Working Papers

How Does the Adoption of Ad Blockers Affect News Consumption? (with Klaus M. Miller & Bernd Skiera)

  • R&R, Journal of Marketing Research

Abstract: Ad blockers allow users to browse websites without viewing ads. Online news providers that rely on advertising revenue tend to perceive users’ adoption of ad blockers purely as a hindrance. Yet, this perception ignores the possibility that avoidance of ads—which users dislike—may affect users’ consumption behavior in positive ways. Indeed, remarkably little is known about how ad blockers impact news consumption. Using 3.1 million anonymized visits from 79,856 users on a news website, we find that the adoption of an ad blocker has a robust positive effect on the quantity and variety of articles that users consume (15.2%-32.0% more articles and 10.7%-20.7% more content categories post-adoption). The increase in news consumption is especially strong for light users, and it is primarily driven by an increase in the number of repeat visits of a user to the news website, rather than by the number of page impressions per visit. These visits tend to start with a direct navigation to the news website, an indication of user loyalty. We discuss how news publishers could benefit from these findings, including exploration of revenue models that leverage users’ desire to avoid ads and their enhanced engagement.

Paywall and Content Polarization

  • Presented at Statistical Challenges in Electronic Commerce Research (SCECR) 2020, Workshop on Information System and Economics (WISE) 2020

Abstract: This paper studies how financing content by subscription through a paywall impacts the content polarization of the news media. Analyzing news articles from the 4 largest US news media for 3 years based on a novel polarization index trained from the US congress speech, this paper shows that the New York Times polarized itself on the political dimension by 20% more after adopting a paywall (i.e., NYT leaning to the political left by 20% more). As the mechanism, this paper finds that the newsroom gets more polarized after the paywall adoption: journalists who wrote more left-leaning news articles are more likely to get new byline assignment and are quicker to get promotion after paywall. I discuss the policy implication of these findings for the media market.

Information-Seeking Argument Mining: A Step Towards Identifying Reasons in Textual Analysis to Improve Services (with Bernd Skiera, Johannes Daxenberger, Marcus Dombois, and Iryna Gurevych)

Service providers increasingly use textual analysis such as sentiment mining or topic models on unstructured data. Still, those techniques fall short when providing linguistic relations such as reasons behind changes in sentiment or topics. Information-seeking argument mining (IS‑AM) is a text mining technique that automatically extracts and identifies the argumentative structures (e.g., reasoning) from natural language text. So far, however, service researchers and managers hardly use IS‑AM. This article outlines how to use IS‑AM to improve services. The empirical study applies IS‑AM to news articles about scooter-sharing systems, i.e., a service enabling the short-term rentals of electric motorized scooters. The results outline that (i) arguments differ strongly across time, providers of scooter-sharing systems, and media, (ii) knowledge of arguments enable to improve services and communications with customers, and (iii) results from sentiment analysis support the validity of IS‑AM. The article closes with an outlook for further research.