skip to main content


Title: New Identities for a ‘New Middle Class’: Media Incitements to Class Subjectivity in Brazil, 2008-2012
Our study examines media representations of the so--called “new middle class” during the period 2008-2012, during which the public sphere overflowed with images of new lifestyles and futures as “previously poor” Brazi-lians were invited in national advertising campaigns and in mainstream journalistic accounts to view themselves as members of an ostensibly new demographic sector. Meanwhile, through television, films, and music, Brazi-lians were exposed to stories of socio-economic mobili-ty, usually tied to love, sex, and consumption. Through a detailed review of existing studies of representations in media and advertising campaigns, we reflect on recurring representational patterns, arguing their importance in the construction of new class subjectivities for popular-class Brazilians. Our article seeks to capture the intense discur-sive formations that flourished over a relatively short pe-riod of political and economic stability.  more » « less
Award ID(s):
1534655
NSF-PAR ID:
10181549
Author(s) / Creator(s):
Date Published:
Journal Name:
Eikon
Volume:
1
Issue:
7
ISSN:
2183-6426
Page Range / eLocation ID:
63-76
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Political campaigns are increasingly turning to targeted advertising platforms to inform and mobilize potential voters. The appeal of these platforms stems from their promise to empower advertisers to select (or "target") users who see their messages with great precision, including through inferences about those users' interests and political affiliations. However, prior work has shown that the targeting may not work as intended, as platforms' ad delivery algorithms play a crucial role in selecting which subgroups of the targeted users see the ads. In particular, the platforms can selectively deliver ads to subgroups within the target audiences selected by advertisers in ways that can lead to demographic skews along race and gender lines, and do so without the advertiser's knowledge. In this work we demonstrate that ad delivery algorithms used by Facebook, the most advanced targeted advertising platform, shape the political ad delivery in ways that may not be beneficial to the political campaigns and to societal discourse. In particular, the ad delivery algorithms lead to political messages on Facebook being shown predominantly to people who Facebook thinks already agree with the ad campaign's message even if the political advertiser targets an ideologically diverse audience. Furthermore, an advertiser determined to reach ideologically non-aligned users is non-transparently charged a high premium compared to their more aligned competitor, a difference from traditional broadcast media. Our results demonstrate that Facebook exercises control over who sees which political messages beyond the control of those who pay for them or those who are exposed to them. Taken together, our findings suggest that the political discourse's increased reliance on profit-optimized, non-transparent algorithmic systems comes at a cost of diversity of political views that voters are exposed to. Thus, the work raises important questions of fairness and accountability desiderata for ad delivery algorithms applied to political ads. 
    more » « less
  2. null (Ed.)
    Online advertising, as a vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps. The prosperity of online campaigns is a challenge in online marketing and is usually evaluated by user response through different metrics, such as clicks on advertisement (ad) creatives, subscriptions to products, purchases of items, or explicit user feedback through online surveys. Recent years have witnessed a significant increase in the number of studies using computational approaches, including machine learning methods, for user response prediction. However, existing literature mainly focuses on algorithmic-driven designs to solve specific challenges, and no comprehensive review exists to answer many important questions. What are the parties involved in the online digital advertising eco-systems? What type of data are available for user response prediction? How do we predict user response in a reliable and/or transparent way? In this survey, we provide a comprehensive review of user response prediction in online advertising and related recommender applications. Our essential goal is to provide a thorough understanding of online advertising platforms, stakeholders, data availability, and typical ways of user response prediction. We propose a taxonomy to categorize state-of-the-art user response prediction methods, primarily focusing on the current progress of machine learning methods used in different online platforms. In addition, we also review applications of user response prediction, benchmark datasets, and open source codes in the field. 
    more » « less
  3. Waldemar Karwowski (Ed.)

    Online advertising is a billion-dollar industry, with many companies choosing online websites and various social media platforms to promote their products. The primary concerns in online marketing are to optimize the performance of a digital advert, reach the right audience, and maximize revenue, which can be achieved by predicting the accurate probability of a given ad being clicked, called the Click-Through Rate. It is assumed that a high CTR depicts the ad reaching its target customers while a low CTR shows that it is not reaching its desired audience, which may constitute a low return on investment (ROI). We propose a data-science-driven approach to help businesses improve their internet advertising campaigns which involves building various machine learning models to accurately predict the CTR and selecting the best-performing model. To build our classification models, we use the Avazu dataset, publicly available on the Kaggle website. Having insights on this metric will allow companies to compete in real-time bidding, gauge how relevant their keywords are in search engine querying, and mitigate an unexpected loss in spending budget. The authors in this paper strive to use modern machine learning tools and techniques to improve the performance of predicting Click-Through Rate (CTR) in online advertisements and bring change to the industry.

     
    more » « less
  4. Abstract Video advertisements, either through television or the Internet, play an essential role in modern political campaigns. For over two decades, researchers have studied television video ads by analyzing the hand-coded data from the Wisconsin Advertising Project and its successor, the Wesleyan Media Project (WMP). Unfortunately, manually coding more than a hundred of variables, such as issue mentions, opponent appearance, and negativity, for many videos is a laborious and expensive process. We propose to automatically code campaign advertisement videos. Applying state-of-the-art machine learning methods, we extract various audio and image features from each video file. We show that our machine coding is comparable to human coding for many variables of the WMP datasets. Since many candidates make their advertisement videos available on the Internet, automated coding can dramatically improve the efficiency and scope of campaign advertisement research. Open-source software package is available for implementing the proposed methodology. 
    more » « less
  5. Recent years have witnessed the rapid progress in deep learning (DL), which also brings their potential weaknesses to the spotlights of security and machine learning studies. With important discoveries made by adversarial learning research, surprisingly little attention, however, has been paid to the realworld adversarial techniques deployed by the cybercriminal to evade image-based detection. Unlike the adversarial examples that induce misclassification using nearly imperceivable perturbation, real-world adversarial images tend to be less optimal yet equally e ective. As a first step to understand the threat, we report in the paper a study on adversarial promotional porn images (APPIs) that are extensively used in underground advertising. We show that the adversary today’s strategically constructs the APPIs to evade explicit content detection while still preserving their sexual appeal, even though the distortions and noise introduced are clearly observable to humans. To understand such real-world adversarial images and the underground business behind them, we develop a novel DL-based methodology called Mal`ena, which focuses on the regions of an image where sexual content is least obfuscated and therefore visible to the target audience of a promotion. Using this technique, we have discovered over 4,000 APPIs from 4,042,690 images crawled from popular social media, and further brought to light the unique techniques they use to evade popular explicit content detectors (e.g., Google Cloud Vision API, Yahoo Open NSFW model), and the reason that these techniques work. Also studied are the ecosystem of such illicit promotions, including the obfuscated contacts advertised through those images, compromised accounts used to disseminate them, and large APPI campaigns involving thousands of images. Another interesting finding is the apparent attempt made by cybercriminals to steal others’ images for their advertising. The study highlights the importance of the research on real-world adversarial learning and makes the first step towards mitigating the threats it poses. 
    more » « less