Misunderstanding the harms of online misinformation.

The controversy over online misinformation and social media has opened a gap between public discourse and scientific research. Public intellectuals and journalists frequently make sweeping claims about the effects of exposure to false content online that are inconsistent with much of the current empirical evidence. Here we identify three common misperceptions: that average exposure to problematic content is high, that algorithms are largely responsible for this exposure and that social media is a primary cause of broader social problems such as polarization. In our review of behavioural science research on online misinformation, we document a pattern of low exposure to false and inflammatory content that is concentrated among a narrow fringe with strong motivations to seek out such information. In response, we recommend holding platforms accountable for facilitating exposure to false and extreme content in the tails of the distribution, where consumption is highest and the risk of real-world harm is greatest. We also call for increased platform transparency, including collaborations with outside researchers, to better evaluate the effects of online misinformation and the most effective responses to it. Taking these steps is especially important outside the USA and Western Europe, where research and data are scant and harms may be more severe.

Measuring the news and its impact on democracy.

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.

Examining the consumption of radical content on YouTube.

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.

Erratum to Peer et al. (2021) Data quality of platforms and panels for online behavioral research.

This erratum reports on a technical error that was discovered in Study 2 of Peer et al. (2021). Because of this technical error, some specific findings on participants' proclivity for dishonesty reported in the paper have been found incorrect. We detail the error, which only affected female participants, and its impact on the findings and report on the reanalyzed findings accounting for the error. The new findings do not change the conclusions provided in the paper, and show again that participants from MTurk are more likely to engage in dishonest behavior than participants from Prolific or CloudResearch.

Data quality of platforms and panels for online behavioral research.

We examine key aspects of data quality for online behavioral research between selected platforms (Amazon Mechanical Turk, CloudResearch, and Prolific) and panels (Qualtrics and Dynata). To identify the key aspects of data quality, we first engaged with the behavioral research community to discover which aspects are most critical to researchers and found that these include attention, comprehension, honesty, and reliability. We then explored differences in these data quality aspects in two studies (N ~ 4000), with or without data quality filters (approval ratings). We found considerable differences between the sites, especially in comprehension, attention, and dishonesty. In Study 1 (without filters), we found that only Prolific provided high data quality on all measures. In Study 2 (with filters), we found high data quality among CloudResearch and Prolific. MTurk showed alarmingly low data quality even with data quality filters. We also found that while reputation (approval rating) did not predict data quality, frequency and purpose of usage did, especially on MTurk: the lowest data quality came from MTurk participants who report using the site as their main source of income but spend few hours on it per week. We provide a framework for future investigation into the ever-changing nature of data quality in online research, and how the evolving set of platforms and panels performs on these key aspects.

The cost of anti-Asian racism during the COVID-19 pandemic.

Anti-Chinese sentiment increased during the COVID-19 pandemic, presenting as a considerable spike in overt violence and hatred directed at Asian American individuals. However, it is less clear how subtle patterns of consumer discrimination, which are difficult to directly observe yet greatly impact Asian American livelihoods, changed through the pandemic. Here we examine this in the context of restaurants-ubiquitous small businesses that sell goods that are closely entwined with ethnicity. Using a series of surveys, online search trends and consumer traffic data, we find that Asian restaurants experienced an 18.4% decrease in traffic (estimated US$7.42 billion lost revenue in 2020) relative to comparable non-Asian restaurants, with greater decreases in areas with higher levels of support for Donald Trump. Our findings are consistent with the roles of collective blame, out-group homogeneity and ethnic misidentification in explaining how anti-China rhetoric can harm the Asian American community, underlining the importance of avoiding racism and stigmatization in political and public health communications.

Quantifying partisan news diets in Web and TV audiences.

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.

Vaccine advertising: preach to the converted or to the unaware?

Encouraging people to vaccinate is a challenging endeavor, but one which has tremendous public health benefits. Doing so requires overcoming barriers of awareness, availability, and (sometimes) vaccine hesitancy. Here we focus on nudging people to vaccinate through online advertising. We conducted a pre-registered online ads campaign encouraging people to vaccinate against three diseases: influenza, human papillomavirus, and herpes zoster. Ads were shown to ~69,000 people and were compared to similar ads shown to 8.6 million people. Outcome measures were clicks on ads and future searches for relevant terms. We find that ads have two main effects: First, a congruence effect whereby ads increase the likelihood of clicks and future searches by up to 116% in people who express an interest in the disease or the vaccine. Most commercial vaccine advertising is aimed entirely at this population. Second, we observed a priming effect, where ads shown to people who were searching for terms unrelated to the vaccine could be encouraged to click on them (odds ratios of 7.5-33.0) and, more often, search for the vaccine later (hazard ratios of 6.9-157.3). We provide analysis for optimizing vaccine advertising campaign budgets to balance the two populations. These findings demonstrate that digital advertising campaigns should consider not just advertising to direct keywords or to individuals that look exactly like existing customers, but consider tangential keywords that draw a wider target population who are likely earlier in their conversion funnel, thus increasing the number of people who vaccinate and maximizing vaccines uptake.

Evaluating the fake news problem at the scale of the information ecosystem.

"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.

Understanding market functionality and trading success.

We examine individual-level trading data from several markets in the PredictIt exchange to determine what strategies correlate with financial success. PredictIt provides many markets with futures contracts linked to political issues, ranging from ongoing policy outcomes to political elections. High fees along with restrictions blocking automatic trading and constraining a one-to-one match between people and accounts, combine to severely limit the upside to investment returns over the fixed costs: this ensures that traders are all retail investors. We have the individual-level data from two markets: Democratic and Republican Iowa Caucuses in 2016. This data includes all orders and trades from every trader across the markets. We are able to fully reconstruct market activity and study trader behavior both within and between markets. We show that understanding how markets and trades works is more important to financial success than proxies for (1) confidence or funding (2) information or objectivity in trading. The work should be a call-to-action in favor of simplifying markets and trading for any exchange with retail investors, and for more research into effects of differential trading efficiency in all financial markets.

Online and Social Media Data As an Imperfect Continuous Panel Survey.

There is a large body of research on utilizing online activity as a survey of political opinion to predict real world election outcomes. There is considerably less work, however, on using this data to understand topic-specific interest and opinion amongst the general population and specific demographic subgroups, as currently measured by relatively expensive surveys. Here we investigate this possibility by studying a full census of all Twitter activity during the 2012 election cycle along with the comprehensive search history of a large panel of Internet users during the same period, highlighting the challenges in interpreting online and social media activity as the results of a survey. As noted in existing work, the online population is a non-representative sample of the offline world (e.g., the U.S. voting population). We extend this work to show how demographic skew and user participation is non-stationary and difficult to predict over time. In addition, the nature of user contributions varies substantially around important events. Furthermore, we note subtle problems in mapping what people are sharing or consuming online to specific sentiment or opinion measures around a particular topic. We provide a framework, built around considering this data as an imperfect continuous panel survey, for addressing these issues so that meaningful insight about public interest and opinion can be reliably extracted from online and social media data.

Population structure of pierid butterflies : I. Numbers and movements of some montane Colias species.

The numbers, dispersal behavior, aging and residence, and Wrightian neighborhood configurations of three species of Colias butterflies have been studied in central Colorado, using mark-release-recapture techniques as major tools. All populations studied have nonoverlapping generations and mature one brood each year. A brief general review of these species' autecology is given. A system for measuring degree of physical damage to the adults is introduced. This "wear rating" varies with temporal position of any given sample in the course of a brood's flight season, the insects becoming progressively more damaged with time. The sex ratio also changes with brood aging: males eclose before females, and are in the majority early in the flight season, while females may predominate at the end of flight. Local population numbers for the montane grassland species C. alexandra may reach peak levels of 700-900 insects in favorable years, but be much lower in other years as a result of, e.g., drought. Peak densities are no more than 2/ha. The montane bog species, C. scudderi, maintains comparable low density but has much smaller local populations. The subalpine/alpine grassland species C. meadii displays peak local numbers as high as 3000, with peak density as high as 120/hectare. Dispersal varies both among and within species. Those C. alexandra who disperse show an average dispersal radius of about 1.3 km, with a radius for the whole population of about 0.6 km; maximum distance moved was 8 km. Dispersal proportions among recaptures are sharply curtailed by adverse weather, but the dispersal radius of those moving is unaffected by weather. C. scudderi's dispersal is strongly influenced by the geometry of its bog and streamside habitats. Some C. meadii populations approach isolated "island" status, but others show much dispersal. Dispersal radius of those dispersing ranges from 0.3 to 0.7 km in different populations, but the proportion of dispersals varies greatly. The longest observed movement by this species is 1.3 km, although up to 2.6 km could have been detected. Colias normally display constant loss (death plus emigration) rates with average residence expectations of 4-6 days; few insects reach their maximum physiological lifespan of approximately 1 month. Bad weather can increase the loss rate drastically. Females show shorter residence than males, appearently as a result of greater mortality. Total-numbers-per-brood estimates are given for our better studied populations. The reproductive strategy of Colias is such that Wright's models for neighborhood size apply. Neighborhood size for C. alexandra varied sixfold in numbers, and from 3 to 1.3 km in physical extent, between a favorable year and a drought year. One localized C. scudderi habitat is only 200 m in diameter, but a streamside population has a neighborhood length of 4.8 km. In C. meadii, one population of 2000-2500 insects is an 8-ha "island", while another of similar numbers extends a single neighborhood across 1.9 km distance, 450 m altitude, and a major ecological boundary (timberline). Factors such as weather, individuals' visual cueing, and thermoregulatory behavior can influence population structure. For some Colias populations, selection may be very uniform within neighborhoods, while for others, single neighborhoods cross sharp discontinuities in selective forces. These patterns may differ for different selective forces, and may also vary with stages of the insects' life cycle. these populations will now prove a valuable resource for studying evolutionary population genetics.

Wait times, patient satisfaction scores, and the perception of care.

OBJECTIVES: To analyze the impact of waiting time on patient satisfaction scores; not only of satisfaction with the provider in general, but also with the specific perception of the quality of care and physician abilities. STUDY DESIGN: Using surveys regarding patient satisfaction with provider care, data was collected from a sample of 11,352 survey responses returned by patients over the course of 1 year across all 44 ambulatory clinics within a large academic medical center. While a small minority of patients volunteered identification, the surveys were made anonymously. METHODS: A questionnaire with Health Consumer Assessment of Healthcare Providers and Systems patient satisfaction and waiting time queries was administered via mail to all clinic patients-roughly 49,000-with a response rate of 23%. Employing a standard statistical approach, results were tabulated and stratified according to provider scores and wait time experience, and then analyzed using statistical modeling techniques. RESULTS: While it is well established that longer wait times are negatively associated with clinical provider scores of patient satisfaction, results indicated that every aspect of patient experience-specifically confidence in the care provider and perceived quality of care-correlated negatively with longer wait times. CONCLUSIONS: The clinical ambulatory patient experience is heavily influenced by time spent waiting for provider care. Not only are metrics regarding the likelihood to recommend and the overall satisfaction with the experience negatively impacted by longer wait times, but increased wait times also affect perceptions of information, instructions, and the overall treatment provided by physicians and other caregivers.