Academic Research

Scientists across disciplines often use data from the internet to conduct research, generating valuable insights about human behavior. However, as generative AI relying on massive text corpora becomes increasingly valuable, platforms have greatly restricted access to data through official channels. As a result, researchers will likely engage in more web scraping to collect data, introducing new challenges and concerns for researchers. This paper proposes a comprehensive framework for web scraping in social science research for U.S.-based researchers, examining the legal, ethical, institutional, and scientific factors that researchers should consider when scraping the web. We present an overview of the current regulatory environment impacting when and how researchers can access, collect, store, and share data via scraping. We then provide researchers with recommendations to conduct scraping in a scientifically legitimate and ethical manner. We aim to equip researchers with the relevant information to mitigate risks and maximize the impact of their research amidst this evolving data access landscape.

In this paper, we aim to situate data portability within the evolving discussions of how to support data access for researchers studying the digital information environment. We explore how data donations, enabled by existing data access rights and data portability requirements, provide promising opportunities for supporting research on critical trust and safety topics. Evaluating other data access mechanisms that are more central to policy debates about platform transparency, we argue that data donations are a powerful additional mechanism that offer key legal, ethical, and scientific benefits. We then assess current challenges with using data donations for research and offer recommendations for various stakeholders to better align portability mechanisms with the needs of research. Taken together, we argue that although portability is often considered within a context of competition and user agency, regulators, industry actors, and researchers should understand and leverage portability’s potential impact to empower critical research on the societal impacts of digital platforms and services.

Online data is key to understanding the leadup to the January 6 insurrection, including how and why election fraud conspiracies spread online, how conspiracy groups organized online to participate in the insurrection, and other factors of online life that led to the insurrection. However, there are significant challenges in accessing data for this research. First, platforms restrict which researchers get access to data, as well as what researchers can do with the data they access. Second, this data is ephemeral; that is, once users or the platform remove the data, researchers can no longer access it. These factors affect what research questions can ever be asked and answered.

Many critics raise concerns about the prevalence of ‘echo chambers’ on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem1,2. Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from ‘like-minded’ sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.

We investigated the effects of Facebook’s and Instagram’s feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content they saw increased on both platforms, the amount of content classified as uncivil or containing slur words they saw decreased on Facebook, and the amount of content from moderate friends and sources with ideologically mixed audiences they saw increased on Facebook. Despite these substantial changes in users’ on-platform experience, the chronological feed did not significantly alter levels of issue polarization, affective polarization, political knowledge, or other key attitudes during the 3-month study period.

Does Facebook enable ideological segregation in political news consumption? We analyzed exposure to news during the US 2020 election using aggregated data for 208 million US Facebook users. We compared the inventory of all political news that users could have seen in their feeds with the information that they saw (after algorithmic curation) and the information with which they engaged. We show that (i) ideological segregation is high and increases as we shift from potential exposure to actual exposure to engagement; (ii) there is an asymmetry between conservative and liberal audiences, with a substantial corner of the news ecosystem consumed exclusively by conservatives; and (iii) most misinformation, as identified by Meta’s Third-Party Fact-Checking Program, exists within this homogeneously conservative corner, which has no equivalent on the liberal side. Sources favored by conservative audiences were more prevalent on Facebook’s news ecosystem than those favored by liberals.

The intersection of social media and politics is yet another realm in which Computational Social Science has a paramount role to play. In this review, I examine the questions that computational social scientists are attempting to answer – as well as the tools and methods they are developing to do so – in three areas where the rise of social media has led to concerns about the quality of democracy in the digital information era: online hate; misinformation; and foreign influence campaigns. I begin, however, by considering a precursor of these topics – and also a potential hope for social media to be able to positively impact the quality of democracy – by exploring attempts to measure public opinion online using Computational Social Science methods. In all four areas, computational social scientists have made great strides in providing information to policy makers and the public regarding the evolution of these very complex phenomena but in all cases could do more to inform public policy with better access to the necessary data; this point is discussed in more detail in the conclusion of the review.