@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{balietti2021reducing,

title = {Reducing opinion polarization: Effects of exposure to similar people with differing political views},

author = {Balietti, Stefano and Getoor, Lise and Goldstein, Daniel G. and Watts, Duncan J.},

editor = {Jackson, Matthew},

url = {https://www.pnas.org/content/118/52/e2112552118.short},

doi = {10.1073/pnas.2112552118},

year  = {2021},

date = {2021-12-28},

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

volume = {118},

number = {52},

abstract = {In a large-scale, preregistered experiment on informal political communication, we algorithmically matched participants, varying two dimensions: 1) the degree of incidental similarity on nonpolitical features; and 2) their stance agreement on a contentious political topic. Matched participants were first shown a computer-generated social media profile of their match highlighting all the shared nonpolitical features; then, they read a short, personal, but argumentative, essay written by their match about the reduction of inequality via redistribution of wealth by the government. We show that support for redistribution increased and polarization decreased for participants with both mild and strong views, regardless of their political leaning. We further show that feeling close to the match is associated with an 86% increase in the probability of assimilation of political views. Our analysis also uncovers an asymmetry: Interacting with someone with opposite views greatly reduced feelings of closeness; however, interacting with someone with consistent views only moderately increased them. By extending previous work about the effects of incidental similarity and shared identity on affect into the domain of political opinion change, our results bear real-world implications for the (re)-design of social media platforms. Because many people prefer to keep politics outside of their social networks, encouraging cross-cutting political communication based on nonpolitical commonalities is a potential solution for fostering consensus on potentially divisive and partisan topics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lifchits2021success,

title = {Success stories cause false beliefs about success},

author = {Lifchits, George and Anderson, Ashton and Goldstein, Daniel G. and Hofman, Jake M. and Watts, Duncan J.},

url = {http://journal.sjdm.org/21/210225/jdm210225.pdf},

issn = {1930-2975},

year  = {2021},

date = {2021-11-30},

journal = {Judgment and Decision Making},

volume = {16},

number = {6},

pages = {1439-1463},

abstract = {Many popular books and articles that purport to explain how people, companies, or ideas succeed highlight a few successes chosen to fit a particular narrative. We investigate what effect these highly selected “success narratives” have on readers’ beliefs and decisions. We conducted a large, randomized, pre-registered experiment, showing participants successful firms with founders that all either dropped out of or graduated college, and asked them to make incentive-compatible bets on a new firm. Despite acknowledging biases in the examples, participants’ decisions were very strongly influenced by them. People shown dropout founders were 55 percentage points more likely to bet on a dropout-founded company than people who were shown graduate founders. Most reported medium to high confidence in their bets, and many wrote causal explanations justifying their decision. In light of recent concerns about false information, our findings demonstrate how true but biased information can strongly alter beliefs and decisions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{konitzer2021comparing,

title = {Comparing Estimates of News Consumption from Survey and Passively Collected Behavioral Data},

author = {Konitzer, Tobias and Allen, Jennifer and Eckman, Stephanie and Howland, Baird and Mobius, Markus and Rothschild, David and Watts, Duncan J.},

url = {https://academic.oup.com/poq/advance-article/doi/10.1093/poq/nfab023/6356135?login=true},

doi = {10.1093/poq/nfab023},

year  = {2021},

date = {2021-08-21},

urldate = {2021-08-21},

journal = {Public Opinion Quarterly},

abstract = {Surveys are a vital tool for understanding public opinion and knowledge, but they can also yield biased estimates of behavior. Here we explore a popular and important behavior that is frequently measured in public opinion surveys: news consumption. Previous studies have shown that television news consumption is consistently overreported in surveys relative to passively collected behavioral data. We validate these earlier findings, showing that they continue to hold despite large shifts in news consumption habits over time, while also adding some new nuance regarding question wording. We extend these findings to survey reports of online and social media news consumption, with respect to both levels and trends. Third, we demonstrate the usefulness of passively collected data for measuring a quantity such as “consuming news” for which different researchers might reasonably choose different definitions. Finally, recognizing that passively collected data suffers from its own limitations, we outline a framework for using a mix of passively collected behavioral and survey-generated attitudinal data to accurately estimate consumption of news and related effects on public opinion and knowledge, conditional on media consumption.},

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{allen2021research,

title = {Research note: Examining potential bias in large-scale censored data},

author = {Jennifer Allen and Markus Mobius and David M. Rothschild and Duncan J. Watts},

url = {https://misinforeview.hks.harvard.edu/article/research-note-examining-potential-bias-in-large-scale-censored-data/},

doi = {10.37016/mr-2020-74},

year  = {2021},

date = {2021-07-26},

journal = {Harvard Kennedy School (HKS) Misinformation Review},

abstract = {We examine potential bias in Facebook’s 10-trillion cell URLs dataset, consisting of URLs shared on its platform and their engagement metrics. Despite the unprecedented size of the dataset, it was altered to protect user privacy in two ways: 1) by adding differentially private noise to engagement counts, and 2) by censoring the data with a 100-public-share threshold for a URL’s inclusion. To understand how these alterations affect conclusions drawn from the data, we estimate the prevalence of fake news in the massive, censored URLs dataset and compare it to an estimate from a smaller, representative dataset. We show that censoring can substantially alter conclusions that are drawn from the Facebook dataset. Because of this 100-public-share threshold, descriptive statistics from the Facebook URLs dataset overestimate the share of fake news and news overall by as much as 4X. We conclude with more general implications for censoring data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{watts2021measuring,

title = {Measuring the news and its impact on democracy},

author = {Duncan J Watts and David M Rothschild and Markus Mobius},

url = {https://drive.google.com/file/d/1FiI7IKkNzdMB8DfvlQl9k5U5w4RyyKT9/view},

doi = {10.1073/pnas.1912443118},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

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

volume = {118},

number = {15},

publisher = {National Acad Sciences},

abstract = {Since the 2016 US presidential election, the deliberate spread of misinformation online, and on social media in particular, has generated extraordinary concern, in large part because of its potential effects on public opinion, political polarization, and ultimately democratic decision making. Recently, however, a handful of papers have argued that both the prevalence and consumption of ``fake news'' per se is extremely low compared with other types of news and news-relevant content. Although neither prevalence nor consumption is a direct measure of influence, this work suggests that proper understanding of misinformation and its effects requires a much broader view of the problem, encompassing biased and misleading---but not necessarily factually incorrect---information that is routinely produced or amplified by mainstream news organizations. In this paper, we propose an ambitious collective research agenda to measure the origins, nature, and prevalence of misinformation, broadly construed, as well as its impact on democracy. We also sketch out some illustrative examples of completed, ongoing, or planned research projects that contribute to this agenda.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{allen2020evaluating,

title = {Evaluating the fake news problem at the scale of the information ecosystem},

author = {Jennifer Allen and Baird Howland and Markus Mobius and David Rothschild and Duncan J Watts},

url = {https://drive.google.com/file/d/15fa72A6LcHAY8tjlQa96kSDeA0xIIdFY/view},

doi = {10.1126/sciadv.aay3539},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Science Advances},

volume = {6},

number = {14},

pages = {eaay3539},

publisher = {American Association for the Advancement of Science},

abstract = {``Fake news,'' broadly defined as false or misleading information masquerading as legitimate news, is frequently asserted to be pervasive online with serious consequences for democracy. Using a unique multimode dataset that comprises a nationally representative sample of mobile, desktop, and television consumption, we refute this conventional wisdom on three levels. First, news consumption of any sort is heavily outweighed by other forms of media consumption, comprising at most 14.2% of Americans' daily media diets. Second, to the extent that Americans do consume news, it is overwhelmingly from television, which accounts for roughly five times as much as news consumption as online. Third, fake news comprises only 0.15% of Americans' daily media diet. Our results suggest that the origins of public misinformedness and polarization are more likely to lie in the content of ordinary news or the avoidance of news altogether as they are in overt fakery.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kao2019understanding,

title = {Understanding Cyberbullying on Instagram and Ask.fm via Social Role Detection},

author = {Hsien-Te Kao and Shen Yan and Di Huang and Nathan Bartley and Homa Hosseinmardi and Emilio Ferrara},

url = {https://dl.acm.org/doi/abs/10.1145/3308560.3316505},

doi = {10.1145/3308560.3316505},

year  = {2019},

date = {2019-05-13},

journal = {Companion Proceedings of The 2019 World Wide Web Conference},

pages = {183-188},

abstract = {Cyberbullying is a major issue on online social platforms, and can have prolonged negative psychological impact on both the bullies and their targets. Users can be characterized by their involvement in cyberbullying according to different social roles including victim, bully, and victim supporter. In this work, we propose a social role detection framework to understand cyberbullying on online social platforms, and select a dataset that contains users’ records on both Instagram and Ask.fm as a case study. We refine the traditional victim-bully framework by constructing a victim-bully-supporter network on Instagram. These social roles are automatically identified via ego comment networks and linguistic cues of comments. Additionally, we analyze the consistency of users’ social role within Instagram and compare users’ behaviors on Ask.fm. Our analysis reveals the inconsistency of social roles both within and across platforms, which suggests social roles in cyberbullying are not invariant by conversation, person, or social platform.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ghasemian2019evaluating,

title = {Evaluating overfit and underfit in models of network community structure},

author = {Amir Ghasemian and Homa Hosseinmardi and Aaron Clauset},

url = {https://arxiv.org/pdf/1802.10582},

year  = {2019},

date = {2019-04-16},

journal = {IEEE Transactions on Knowledge and Data Engineering},

volume = {32},

number = {9},

pages = {1722-1735},

abstract = {A common graph mining task is community detection, which seeks an unsupervised decomposition of a network into groups based on statistical regularities in network connectivity. Although many such algorithms exist, community detection’s No Free Lunch theorem implies that no algorithm can be optimal across all inputs. However, little is known in practice about how different algorithms over or underfit to real networks, or how to reliably assess such behavior across algorithms. Here, we present a broad investigation of over and underfitting across 16 state-of-the-art community detection algorithms applied to a novel benchmark corpus of 572 structurally diverse real-world networks. We find that (i) algorithms vary widely in the number and composition of communities they find, given the same input; (ii) algorithms can be clustered into distinct high-level groups based on similarities of their outputs on real-world networks; (iii) algorithmic differences induce wide variation in accuracy on link-based learning tasks; and, (iv) no algorithm is always the best at such tasks across all inputs. Finally, we quantify each algorithm’s overall tendency to over or underfit to network data using a theoretically principled diagnostic, and discuss the implications for future advances in community detection.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lazer2018the,

title = {The science of fake news},

author = {David M. J. Lazer and Matthew A. Baum and Yochai Benkler and Adam J. Berinsky and Kelly M. Greenhill and Filippo Menczer and Miriam J. Metzger and Brendan Nyhan and Gordon Pennycook and David Rothschild and Michael Schudson and Steven A. Sloman and Cass R. Sunstein and Emily A. Thorson and Duncan J. Watts and Jonathan L. Zittrain},

url = {https://science.sciencemag.org/content/359/6380/1094/tab-pdf},

doi = {10.1126/science.aao2998},

year  = {2018},

date = {2018-03-09},

journal = {Science},

volume = {359},

number = {6380},

pages = {1094-1096},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{houghton2017beyond,

title = {Beyond Keywords: Tracking the evolution of conversational clusters in social media},

author = {James P. Houghton and Michael Siegel and Stuart Madnick and Nobuaki Tounaka and Kazutaka Nakamura and Takaaki Sugiyama and Daisuke Nakagawa and Buyanjargal Shirnen},

url = {https://core.ac.uk/download/pdf/188187247.pdf},

doi = {10.1177/0049124117729705},

year  = {2017},

date = {2017-10-09},

journal = {Sociological Methods & Research},

volume = {48},

number = {3},

pages = {588-607},

abstract = {The potential of social media to give insight into the dynamic evolution of public conversations, and into their reactive and constitutive role in political activities, has to date been underdeveloped. While topic modeling can give static insight into the structure of a conversation, and keyword volume tracking can show how engagement with a specific idea varies over time, there is need for a method of analysis able to understand how conversations about societal values evolve and react to events in the world, incorporating new ideas and relating them to existing themes. In this paper, we propose a method for analyzing social media messages that formalizes the structure of public conversations, and allows the sociologist to study the evolution of public discourse in a rigorous, replicable, and data-driven fashion. This approach may be useful to those studying the social construction of meaning, the origins of factionalism and internecine conflict, or boundary-setting and group-identification exercises; and has potential implications for those working to promote understanding and intergroup reconciliation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}