Analyzing Patterns and Influence of Advertising in Print Newspapers

• URL: http://arxiv.org/abs/2505.10791v1
• Date: Fri, 16 May 2025 02:05:53 GMT
• Title: Analyzing Patterns and Influence of Advertising in Print Newspapers
• Authors: N Harsha Vardhan, Ponnurangam Kumaraguru, Kiran Garimella,
• Abstract summary: This paper investigates advertising practices in print newspapers across India using a novel data-driven approach.<n>We develop a pipeline employing image processing and OCR techniques to extract articles and advertisements from digital versions of print newspapers with high accuracy.<n>Applying this methodology to five popular newspapers that span multiple regions and three languages, we assembled a dataset of more than 12,000 editions containing several hundred thousand advertisements.
• Score: 7.779477387565033
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper investigates advertising practices in print newspapers across India using a novel data-driven approach. We develop a pipeline employing image processing and OCR techniques to extract articles and advertisements from digital versions of print newspapers with high accuracy. Applying this methodology to five popular newspapers that span multiple regions and three languages, English, Hindi, and Telugu, we assembled a dataset of more than 12,000 editions containing several hundred thousand advertisements. Collectively, these newspapers reach a readership of over 100 million people. Using this extensive dataset, we conduct a comprehensive analysis to answer key questions about print advertising: who advertises, what they advertise, when they advertise, where they place their ads, and how they advertise. Our findings reveal significant patterns, including the consistent level of print advertising over the past six years despite declining print circulation, the overrepresentation of company ads on prominent pages, and the disproportionate revenue contributed by government ads. Furthermore, we examine whether advertising in a newspaper influences the coverage an advertiser receives. Through regression analyses on coverage volume and sentiment, we find strong evidence supporting this hypothesis for corporate advertisers. The results indicate a clear trend where increased advertising correlates with more favorable and extensive media coverage, a relationship that remains robust over time and across different levels of advertiser popularity.

Related papers

• Paid to Look Like Truth: The Prevalence and Dark Patterns of Advertorials in News Outlets [1.9827599081330742]
  We conduct the first large-scale and systematic study of advertorials.<n>We find that advertorials appear in 1 out of 3 news sites, including some of the most popular and credible outlets worldwide.<n>We highlight that legal disclaimers intended to inform users of the promotional nature of the content, are often deliberately obscured or difficult to recognize.
  arXiv  Detail & Related papers  (2026-02-13T10:43:50Z)
• A Multimodal Analysis of Influencer Content on Twitter [40.41635575764701]
  Line between personal opinions and commercial content promotion is frequently blurred. This makes automatic detection of regulatory compliance breaches related to influencer advertising difficult. We introduce a new Twitter (now X) dataset consisting of 15,998 influencer posts mapped into commercial and non-commercial categories.
  arXiv  Detail & Related papers  (2023-09-06T15:07:23Z)
• Long-Term Ad Memorability: Understanding & Generating Memorable Ads [54.23854539909078]
  Despite the importance of long-term memory in marketing and brand building, until now, there has been no large-scale study on the memorability of ads.<n>We release the first memorability dataset, LAMBDA, consisting of 1749 participants and 2205 ads covering 276 brands.<n>Running statistical tests over different participant subpopulations and ad types, we find many interesting insights into what makes an ad memorable, e.g., fast-moving ads are more memorable than those with slower scenes.<n>We present a scalable method to build a high-quality memorable ad generation model by leveraging automatically annotated data.
  arXiv  Detail & Related papers  (2023-09-01T10:27:04Z)
• Discrimination through Image Selection by Job Advertisers on Facebook [79.21648699199648]
  We propose and investigate the prevalence of a new means for discrimination in job advertising. It combines both targeting and delivery -- through the disproportionate representation or exclusion of people of certain demographics in job ad images. We use the Facebook Ad Library to demonstrate the prevalence of this practice.
  arXiv  Detail & Related papers  (2023-06-13T03:43:58Z)
• Problematic Advertising and its Disparate Exposure on Facebook [15.667983888666312]
  We study Facebook and investigate key gaps in our understanding of problematic online advertising. We find that older people and minority groups are especially likely to be shown such ads. Given that 22% of problematic ads had no specific targeting from advertisers, we infer that ad delivery algorithms played a significant role in the biased distribution of these ads.
  arXiv  Detail & Related papers  (2023-06-09T17:23:59Z)
• Persuasion Strategies in Advertisements [68.70313043201882]
  We introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. We then formulate the task of persuasion strategy prediction with multi-modal learning. We conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500 companies.
  arXiv  Detail & Related papers  (2022-08-20T07:33:13Z)
• Multilingual Disinformation Detection for Digital Advertising [0.9684919127633844]
  We make the first step towards quickly detecting and red-flaging websites that potentially manipulate the public with disinformation. We build a machine learning model based on multilingual text embeddings that first determines whether the page mentions a topic of interest, then estimates the likelihood of the content being malicious. Our system empowers internal teams to proactively blacklist unsafe content, thus protecting the reputation of the advertisement provider.
  arXiv  Detail & Related papers  (2022-07-04T10:29:20Z)
• Aspect-based Analysis of Advertising Appeals for Search Engine Advertising [37.85305426549587]
  We focus on exploring the effective A$3$ for different industries with the aim of assisting the ad creation process. Our experiments demonstrated that different industries have their own effective A$3$ and that the identification of the A$3$ contributes to the estimation of advertising performance.
  arXiv  Detail & Related papers  (2022-04-25T05:31:07Z)
• Distinguishing Commercial from Editorial Content in News [0.0]
  We aim to differentiate the two using a machine learning model, and a lexicon derived from it. This was accomplished by scraping 1.000 articles and 1.000 advertorials from four different Dutch news sources.
  arXiv  Detail & Related papers  (2021-11-06T16:45:48Z)
• Cross-Domain Learning for Classifying Propaganda in Online Contents [67.10699378370752]
  We present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches with a clear difference in their degrees of being propagandistic. Our experiments demonstrate the usefulness of this approach, and identify difficulties and limitations in various configurations of sources and targets for the transfer step.
  arXiv  Detail & Related papers  (2020-11-13T10:19:13Z)
• An Empirical Study of In-App Advertising Issues Based on Large Scale App Review Analysis [67.58267006314415]
  We present a large-scale analysis on ad-related user feedback from App Store and Google Play. From a statistical analysis of 36,309 ad-related reviews, we find that users care most about the number of unique ads and ad display frequency during usage. Some ad issue types are addressed more quickly by developers than other ad issues.
  arXiv  Detail & Related papers  (2020-08-22T05:38:24Z)
• Do Interruptions Pay Off? Effects of Interruptive Ads on Consumers Willingness to Pay [79.9312329825761]
  We present the results of a study designed to measure the impact of interruptive advertising on consumers willingness to pay for products bearing the advertiser's brand. Our results contribute to the research on the economic impact of advertising, and introduce a method of measuring actual (as opposed to self-reported) willingness to pay in experimental marketing research.
  arXiv  Detail & Related papers  (2020-05-14T09:26:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.