The coercive logic of fake news

The spread of misinformation and "fake news" continues to be a major focus of public concern. A great deal of research has examined who falls for misinformation and why, and what can be done to make people more discerning consumers of news. Comparatively little work, however, has considered misinformation producers, and how their strategies interact with the psychology of news consumers. Here we use game-theoretic models to study the strategic interaction between news publishers and news readers. We show that publishers who seek to spread misinformation can generate high engagement with falsehoods by using strategies that mix true and false stories over time, in such a way that they serve more false stories to more loyal readers. These coercive strategies cause false stories to receive higher reader engagement than true stories - even when readers strictly prefer truth over falsehood. In contrast, publishers who seek to promote engagement with accurate information will use strategies that generate more engagement with true stories than with false stories. We confirm these predictions empirically by examining 1,000 headlines from 20 mainstream and 20 fake news sites, comparing Facebook engagement data with 20,000 perceived accuracy ratings collected in a survey experiment. We show that engagement is negatively correlated with perceived accuracy among misinformation sites, but positively correlated with perceived accuracy among mainstream sites. We then use our model to analyze the conditions under which news sites seeking engagement will produce false stories. We show that if a publisher incorrectly assumes that readers prefer falsehoods, their resulting publication strategy can itself manufacture greater engagement with false news - leading to a self-reinforcing cycle of false news promotion

The distorting effects of producer strategies : why engagement does not reveal consumer preferences for misinformation

A great deal of empirical research has examined who falls for misinformation and why. Here, we introduce a formal game-theoretic model of engagement with news stories that captures the strategic interplay between (mis)information consumers and producers. A key insight from the model is that observed patterns of engagement do not necessarily reflect the preferences of consumers. This is because producers seeking to promote misinformation can use strategies that lead moderately inattentive readers to engage more with false stories than true ones—even when readers prefer more accurate over less accurate information. We then empirically test people’s preferences for accuracy in the news. In three studies, we find that people strongly prefer to click and share news they perceive as more accurate—both in a general population sample, and in a sample of users recruited through Twitter who had actually shared links to misinformation sites online. Despite this preference for accurate news—and consistent with the predictions of our model—we find markedly different engagement patterns for articles from misinformation versus mainstream news sites. Using 1,000 headlines from 20 misinformation and 20 mainstream news sites, we compare Facebook engagement data with 20,000 accuracy ratings collected in a survey experiment. Engagement with a headline is negatively correlated with perceived accuracy for misinformation sites, but positively correlated with perceived accuracy for mainstream sites. Taken together, these theoretical and empirical results suggest that consumer preferences cannot be straightforwardly inferred from empirical patterns of engagement.Peer reviewe

Conducting interactive experiments online

Online labor markets provide new opportunities for behavioral research, but conducting economic experiments online raises important methodological challenges. This particularly holds for interactive designs. In this paper, we provide a methodological discussion of the similarities and differences between interactive experiments conducted in the laboratory and online. To this end, we conduct a repeated public goods experiment with and without punishment using samples from the laboratory and the online platform Amazon Mechanical Turk. We chose to replicate this experiment because it is long and logistically complex. It therefore provides a good case study for discussing the methodological and practical challenges of online interactive experimentation. We find that basic behavioral patterns of cooperation and punishment in the laboratory are replicable online. The most important challenge of online interactive experiments is participant dropout. We discuss measures for reducing dropout and show that, for our case study, dropouts are exogenous to the experiment. We conclude that data quality for interactive experiments via the Internet is adequate and reliable, making online interactive experimentation a potentially valuable complement to laboratory studies

Turking in the time of COVID

Abstract On March 16, 2020, the US Government introduced strict social distancing protocols for the United States in an effort to stem the spread of the COVID-19 pandemic. This had an immediate major effect on the job market, with millions of Americans forced to find alternative ways to make a living from home. As online labor markets like Amazon Mechanical Turk (MTurk) play a major role in social science research, concerns have been raised that the pandemic may be reducing the diversity of subjects participating in experiments. Here, we investigate this possibility empirically. Specifically, we look at 15,539 responses gathered in 23 studies run on MTurk between February and July 2020, examining the distribution of gender, age, ethnicity, political preference, and analytic cognitive style. We find notable changes on some of the measures following the imposition of nationwide social distancing: participants are more likely to be less reflective (as measured by the Cognitive Reflection Test), and somewhat less likely to be white, Democrats (traditionally over-represented on MTurk), and experienced with MTurk. Most of these differences are explained by an influx of new participants into the MTurk subject pool who are more diverse and representative – but also less attentive – than previous MTurkers

“I'm just a soul whose intentions are good”: the role of communication in noisy repeated games

We let participants indicate their intended action in a repeated game experiment where actions are implemented with errors. Even though communication is cheap talk, we find that the majority of messages were honest (although the majority of participants lied at least occasionally). As a result, communication has a positive effect on cooperation when the payoff matrix makes the returns to cooperation high; when the payoff matrix gives a lower return to cooperation, communication reduces overall cooperation. These results suggest that cheap talk communication can promote cooperation in repeated games, but only when there is already a self-interested motivation to cooperate. ©2017National Science Foundation Grant (no. SES- 1258665

From foe to friend and back again: The temporal dynamics of intra-party bias in the 2016 U.S. Presidential Election

Political identification is the basis of enduring conflict, suggesting that political attitudes are difficult to change. Here we show that in the 2016 U.S. Presidential Election, political identities underwent modification in response to salient political events. We investigate these dynamics in detail by collecting data at periodic intervals from mid-June 2016 through the general election (N = 3,958). We operationalize identification using prosocial giving in Dictator Games played between supporters of competing primary candidates recruited from Amazon Mechanical Turk. The observed dynamics differed across political parties. In-group bias among Democrats remained high until the Democratic National Convention, disappeared shortly thereafter, and then returned during the final stage of the election. Bias among Republicans was generally high until the final days of the election. The late resurgence of bias among Democrats was not reflected in voting intentions, but may have presaged the Democratic election loss

Examining Spillovers between Long and Short Repeated Prisoner’s Dilemma Games Played in the Laboratory

We had participants play two sets of repeated Prisoner’s Dilemma (RPD) games, one with a large continuation probability and the other with a small continuation probability, as well as Dictator Games (DGs) before and after the RPDs. We find that, regardless of which is RPD set is played first, participants typically cooperate when the continuation probability is large and defect when the continuation probability is small. However, there is an asymmetry in behavior when transitioning from one continuation probability to the other. When switching from large to small, transient higher levels of cooperation are observed in the early games of the small continuation set. Conversely, when switching from small to large, cooperation is immediately high in the first game of the large continuation set. We also observe that response times increase when transitioning between sets of RPDs, except for altruistic participants transitioning into the set of RPDs with long continuation probabilities. These asymmetries suggest a bias in favor of cooperation. Finally, we examine the link between altruism and RPD play. We find that small continuation probability RPD play is correlated with giving in DGs played before and after the RPDs, whereas high continuation probability RPD play is not