Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

/pdf/social-media-and-fake-news-in-the-2016-election-1m4ttkb5mo.pdf

Social Media and Fake News in the 2016 Election

Exposure to news, opinion and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using de-identified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks, and examine the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed, and further studied users’ choices to click through to ideologically discordant content. Compared to algorithmic ranking, individuals’ choices about what to consume had a stronger effect limiting exposure to cross-cutting content.

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Exposure to ideologically diverse news and opinion on Facebook

Economics Of Discrimination

Most charitable organizations depend on private contributions, in the form of monetary gifts, volunteer efforts, or other tangible contributions, such as blood donations. The magnitude of private contributions is impressive?in the United States 89 percent of households donate, aver? aging $1,620 per year, and 44 percent of US adults volunteer the equivalent of 9 million full time jobs (Independent Sector 2001). This level of prosocial behavior is striking in light of the economic incentive to free-ride in the provision of public goods. In order to elicit contributions, charitable organizations use many creative efforts to incentivize voluntary giving: wrist bands, thank-you gifts, organized walks, concerts, and advertised donors lists. The government also helps promote charitable giving by offering tax breaks for donations. The various types of charitable contributions and the many real-life ways of soliciting such donations suggest that there may be different motives for individuals to behave prosocially. These motives are roughly divisible into three broad categories: intrinsic, extrinsic, and image motiva? tion. Intrinsic motivation is the value of giving per se, represented by private preferences for others' well-being, such as pure altruism or other forms of prosocial preferences (for surveys, see Ernst Fehr and Klaus Schmidt 2003; Meier 2007). Extrinsic motivation is any material reward or benefit associated with giving, such as thank-you gestures and tax breaks. Image motivation, or signaling motivation, refers to an individual's tendency to be motivated partly by others' percep? tions. Image motivation therefore captures the rule of opinion in utility, i.e., the desire to be liked and respected by others and by one's self. If individuals are looking to gain social approval of their behavior, they should try to signal traits defined as "good" based on the community's norms

/pdf/doing-good-or-doing-well-image-motivation-and-monetary-2p6oe086zj.pdf

Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially

Algorithms are now regularly used to decide whether defendants awaiting trial are too dangerous to be released back into the community. In some cases, black defendants are substantially more likely than white defendants to be incorrectly classified as high risk. To mitigate such disparities, several techniques have recently been proposed to achieve algorithmic fairness. Here we reformulate algorithmic fairness as constrained optimization: the objective is to maximize public safety while satisfying formal fairness constraints designed to reduce racial disparities. We show that for several past definitions of fairness, the optimal algorithms that result require detaining defendants above race-specific risk thresholds. We further show that the optimal unconstrained algorithm requires applying a single, uniform threshold to all defendants. The unconstrained algorithm thus maximizes public safety while also satisfying one important understanding of equality: that all individuals are held to the same standard, irrespective of race. Because the optimal constrained and unconstrained algorithms generally differ, there is tension between improving public safety and satisfying prevailing notions of algorithmic fairness. By examining data from Broward County, Florida, we show that this trade-off can be large in practice. We focus on algorithms for pretrial release decisions, but the principles we discuss apply to other domains, and also to human decision makers carrying out structured decision rules.

Algorithmic Decision Making and the Cost of Fairness

Online publishing, social networks, and web search have dramatically lowered the costs of producing, distributing, and discovering news articles. Some scholars argue that such technological changes increase exposure to diverse perspectives, while others worry that they increase ideological segregation. We address the issue by examining web-browsing histories for 50,000 US-located users who regularly read online news. We find that social networks and search engines are associated with an increase in the mean ideological distance between individuals. However, somewhat counterintuitively, these same channels also are associated with an increase in an individual’s exposure to material from his or her less preferred side of the political spectrum. Finally, the vast majority of online news consumption is accounted for by individuals simply visiting the home pages of their favorite, typically mainstream, news outlets, tempering the consequences—both positive and negative—of recent technological changes. We thus uncover evidence for both sides of the debate, while also finding that the magnitude of the effects is relatively modest.

Filter Bubbles, Echo Chambers, and Online News Consumption

We study processing and acquisition of objective information regarding qualities that people care about, intelligence and beauty. Subjects receiving negative feedback did not respect the strength of these signals, were far less predictable in their updating behavior and exhibited an aversion to new information. In response to good news, inference conformed more closely to Bayes' Rule, both in accuracy and precision. Signal direction did not affect updating or acquisition in our neutral control. Unlike past work, our design varied direction and agreement with priors independently. The results indicate that confirmation bias is driven by direction; confirmation alone had no effect. ( JEL D82, D83)

/pdf/the-good-news-bad-news-effect-asymmetric-processing-of-hk4ne5m7ed.pdf

The Good News-Bad News Effect: Asymmetric Processing of Objective Information about Yourself

If people enjoy giving, then why do they avoid fund-raisers? Partnering with the Salvation Army at Christmastime, we conducted a randomized field experiment placing bell ringers at one or both main entrances to a supermarket, making it easy or difficult to avoid the ask. Additionally, bell ringers either were silent or said “please give.” Making avoidance difficult increased both the rate of giving and donations. Paradoxically, the verbal ask dramatically increased giving but also led to dramatic avoidance. We argue that this illustrates sophisticated awareness of the empathy-altruism link: people avoid empathic stimulation to regulate their giving and guilt.

/pdf/avoiding-the-ask-a-field-experiment-on-altruism-empathy-and-4m96bkxk4h.pdf

Avoiding the Ask: A Field Experiment on Altruism, Empathy, and Charitable Giving

It is widely thought that news organizations exhibit ideological bias, but rigorously quantifying such slant has proven methodologically challenging. Through a combination of machine-learning and crowdsourcing techniques, we investigate the selection and framing of political issues in fifteen major US news outlets. Starting with 803,146 news stories published over twelve months, we first used supervised learning algorithms to identify the 14 percent of articles pertaining to political events. We then recruited 749 online human judges to classify a random subset of 10,502 of these political articles according to topic and ideological position. Our analysis yields an ideological ordering of outlets consistent with prior work. However, news outlets are considerably more similar than generally believed. Specifically, with the exception of political scandals, major news organizations present topics in a largely nonpartisan manner, casting neither Democrats nor Republicans in a particularly favorable or unfavorable light. Moreover, again with the exception of political scandals, little evidence exists of systematic differences in story selection, with all major news outlets covering a wide variety of topics with frequency largely unrelated to the outlet’s ideological position. Finally, news organizations express their ideological bias not by directly advocating for a preferred political party, but rather by disproportionately criticizing one side, a convention that further moderates overall differences.

https://www8.gsb.columbia.edu/media/sites/media/files/JustinRaoMediaBias.pdf

Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis

Twenty-five large field experiments with major U.S. retailers and brokerages, most reaching millions of customers and collectively representing $2.8 million in digital advertising expenditure, reveal that measuring the returns to advertising is difficult. The median confidence interval on return on investment is over 100 percentage points wide. Detailed sales data show that relative to the per capita cost of the advertising, individual-level sales are very volatile; a coefficient of variation of 10 is common. Hence, informative advertising experiments can easily require more than 10 million person-weeks, making experiments costly and potentially infeasible for many firms. Despite these unfavorable economics, randomized control trials represent progress by injecting new, unbiased information into the market. The inference challenges revealed in the field experiments also show that selection bias, due to the targeted nature of advertising, is a crippling concern for widely employed observational methods. JEL Codes: L10, M37, C93.

Justin M. Rao

Papers

Filter Bubbles, Echo Chambers, and Online News Consumption

The Good News-Bad News Effect: Asymmetric Processing of Objective Information about Yourself

Avoiding the Ask: A Field Experiment on Altruism, Empathy, and Charitable Giving

Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis

The Unfavorable Economics of Measuring the Returns to Advertising