Hasil untuk "Advertising"

Chen Lou, Shupei Yuan

Abstract In the past few years, expenditure on influencer marketing has grown exponentially. The present study involves preliminary research to understand the mechanism by which influencer marketing affects consumers via social media. It proposes an integrated model—the social media influencer value model—to account for the roles of advertising value and source credibility. To test this model, we administered an online survey among social media users who followed at least one influencer. Partial least squares (PLS) path modeling results show that the informative value of influencer-generated content, influencer’s trustworthiness, attractiveness, and similarity to the followers positively affect followers’ trust in influencers’ branded posts, which subsequently influence brand awareness and purchase intentions. Theoretical and practical implications are discussed.

John R. Searle

Marian S. Friestad, Peter Wright

Abagail McWilliams, D. Siegel

Jialu Gao, Mithun Das Gupta, Qun Li et al.

Lifestyle images are photographs that capture environments and objects in everyday settings. In furniture product marketing, advertisers often create lifestyle images containing products to resonate with potential buyers, allowing buyers to visualize how the products fit into their daily lives. While recent advances in Generative Artificial Intelligence (GenAI) have given rise to realistic image content creation, their application in e-commerce advertising is challenging because high-quality ads must authentically representing the products in realistic scearios. Therefore, manual intervention is usually required for individual generations, making it difficult to scale to larger product catalogs. To understand the challenges faced by advertisers using GenAI to create lifestyle images at scale, we conducted evaluations on ad images generated using state-of-the-art image generation models and identified the major challenges. Based on our findings, we present CreativeAds, a multi-product ad creation system that supports scalable automated generation with customized parameter adjustment for individual generation. To ensure automated high-quality ad generation, CreativeAds innovates a pipeline that consists of three modules to address challenges in product pairing, layout generation, and background generation separately. Furthermore, CreativeAds contains an intuitive user interface to allow users to oversee generation at scale, and it also supports detailed controls on individual generation for user customized adjustments. We performed a user study on CreativeAds and extensive evaluations of the generated images, demonstrating CreativeAds's ability to create large number of high-quality images at scale for advertisers without requiring expertise in GenAI tools.

V. V. Ivashkin

INTRODUCTION. The modern advertising discourse of social networks is developing in conditions of information saturation, media stress and a decrease in the criticality of audience perception. The growth of advertising budgets in the digital environment does not always correlate with the effectiveness of communication, which actualizes the appeal to socio-psychological mechanisms of influence on mass consciousness. The classical theories of mass psychology described by G. Lebon and S. Moscovici retain methodological significance and are actively being adapted to the advertising strategies of digital platforms. The purpose of the study is to identify and analyze the communicative strategies of suggestive influence on the mass audience in the advertising texts of the social networks Telegram and VKontakte based on psychology concepts of the mass audience described in the works of G. Lebon and S. Moscovici. MATERIALS AND METHODS. The empirical basis is made up of advertising publications in Telegram channels and VKontakte communities (October 2023 – December 2025). Methods of observation, comparative analysis, and philological analysis are used. RESULTS AND DISCUSSION. It has been established that advertising discourse uses techniques of simplifying meanings, emotionalization, appealing to authority, exploiting dissatisfaction and constructing an image of an ideal future. The specifics of the platforms determine the forms of implementation of these strategies and the degree of their impact. CONCLUSION. Social networks form a special communicative environment in which the classical mechanisms of mass psychology are used in native advertising, enhancing the invasive effect, which requires a rethink of ethical and regulatory approaches to advertising communication.

David Mick, C. Buhl

Jun Zhang, Yi Li, Yue Liu et al.

As an intelligent infrastructure connecting users with commercial content, advertising recommendation systems play a central role in information flow and value creation within the digital economy. However, existing multi-stage advertising recommendation systems suffer from objective misalignment and error propagation, making it difficult to achieve global optimality, while unified generative recommendation models still struggle to meet the demands of practical industrial applications. To address these issues, we propose GPR (Generative Pre-trained Recommender), the first one-model framework that redefines advertising recommendation as an end-to-end generative task, replacing the traditional cascading paradigm with a unified generative approach. To realize GPR, we introduce three key innovations spanning unified representation, network architecture, and training strategy. First, we design a unified input schema and tokenization method tailored to advertising scenarios, mapping both ads and organic content into a shared multi-level semantic ID space, thereby enhancing semantic alignment and modeling consistency across heterogeneous data. Second, we develop the Heterogeneous Hierarchical Decoder (HHD), a dual-decoder architecture that decouples user intent modeling from ad generation, achieving a balance between training efficiency and inference flexibility while maintaining strong modeling capacity. Finally, we propose a multi-stage joint training strategy that integrates Multi-Token Prediction (MTP), Value-Aware Fine-Tuning and the Hierarchy Enhanced Policy Optimization (HEPO) algorithm, forming a complete generative recommendation pipeline that unifies interest modeling, value alignment, and policy optimization. GPR has been fully deployed in the Tencent Weixin Channels advertising system, delivering significant improvements in key business metrics including GMV and CTCVR.

Klajdi Hoxha

E-commerce platforms are rolling out ambitious targeted advertising initiatives that rely on merchants sharing customer data with each other via the platform. Yet current platform designs fail to address participating merchants' concerns about customer poaching. This paper proposes a model of designing targeted advertising platforms that incentivizes merchants to voluntarily share customer data despite poaching concerns. I characterize the optimal mechanism that maximizes a weighted sum of platform's revenues, customer engagement and merchants' surplus. In sufficiently large platforms, the optimal mechanism can be implemented through the design of three markets: $i)$ selling market, where merchants can sell all their data at a posted price $p$, $ii)$ exchange market, where merchants share all their data in exchange for high click-through rate (CTR) ads, and $iii)$ buying market, where high-value merchants buy high CTR ads at the full price. The model is broad in scope with applications in other market design settings like the greenhouse gas credit markets and reallocating public resources, and points toward new directions in combinatorial market exchange designs.

Stefano Civelli, Pietro Bernardelle, Frank Mols et al.

The spread of targeted advertising on social media platforms has revolutionized political marketing strategies. Monitoring these digital campaigns is essential for maintaining transparency and accountability in democratic processes. Leveraging Meta's Ad Library, we analyze political advertising on Facebook and Instagram during the 2022 Australian federal election campaign. We investigate temporal, demographic, and geographical patterns in the advertising strategies of major Australian political actors to establish an empirical evidence base, and interpret these findings through the lens of Social Identity Theory (SIT). Our findings not only reveal significant disparities in spending and reach among parties, but also in persuasion strategies being deployed in targeted online campaigns. We observe a marked increase in advertising activity as the election approached, peaking just before the mandated media blackout period. Demographic analysis shows distinct targeting strategies, with parties focusing more on younger demographics and exhibiting gender-based differences in ad impressions. Regional distribution of ads largely mirrored population densities, with some parties employing more targeted approaches in specific states. Moreover, we found that parties emphasized different themes aligned with their ideologies-major parties focused on party names and opponents, while smaller parties emphasized issue-specific messages. Drawing on SIT, we interpret these findings within Australia's compulsory voting context, suggesting that parties employed distinct persuasion strategies. With turnout guaranteed, major parties focused on reinforcing partisan identities to prevent voter defection, while smaller parties cultivated issue-based identities to capture the support of disaffected voters who are obligated to participate.

Hou-Wan Long, Yicheng Song, Zidong Wang et al.

Sponsored search advertising (SSA) requires advertisers to constantly adjust keyword strategies. While bid adjustment and keyword generation are well-studied, keyword pruning-refining keyword sets to enhance campaign performance-remains under-explored. This paper addresses critical inefficiencies in current practices as evidenced by a dataset containing 0.5 million SSA records from a pharmaceutical advertiser on search engine Meituan, China's largest delivery platform. We propose KP-Agent, an LLM agentic system with domain tool set and a memory module. By modeling keyword pruning within a contextual bandit framework, KP-Agent generates code snippets to refine keyword sets through reinforcement learning. Experiments show KP-Agent improves cumulative profit by up to 49.28% over baselines.

Rianne Riemens

This article provides an analysis of “tech-on-climate discourse,” understood as all textual and visual materials through which major North American tech companies position themselves in relation to the climate crisis, published between 2019 and 2024. This public-facing tech-on-climate discourse includes sustainability reports, web pages, advertising, and online tools and projects. The article examines this discourse by focusing on three case studies: Amazon's Climate Pledge , Apple's Carbon Neutral campaign, and Microsoft's AI for Good and Planetary Computer programs. The cases are all designed in relation to the companies’ mission to become “carbon neutral,” each taking a different approach. Through a close analysis of these campaigns, I discuss four legitimization strategies that allow for the justification of what I call “green platform capitalism.” These strategies are expressions of an ecomodernist worldview in which economic growth is decoupled from ecological well-being and in which innovative techno-fixes make Silicon Valley a central environmental agent. This worldview (1) frames Earth as a datafied planet, (2) naturalizes digital technologies, and (3) imagines a singular “humanity” modelled after the white, male entrepreneur. In sum, tech-on-climate discourse forwards the story that with the right technological innovations and corporate interventions, Earth and life on it can be saved from the climate crisis without making changes to the operations of Big Tech beyond their own proposals, or societies at large. I position this techno-optimistic narrative within ongoing “tech-for-good” and “green AI” debates, of which the orientations are reconfigured vis-à-vis ongoing political changes in the US, and elsewhere.

А.О. Сюрикпаева

With modern development, digitalization in many countries is becoming a strategic priority area of economic development. The digital economy is an economic activity carried out through the internet, mobile communications, digital technologies, that is, virtual reality, in economic relations on production, distribution, exchange and consumption. The author gives a number of author's recommendations in the article for the systematic formation of the structure of an online store, e-commerce to increase the competitiveness of e-commerce, indicating that in addition to effective opportunities in the development of e-commerce, there are risks that threaten. Among them are the creation of a national research organization to improve the conditions for the development of digital business, the provision of land by the state for the construction of warehouses in the regions in the development of digital commerce, as well as the improvement of logistics networks, the use of new innovative models in the technological modernization of certain sectors of the economy, the introduction of new types of business plans, the approval of modern structures of public service. Given the dynamic development of digital business, in addition to traditional trade, IT specialists are in high demand in accordance with the requirements of the time.  At the same time, the author emphasizes the need to take state measures to train specialists in the field of digital commerce, including the organization of special courses or programs for rural residents in the regions. In order to strengthen the protection of consumer rights, the new version of the law «Оn protection of Consumer Rights» provides for additional regulatory measures. As we all know, such refined concepts as «electronic sales platform (marketplace)», «online store» and «information and advertising platform» have been introduced into the law. At the same time, the author proposes to introduce the term «e-commerce» and give it a scientifically consistent definition. According to the author, e-commerce contributes to the settlement of issues of consumer violations that occur when ordering services and works with a mobile phone. The article also proposes to ensure the mandatory protection of personal data about consumers and supplement the inadmissibility of claims by entrepreneurs that violate and (or) oppress the rights and legitimate interests of consumers. Keywords: digital economy, e-commerce, consumer, consumer rights, seller, buyer, digital environment, online store.

Izvolov Nikolai A.

The article suggests that literature about cinema depends very much on cinema, and not only on its repertoire, but also on more significant, deeper issues. This idea is considered by the example of various types of books and magazines published in our country in different historical periods: the pre-revolutionary period, the Soviet period, and the post-Soviet period, which continues to this day. The first part of the article discusses the pre-revolutionary period of the Russian film press, the first examples of theoretical thought — the articles by Maxim Gorky and the books by Boleslav Matushevsky, and the appearance of the first specialized film magazines. The main part is devoted to the period of Soviet cinema, which was marked by the greatest number of technological and aesthetic achievements of Russian cinema, the appearance of works of cinematographic art and various types of printed products on cinema. The final part traces the modification of cinematographic publications in the post-Soviet period and their gradual integration into the technologies of the digital period.

Zhenxiang He, Ke Chen, Zhenyu Zhao

Recommender systems are widely used in e-commerce, news, and advertising, providing personalized recommendations by analyzing user interaction history. However, during large-scale data analysis and sharing, user privacy faces the risk of exposure, especially for users who wish to remain anonymous. While existing synthetic data methods perform well in privacy protection, there is still room for improvement in balancing personalized privacy protection and data utility, particularly in sparse data environments. To address this issue, this paper proposes a user-privacy-controllable data augmentation model that generates synthetic substitutes to replace original data, ensuring anonymity. The model mitigates the data sparsity problem by introducing a global average user-item vector and an attention mechanism. The global vector captures the overall user interest trend, reducing noise and outliers, which allows for a more accurate representation of user interests and a precise selection of items to replace, even in sparse data environments. Additionally, the model dynamically generates synthetic substitutes that align with user interests based on privacy preferences, prioritizing replacing low-interest items, thus achieving a good balance between privacy protection and data utility. Experimental results on three public datasets show that the model effectively protects privacy while maintaining high data utility, outperforming existing mainstream methods in personalized privacy protection.

Fengxin Li, Yi Li, Yue Liu et al.

Display advertising provides significant value to advertisers, publishers, and users. Traditional display advertising systems utilize a multi-stage architecture consisting of retrieval, coarse ranking, and final ranking. However, conventional retrieval methods rely on ID-based learning to rank mechanisms and fail to adequately utilize the content information of ads, which hampers their ability to provide diverse recommendation lists. To address this limitation, we propose leveraging the extensive world knowledge of LLMs. However, three key challenges arise when attempting to maximize the effectiveness of LLMs: "How to capture user interests", "How to bridge the knowledge gap between LLMs and advertising system", and "How to efficiently deploy LLMs". To overcome these challenges, we introduce a novel LLM-based framework called LLM Empowered Display ADvertisement REcommender system (LEADRE). LEADRE consists of three core modules: (1) The Intent-Aware Prompt Engineering introduces multi-faceted knowledge and designs intent-aware <Prompt, Response> pairs that fine-tune LLMs to generate ads tailored to users' personal interests. (2) The Advertising-Specific Knowledge Alignment incorporates auxiliary fine-tuning tasks and Direct Preference Optimization (DPO) to align LLMs with ad semantic and business value. (3) The Efficient System Deployment deploys LEADRE in an online environment by integrating both latency-tolerant and latency-sensitive service. Extensive offline experiments demonstrate the effectiveness of LEADRE and validate the contributions of individual modules. Online A/B test shows that LEADRE leads to a 1.57% and 1.17% GMV lift for serviced users on WeChat Channels and Moments separately. LEADRE has been deployed on both platforms, serving tens of billions of requests each day.

Xiaoqing Zhang, Xiuying Chen, Yuhan Liu et al.

In the digital world, influencers are pivotal as opinion leaders, shaping the views and choices of their influencees. Modern advertising often follows this trend, where marketers choose appropriate influencers for product endorsements, based on thorough market analysis. Previous studies on influencer selection have typically relied on numerical representations of individual opinions and interactions, a method that simplifies the intricacies of social dynamics. In this work, we first introduce a Time-aware Influencer Simulator (TIS), helping promoters identify and select the right influencers to market their products, based on LLM simulation. To validate our approach, we conduct experiments on the public advertising campaign dataset SAGraph which encompasses social relationships, posts, and user interactions. The results show that our method outperforms traditional numerical feature-based approaches and methods using limited LLM agents. Our research shows that simulating user timelines and content lifecycles over time simplifies scaling, allowing for large-scale agent simulations in social networks. Additionally, LLM-based agents for social recommendations and advertising offer substantial benefits for decision-making in promotional campaigns.

Francisca Pulido Valente, Ana Carolina Baptista, Bárbara S. Vieira

Introduction: In Portugal, the alcohol industry (AI) engages in corporate political activity, influencing decision-making and potentially obstructing the implementation of evidence-based alcohol policies, such as higher taxes, stricter advertising laws, and mandatory warning labels on all alcoholic beverages. Yet, no research has been conducted specifically on the industry’s corporate social responsibility activities (CSR), which can also hinder the implementation of these cost-effective policies while serving as a means for nurturing its reputation. Methods: This study addresses this gap by examining the CSR practices of ten AI actors in Portugal. Through qualitative content analysis, data collected from their websites was categorized based on an adapted framework considering seven CSR categories. Results: The results revealed CSR practices across all categories, with corporate philanthropy and alcohol information provision and education accounting for 65% of the analyzed data. The findings highlight potential conflicts between the public health initiatives of AI in Portugal and its profit-driven objectives. Conclusion: CSR activities undermine health protection and alcohol harm prevention efforts by promoting a dubious discourse about an undeniably harmful substance. The present study recommends considering vigilant monitoring, regulation, or even the ban on CSR practices of AI in Portugal.

Khuman Bhagirath Jetubhai

In the late 2000s, LGBTQ+ (lesbian, gay, bisexual, transgender, queer, and others) identities began to surface in Indian audiovisual marketing campaigns. By 2021, brands had produced at least 105 advertisements featuring LGBTQ+ identities. This research conducts an analysis of these campaigns, examining aspects such as their release date, featured characters, gender identity, sexual orientation, character age, narrative usage, genre, theme, and setting. The study reveals a substantial surge in advertising campaigns since 2018, with brands strategically launching them during Pride Month, Valentine's Day, and Women's Day. Characters embodying gay, lesbian, and transgender identities were most prevalent. Advertisements spotlighting gay, lesbian, or both gay and lesbian characters predominantly emphasised themes of love, featuring youthful characters and urban settings. In contrast, advertisements featuring transgender characters centred on human rights, with older characters and non-urban settings. Based on these findings, it is recommended that the number of LGBTQ+ marketing campaigns should continue to rise, diversifying character identities, ages, and settings while being released throughout the year.

T. P. Romanova

The article is devoted to the study of the history of the formation of Russian proper names of aviation and armored military equipment. Our research has shown that the main function of Russian armonyms – the proper names of Russian weapons – is the representative identification of types and brands of weapons produced at state-owned enterprises or unique mechanisms. Armonyms as verbal identifiers of complex military-technical mechanisms function in professional or publicistic discourse. The structure of the armonymic field includes verbal index signs marking types and series of weapons that correspond to the term pragmatonyms (T-90M «Breakthrough»), and proper names of unique weapons objects - chrematonyms (Tu–160 «Ilya Muromets»). The nominations of individual combat units can form armonymic complexes, for example, one of the Tu-160 strategic bombers of the White Swan series has an individual name «Nikolai Kuznetsov». In the process of the official naming of Russian high-tech combat units, the conventionally symbolic and commemorative principles of nomination traditionally occupy a central place. The composition of the armonym is dominated by the conditionally symbolic type of names, as a rule, not motivated by the properties of the named object (UAV «Geranium»), although some names retain an associative connection with the objects of the nomination (UAV «Lancet» – a high-precision kamikaze drone); the memorative type of names is characteristic of individual names of military vehicles (Tu-160 «Igor Sikorsky»). Official nominations occupy the main place in the Russian field of armonymy; however, some of the names may have an unofficial status, for example, the nicknames of the T-80BV tank Alyosha – «Ded Moroz», or «Ded». Armonyms are not a tool for the advertising promotion of weapons, they do not have an attractive and pragmatic function, however, the choice of words is far from accidental – in general, it reflects the symbolic value orientations of the nominating team.