We provide a guide to the burgeoning literature on the economics of social media. We first define social media platforms and highlight their unique features. We then synthesize the main lessons from the empirical economics literature and organize them around the three stages of the life cycle of content: (1) production, (2) distribution, and (3) consumption. Under production, we discuss how incentives affect content produced on and off social media and how harmful content is moderated. Under distribution, we discuss the social network structure, algorithms, and targeted advertisements. Under consumption, we discuss how social media affects individuals who consume its content and society at large, and explore consumer substitution patterns across platforms. Throughout the guide, we examine case studies on the deterrence of misinformation, segregation, political advertisements, and the effects of social media on political outcomes. We conclude with a brief discussion of the future of social media.

We provide quasi-experimental estimates of the impact of social media on mental health by leveraging a unique natural experiment: the staggered introduction of Facebook across US colleges. Our analysis couples data on student mental health around the years of Facebook’s expansion with a generalized difference-in-differences empirical strategy. We find that the rollout of Facebook at a college had a negative impact on student mental health. It also increased the likelihood with which students reported experiencing impairments to academic performance due to poor mental health. Additional evidence on mechanisms suggests the results are due to Facebook fostering unfavorable social comparisons.

Does the consumption of ideologically congruent news on social media exacerbate polarization? I estimate the effects of social media news exposure by conducting a large field experiment randomly offering participants subscriptions to conservative or liberal news outlets on Facebook. I collect data on the causal chain of media effects: subscriptions to outlets, exposure to news on Facebook, visits to online news sites, and sharing of posts, as well as changes in political opinions and attitudes. Four main findings emerge. First, random variation in exposure to news on social media substantially affects the slant of news sites that individuals visit. Second, exposure to counter-attitudinal news decreases negative attitudes toward the opposing political party. Third, in contrast to the effect on attitudes, I find no evidence that the political leanings of news outlets affect political opinions. Fourth, Facebook’s algorithm is less likely to supply individuals with posts from counter-attitudinal outlets, conditional on individuals subscribing to them. Together, the results suggest that social media algorithms may limit exposure to counter-attitudinal news and thus increase polarization.

Pervasive overuse and degradation of common pool resources (CPRs) is a global concern. To sustainably manage CPRs, effective governance institutions are essential. A large literature has developed to describe the institutional design features employed by communities that successfully manage their CPRs. Yet, these designs remain far from universally adopted. We focus on one prominent institutional design feature, community monitoring, and ask whether nongovernmental organizations or governments can facilitate its adoption and whether adoption of monitoring affects CPR use. To answer these questions, we implemented randomized controlled trials in six countries. The harmonized trials randomly assigned the introduction of community monitoring to 400 communities, with data collection in an additional 347 control communities. Most of the 400 communities adopted regular monitoring practices over the course of a year. In a meta-analysis of the experimental results from the six sites, we find that the community monitoring reduced CPR use and increased user satisfaction and knowledge by modest amounts. Our findings demonstrate that community monitoring can improve CPR management in disparate contexts, even when monitoring is externally initiated rather than homegrown. These findings provide guidance for the design of future programs and policies intended to develop monitoring capabilities in communities. Furthermore, our harmonized, multisite trial provides sustainability science with a new way to study the complexity of socioecological systems and builds generalizable insights about how to improve CPR management.

We study the effects of social media political advertising by randomizing subsets of 36,906 Facebook users and 25,925 Instagram users to have political ads removed from their news feeds for six weeks before the 2020 US presidential election. We show that most presidential ads were targeted toward parties’ own supporters and that fundraising ads were most common. On both Facebook and Instagram, we found no detectable effects of removing political ads on political knowledge, polarization, perceived legitimacy of the election, political participation (including campaign contributions), candidate favorability, and turnout. This was true overall and for both Democrats and Republicans separately.

Social movements are associated with large societal changes, but evidence on their causal effects is limited. We study the effect of the MeToo movement on an important personal decision—reporting a sex crime to the police. We construct a new quarterly dataset of crimes reported in 31 OECD countries and analyze the effect of the MeToo movement by employing a triple-difference strategy over time, across countries with strong and weak MeToo movements, and between crime types. The movement increased reporting of sex crimes by 10% during its first six months. The effect persists until the end of our data, more than a year after the movement started. Using more detailed US data, we show that the MeToo movement not only increased reporting, but also increased arrests for sexual assaults. In contrast to a common criticism of the movement, we do not find evidence for large differences in the effect across racial and socioeconomic groups. Based on additional survey and crime data, we show that the increased reporting reflects a higher propensity to report sex crimes, and not an increase in the incidence of sex crimes. The mechanism most consistent with our results is that victims were more motivated to report sex crimes because individuals perceived sexual misconduct to be a more serious problem following the MeToo movement. Our results demonstrate that social movements can rapidly and persistently change high-stakes personal decisions.

We provide a practical guide to designing, conducting, and analyzing experiments using social media platforms. First, we discuss the benefits and challenges of using the targeting capabilities of advertisements on social media to recruit participants for a large class of experiments. Next, we outline the different types of interventions and their advantages and disadvantages. Finally, we summarize available compliance and outcome data, as well as the main limitations and challenges involved in the design and analysis of social media experiments. Throughout, we provide technical details that are helpful when implementing these experiments. Overall, we argue that experiments on social media are powerful not only for studying economic issues around social media and online platforms but also for experiments studying economic behavior more broadly.

How polarized is news consumption on social media? We fine-tune a large-language-model to assign a content-based measure of slant to the near-universe of hard-news articles published online by the top 100 U.S. outlets in 2019. We find that polarized news consumption on Facebook, defined as the difference in the average slant of articles consumed by Republicans and Democrats on the platform, is arguably high. We identify pro-attitudinal news consumption within outlets as a crucial mechanism. Analyzing the distribution of slant on the production side, we find that 67% of the article-level variation in slant is within rather than across outlets.

Support for populist radical right parties in Europe has dramatically increased in the twenty-first century. We decompose the rise of the populist radical right between 2005 and 2020 into four components: changes in party positions, changes in voter attributes (demographics and opinions), changes in voters’ priorities, and a residual. We merge two wide data sets on party positions and voter attributes and estimate voter priorities using a probabilistic voting model. Voter priorities determine the weights voters place on different party positions, given their attributes. We find that shifts in party positions and changes in voter attributes explain only a negligible part of the rise of populist radical right parties. The primary driver behind the success of these parties lies in voters’ changing priorities. Particularly, voters are less likely to decide which party to support based on parties’ economic positions. Instead, voters—mainly older, non-unionized, low-educated men—increasingly prioritize nationalist cultural issues. This allows populist radical right parties to tap into a pre-existing reservoir of culturally conservative voters.