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on Cultural Economics |
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Issue of 2023‒09‒25
two papers chosen by Roberto Zanola, Università degli Studi del Piemonte Orientale |
| By: | Gambato, Jacopo; Sandrini, Luca |
| Abstract: | We study the incentives of a streaming platform to bias consumption when products are vertically differentiated. The platform offers mixed bundles of content to monetize consumers' interest in variety and pays royalties to sellers based on the effective consumption of the content they produce. When products are not vertically differentiated, the platform has no incentive to bias consumption in equilibrium: the platform being active represents a Pareto-improvement compared to the case in which she is not. With vertical differentiation, royalties can differ; the platform always biases recommendations in favor of the cheapest content, which hurts consumers and the high-quality seller. Biased recommendation always diminishes the incentives of a seller to increase the quality of her content for a given demand. If a significant share of the users is ex-ante unaware of the existence of the sellers the platform can bias recommendations more freely, but joining the platform encourages investment in quality. The bias, however, can lead to inefficient allocation of R&D efforts. From a policy perspective, we propose this as a novel rationale for regulating algorithmic recommendations in streaming platforms. |
| Keywords: | platform economics, media economics, recommendation bias, innovation |
| JEL: | D4 L1 L5 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:23032&r=cul |
| By: | Melissa Dell; Jacob Carlson; Tom Bryan; Emily Silcock; Abhishek Arora; Zejiang Shen; Luca D'Amico-Wong; Quan Le; Pablo Querubin; Leander Heldring |
| Abstract: | Existing full text datasets of U.S. public domain newspapers do not recognize the often complex layouts of newspaper scans, and as a result the digitized content scrambles texts from articles, headlines, captions, advertisements, and other layout regions. OCR quality can also be low. This study develops a novel, deep learning pipeline for extracting full article texts from newspaper images and applies it to the nearly 20 million scans in Library of Congress's public domain Chronicling America collection. The pipeline includes layout detection, legibility classification, custom OCR, and association of article texts spanning multiple bounding boxes. To achieve high scalability, it is built with efficient architectures designed for mobile phones. The resulting American Stories dataset provides high quality data that could be used for pre-training a large language model to achieve better understanding of historical English and historical world knowledge. The dataset could also be added to the external database of a retrieval-augmented language model to make historical information - ranging from interpretations of political events to minutiae about the lives of people's ancestors - more widely accessible. Furthermore, structured article texts facilitate using transformer-based methods for popular social science applications like topic classification, detection of reproduced content, and news story clustering. Finally, American Stories provides a massive silver quality dataset for innovating multimodal layout analysis models and other multimodal applications. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.12477&r=cul |