reduce content deemed harmful. Deplatformed users often migrate to alternative platforms, which raises concerns about the effectiveness of deplatforming. Here, we study the deplatforming of Parler, a fringe social media platform, between 2020 January 11 and 2021 February 25, in the aftermath of the US Capitol riot. Using two large panels that capture longitudinal user-level activity across mainstream and fringe social media content (N = 112, 705, adjusted to be representative of US desktop and mobile users), we find that other fringe social media, such as Gab and Rumble, prospered after Parler’s deplatforming. Further, the overall activity on fringe social media increased while Parler was offline. Using a difference-in-differences analysis (N = 996), we then identify the causal effect of deplatforming on active Parler users, finding that deplatforming increased the probability of daily activity across other fringe social media in early 2021 by 10.9 percentage points (pp) (95% CI [5.9 pp, 15.9 pp]) on desktop devices, and by 15.9 pp (95% CI [10.2 pp, 21.7 pp]) on mobile devices, without decreasing activity on fringe social media in general (including Parler). Our results indicate that the isolated deplatforming of a major fringe platform was ineffective at reducing overall user activity on fringe social media.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{muise2022quantifying,

title = {Quantifying partisan news diets in Web and TV audiences},

author = {Daniel Muise and Homa Hosseinmardi and Baird Howland and Markus Mobius and David Rothschild and Duncan J. Watts},

url = {https://www.science.org/doi/10.1126/sciadv.abn0083},

doi = {10.1126/sciadv.abn0083},

year  = {2022},

date = {2022-07-13},

urldate = {2022-07-13},

journal = {Science Advances},

volume = {8},

issue = {28},

abstract = {Partisan segregation within the news audience buffers many Americans from countervailing political views, posing a risk to democracy. Empirical studies of the online media ecosystem suggest that only a small minority of Americans, driven by a mix of demand and algorithms, are siloed according to their political ideology. However, such research omits the comparatively larger television audience and often ignores temporal dynamics underlying news consumption. By analyzing billions of browsing and viewing events between 2016 and 2019, with a novel framework for measuring partisan audiences, we first estimate that 17% of Americans are partisan-segregated through television versus roughly 4% online. Second, television news consumers are several times more likely to maintain their partisan news diets month-over-month. Third, TV viewers’ news diets are far more concentrated on preferred sources. Last, partisan news channels’ audiences are growing even as the TV news audience is shrinking. Our results suggest that television is the top driver of partisan audience segregation among Americans.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hosseinmardi2021examining,

title = {Examining the consumption of radical content on YouTube},

author = {Homa Hosseinmardi and Amir Ghasemian and Aaron Clauset and Markus Mobius and David M. Rothschild and Duncan J. Watts},

url = {https://www.pnas.org/content/118/32/e2101967118},

doi = {10.1073/pnas.2101967118},

year  = {2021},

date = {2021-08-10},

journal = {Proceedings of the National Academy of Sciences},

volume = {118},

number = {32},

abstract = {Although it is under-studied relative to other social media platforms, YouTube is arguably the largest and most engaging online media consumption platform in the world. Recently, YouTube’s scale has fueled concerns that YouTube users are being radicalized via a combination of biased recommendations and ostensibly apolitical “anti-woke” channels, both of which have been claimed to direct attention to radical political content. Here we test this hypothesis using a representative panel of more than 300,000 Americans and their individual-level browsing behavior, on and off YouTube, from January 2016 through December 2019. Using a labeled set of political news channels, we find that news consumption on YouTube is dominated by mainstream and largely centrist sources. Consumers of far-right content, while more engaged than average, represent a small and stable percentage of news consumers. However, consumption of “anti-woke” content, defined in terms of its opposition to progressive intellectual and political agendas, grew steadily in popularity and is correlated with consumption of far-right content off-platform. We find no evidence that engagement with far-right content is caused by YouTube recommendations systematically, nor do we find clear evidence that anti-woke channels serve as a gateway to the far right. Rather, consumption of political content on YouTube appears to reflect individual preferences that extend across the web as a whole.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ghasemian2020stacking,

title = {Stacking models for nearly optimal link prediction in complex networks},

author = {Amir Ghasemian and Homa Hosseinmardi and Aram Galstyan and Edoardo Airoldi and Aaron Clauset},

url = {https://www.pnas.org/content/117/38/23393},

doi = {10.1073/pnas.1914950117},

year  = {2020},

date = {2020-09-22},

journal = {Proceedings of the National Academy of Sciences},

volume = {117},

number = {38},

pages = {23393-23400},

abstract = {Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speed up network data collection and improve network model validation. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 550 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity using network-based metalearning to construct a series of “stacked” models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state of the art for link prediction comes from combining individual algorithms, which can achieve nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvements.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}