Accelerating dynamics of collective attention

The echo chamber effect on social media

Significance We explore the key differences between the main social media platforms and how they are likely to influence information spreading and the formation of echo chambers. To assess the different dynamics, we perform a comparative analysis on more than 100 million pieces of content concerning controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter. The analysis focuses on two main dimensions: 1) homophily in the interaction networks and 2) bias in the information diffusion toward like-minded peers. Our results show that the aggregation in homophilic clusters of users dominates online dynamics. However, a direct comparison of news consumption on Facebook and Reddit shows higher segregation on Facebook. , Social media may limit the exposure to diverse perspectives and favor the formation of groups of like-minded users framing and reinforcing a shared narrative, that is, echo chambers. However, the interaction paradigms among users and feed algorithms greatly vary across social media platforms. This paper explores the key differences between the main social media platforms and how they are likely to influence information spreading and echo chambers’ formation. We perform a comparative analysis of more than 100 million pieces of content concerning several controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter. We quantify echo chambers over social media by two main ingredients: 1) homophily in the interaction networks and 2) bias in the information diffusion toward like-minded peers. Our results show that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter. We conclude the paper by directly comparing news consumption on Facebook and Reddit, finding higher segregation on Facebook.

The spreading of misinformation online

Significance The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web is a fruitful environment for the massive diffusion of unverified rumors. In this work, using a massive quantitative analysis of Facebook, we show that information related to distinct narratives––conspiracy theories and scientific news––generates homogeneous and polarized communities (i.e., echo chambers) having similar information consumption patterns. Then, we derive a data-driven percolation model of rumor spreading that demonstrates that homogeneity and polarization are the main determinants for predicting cascades’ size. , The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15––where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades’ size.

Social media is a gnarly coordination problem we need to decompose - a warm space

My 2024 thoughts on the state of social media

I see a continued fragmentation of social media as people leave X(Twitter).

An attention economic perspective on the future of the information age

In this paper, we apply an attention economic perspective to explain current and predict future trends in our society. We first describe the rise of the attention economy, and we highlight one important mechanism of this economy: a spiral of attention scarcity. Second, we show that an attention economic perspective provides glimpses of a potential future. In particular, we predict an information environment that increasingly targets citizens with attention-grabbing content, form, and technology; a continuing trend toward excessive media consumption levels; and a continuing trend toward inattentive uses of information. We also predict increasing problems of public misinformation and misconceptions; an increased prevalence of certain mental and physical health issues; and an increased reliance on technology to perform mental tasks. At the end of this paper, we show why and how despite these predictions, alternative futures are conceivable. These alternatives largely depend on the behavior of various social actors. We discuss resistance to the attention economy by consumers and producers, a laissez-faire policy toward the attention economy, a policy of taxing attention-seeking efforts, and the promotion of public values in regulatory policies toward the internet.

Modelling Opinion Dynamics in the Age of Algorithmic Personalisation

Modern technology has drastically changed the way we interact and consume information. For example, online social platforms allow for seamless communication exchanges at an unprecedented scale. However, we are still bounded by cognitive and temporal constraints. Our attention is limited and extremely valuable. Algorithmic personalisation has become a standard approach to tackle the information overload problem. As result, the exposure to our friends' opinions and our perception about important issues might be distorted. However, the effects of algorithmic gatekeeping on our hyper-connected society are poorly understood. Here, we devise an opinion dynamics model where individuals are connected through a social network and adopt opinions as function of the view points they are exposed to. We apply various filtering algorithms that select the opinions shown to users i) at random ii) considering time ordering or iii) their current beliefs. Furthermore, we investigate the interplay between such mechanisms and crucial features of real networks. We found that algorithmic filtering might influence opinions' share and distributions, especially in case information is biased towards the current opinion of each user. These effects are reinforced in networks featuring topological and spatial correlations where echo chambers and polarisation emerge. Conversely, heterogeneity in connectivity patterns reduces such tendency. We consider also a scenario where one opinion, through nudging, is centrally pushed to all users. Interestingly, even minimal nudging is able to change the status quo moving it towards the desired view point. Our findings suggest that simple filtering algorithms might be powerful tools to regulate opinion dynamics taking place on social networks

Changes to the Facebook Algorithm Decreased News Visibility Between 2021-2024

Platforms, especially Facebook, are primary news sources in the US. In its widely criticized "War on News," Meta algorithmically deprioritized news and political content. We use data from 40 news organizations (5,243,302 Facebook posts, 7,875,372,958 user reactions) and 21 non-news pages (396,468 posts; 1,909,088,308 reactions) between January 1, 2016 and February 13, 2025 to examine how these changes influenced news visibility on the platform. Reactions to news declined by 78% between 2021 and 2024 while reactions to non-news pages increased, indicating targeted suppression of news visibility. Low-quality sources were especially suppressed, yet the 2025 end to "War on News" increased user reactions to news, especially low-quality ones. These changes do not reflect decreased news supply, Facebook user base, or interest in news over this period.

Popularity Feedback Constrains Innovation in Cultural Markets

Real-world creative processes ranging from art to science rely on social feedback-loops between selection and creation. Yet, the effects of popularity feedback on collective creativity remain poorly understood. We investigate how popularity ratings influence cultural dynamics in a large-scale online experiment where participants ($N = 1\,008$) iteratively \textit{select} images from evolving markets and \textit{produce} their own modifications. Results show that exposing the popularity of images reduces cultural diversity and slows innovation, delaying aesthetic improvements. Popularity feedback is associated with changes to both selection and creative stages. During selection, popularity information triggers cumulative advantage, with participants preferentially building upon popular images, reducing diversity. During creation, participants make less disruptive changes, and are more likely to expand existing visual patterns. Feedback loops in cultural markets thus not only shape selection, but also, directly or indirectly, the form and direction of cultural innovation.

Variety Effects in Mobile Advertising

Mobile app users are often exposed to a sequence of short-lived marketing interventions (e.g., ads) within each usage session. This study examines how an increase in the variety of ads shown in a session affects a user's response to the next ad. The authors leverage the quasi-experimental variation in ad assignment in their data and propose an empirical framework that accounts for different types of confounding to isolate the effects of a unit increase in variety. Across a series of models, the authors consistently show that an increase in ad variety in a session results in a higher response rate to the next ad: holding all else fixed, a unit increase in variety of the prior sequence of ads can increase the click-through rate on the next ad by approximately 13%. The authors then explore the underlying mechanism and document empirical evidence for an attention-based account. The article offers important managerial implications by identifying a source of interdependence across ad exposures that is often ignored in the design of advertising auctions. Furthermore, the attention-based mechanism suggests that platforms can incorporate real-time attention measures to help advertisers with targeting dynamics.

Shifts in U.S. Social Media Use, 2020–2024: Decline, Fragmentation, and Enduring Polarization

The social media ecosystem appears to be in flux. Twitter’s rebranding to X has come to symbolize a broader reorganization of online publics – political reshuffling, user attrition, and uncertainty over the role of legacy platforms. Simultaneously, the shift from text-based, networked feeds toward algorithmically curated, short-form video has accelerated, with TikTok setting the pace for the wider ecosystem [18, 14, 16]. At the same time, everyday communication increasingly migrates from large, open networks to semi-private spaces such as group chats and messaging apps. Commentators describe a digital public sphere in transition: smaller, more fragmented, less dominated by traditional social networking sites, and rise of more broadcast-oriented forms of media – with some even suggesting that we are seeing the beginning of the end of the social media era.

Impact of the COVID-19 pandemic on social media

Social media became an important platform for interaction during the COVID-19 pandemic, coinciding with the onset of social distancing. According to a study conducted by Facebook's analytics department, messaging rates rose by over 50% during this period. Individuals confined to their homes utilized social media not only to maintain social connections but also as a source of entertainment to alleviate boredom. Concerns arose regarding the overreliance on social media for primary social interactions, particularly given the constraints imposed by the pandemic.

Death by a thousand likes: How Facebook and Twitter are killing the open web

Here’s one way to understand the symbiotic relationship between publications and platforms in the digital age. Publications depend on advertising dollars to keep producing content, so they need to hold readers’ attention. Big platforms like Facebook and Twitter already have plenty of attention, but they need vast quantities of content to fill up their newsfeeds. It seems natural, then, that publications have started relying on platforms to drive readership.

Consequences of Information Feed Integration on User Engagement and Contribution: A Natural Experiment in an Online Knowledge-Sharing Community

Many online communities that rely on effortful, voluntary content contributions offer additional content curation tools to facilitate social interactions and encourage user contributions. Any platform that offers two or more heterogeneous content types (e.g., expert knowledge and social posts) faces a choice about the presentation format: whether to display the content types separately or in an integrated information feed. We leverage a natural experiment on Zhihu, a Q&A platform that offers a social-interaction-oriented functionality called Ideas. Zhihu initially presented answers (expert knowledge content) and ideas (social posts) in two different information feeds, but the platform integrated ideas into the same information feed as answers in June 2019. We find that information feed integration significantly decreased user engagement with and contribution of both ideas and answers. We hypothesize that users decreased their engagement because the juxtaposition of incongruous types of content increased mindset switching and cognitive strain. This hypothesis is supported by an additional laboratory experiment. We also present evidence showing that contributions decreased both because of the decrease in engagement (weaker social recognition incentives) and because integration heightened concerns that posting ideas would dilute the contributor’s professional image. Our findings have important theoretical and practical implications for any platform that hosts heterogeneous content. History: Xiaoquan (Michael) Zhang served as the senior editor and Yili (Kevin) Hong served as associate editor for this article. Funding: Z. Cao acknowledges this research was funded by National Natural Science Foundation of China [Grants 72201238, 72192823]. G. Li acknowledges this research was funded by National Natural Science Foundation of China [Grant 72102047] and Shanghai Pujiang Program [Grant 21PJC006]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/isre.2022.0043.

Sharing without clicking on news in social media

Social media have enabled laypersons to disseminate, at scale, links to news and public affairs information. Many individuals share such links without first reading the linked information. Here we analysed over 35 million public Facebook posts with uniform resource locators shared between 2017 and 2020, and discovered that such ‘shares without clicks’ (SwoCs) constitute around 75% of forwarded links. Extreme and user-aligned political content received more SwoCs, with partisans engaging in it more than politically neutral users. In addition, analyses with 2,969 false uniform resource locators revealed higher shares and, hence, SwoCs by conservatives (76.94%) than liberals (14.25%), probably because, in our dataset, the vast majority (76–82%) of them originated from conservative news domains. Findings suggest that the virality of political content on social media (including misinformation) is driven by superficial processing of headlines and blurbs rather than systematic processing of core content, which has design implications for promoting deliberate discourse in the online public sphere.

The Dynamics of Distortion: How Successive Summarization Alters the Retelling of News

This work advances and tests a theory of how news information evolves as it is successively retold by consumers. Drawing on data from over 11,000 participants across ten experiments, the authors offer evidence that when news is repeatedly retold, it undergoes a stylistic transformation termed “disagreeable personalization,” wherein original facts are increasingly supplanted by opinions and interpretations with a slant toward negativity. The central thesis is that when retellers believe they are more (vs. less) knowledgeable than their recipient about the information they are relaying, they feel more compelled to provide guidance on its meaning and to do so in a persuasive manner. This enhanced motivation to guide persuasively, in turn, leads retellers to not only select the subset of facts they deem most essential but, critically, to provide their interpretations and opinions on those facts, with negativity being used as a means of grabbing their audience’s attention. Implications of this work for research on retelling and consumer information diffusion are explored.