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.

Remembering the pre-Google web, when search was an experiment

Most people have completely forgotten how chaotic it really was."

Accelerating dynamics of collective attention

With news pushed to smart phones in real time and social media reactions spreading across the globe in seconds, the public discussion can appear accelerated and temporally fragmented. In longitudinal datasets across various domains, covering multiple decades, we find increasing gradients and shortened periods in the trajectories of how cultural items receive collective attention. Is this the inevitable conclusion of the way information is disseminated and consumed? Our findings support this hypothesis. Using a simple mathematical model of topics competing for finite collective attention, we are able to explain the empirical data remarkably well. Our modeling suggests that the accelerating ups and downs of popular content are driven by increasing production and consumption of content, resulting in a more rapid exhaustion of limited attention resources. In the interplay with competition for novelty, this causes growing turnover rates and individual topics receiving shorter intervals of collective attention.

The Hatred of Podcasting | Brace Belden

In 2015, if you said, “I heard it on a podcast,” you were trying to sound smart. In 2025, it’s better to lie.

People are not friction

The Gell-Mann Amnesia Effect of AI is a pretty well documented phenomenon: The Gell-Mann amnesia effect is a cognitive bias describing the tendency of individuals to critically assess media reports in a domain they are knowledgeable about, yet continue to trust reporting in other areas despite recognizing similar potential inaccuracies.

The Feed Is Fake

That “viral” song, movie, influencer, and celebrity drama you scrolled by recently was likely the result of a stealth marketing campaign.

Blacksky - Apps on Google Play

Discover communities, trending conversations, and social media built for you.

Phantom Fluency

Why listening to smart people doesn't make you more thoughtful. You're not bad at remembering podcasts. Podcasts are bad at being remembered.

The Truth About Social Media as an "Advertising Industry," Bluesky's Gamble, and "Reclaiming Conversation" - Nightflight

News Influencers Fact Sheet

About one-in-five U.S. adults say they regularly get news from news influencers on social media, and this is especially common among younger adults.

Social Media Algorithms Distort Social Instincts and Fuel Misinformation - Neuroscience News

Social media algorithms, designed to boost user engagement for advertising revenue, amplify the biases inherent in human social learning processes, leading to misinformation and polarization.

Mouth Coding

Lately, I've been talking websites into existence. Not metaphorically, but actually sitting in important meetings with people — clients, collaborators, my wife, friends, neighbors — watching real websites materialize in front of us as we converse. I've been half-jokingly call it mouth coding, and

The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict Behavior

More and more machine learning is applied to human behavior. Increasingly these algorithms suffer from a hidden—but serious—problem. It arises because they often predict one thing while hoping for another. Take a recommender system: It predicts clicks but hopes to identify preferences. Or take an algorithm that automates a radiologist: It predicts in-the-moment diagnoses while hoping to identify their reflective judgments. Psychology shows us the gaps between the objectives of such prediction tasks and the goals we hope to achieve: People can click mindlessly; experts can get tired and make systematic errors. We argue such situations are ubiquitous and call them “inversion problems”: The real goal requires understanding a mental state that is not directly measured in behavioral data but must instead be inverted from the behavior. Identifying and solving these problems require new tools that draw on both behavioral and computational science.

Cognitive Load and Social Media Advertising

Social media engagement requires cognitive resources, which subsequently impact the advertisements consumers see while browsing. For the most part, however, advertising practitioners and scholars s...