Green brands are increasingly leveraging electronic commerce and interactive marketing to enhance persuasive efforts in new media environments. However, empirical research on the role of linguistic rhetoric in these interactive settings remains limited. This study examines whether the use of homophonic puns in green brand advertising increases green purchase intention on digital platforms, and how visual salience moderates this effect. Across four online experiments (total N = 1382), using stimuli modeled on social media feeds and mobile commerce interfaces, we manipulated advertising copy to include either homophonic puns or non-pun alternatives and varied the level of interface salience. The results indicate that homophonic puns significantly increase green purchase intention, primarily through the mechanism of affiliative humor, which proved stronger than alternative humor pathways. Moreover, visual salience moderates this effect: high-salience layouts amplify humor perception and purchase intention, while low-salience conditions yield no significant effects. This study advances the field of interactive marketing by proposing a language–emotion–behavior framework and introducing a cross-modal synergy mechanism that links linguistic rhetoric with visual attention in digital media contexts. The findings provide both theoretical insights and practical implications for e-commerce platforms, particularly in optimizing advertising copy, attention design, and message placement strategies to align pun-based messages with high-salience executions, thereby enhancing green brand engagement.
Advertisers commonly need multiple versions of the same advertisement (ad) at varying durations for a single campaign. The traditional approach involves manually selecting and re-editing shots from longer video ads to create shorter versions, which is labor-intensive and time-consuming. In this paper, we introduce a framework for automated video ad clipping using video summarization techniques. We are the first to frame video clipping as a shot selection problem, tailored specifically for advertising. Unlike existing general video summarization methods that primarily focus on visual content, our approach emphasizes the critical role of audio in advertising. To achieve this, we develop a two-stream audio-visual fusion model that predicts the importance of video frames, where importance is defined as the likelihood of a frame being selected in the firm-produced short ad. To address the lack of ad-specific datasets, we present AdSum204, a novel dataset comprising 102 pairs of 30-second and 15-second ads from real advertising campaigns. Extensive experiments demonstrate that our model outperforms state-of-the-art methods across various metrics, including Average Precision, Area Under Curve, Spearman, and Kendall. The dataset and code are available at https://github.com/ostadabbas/AdSum204.
Christine Sowa Lepird, Lynnette Hui Xian Ng, Kathleen M. Carley
Pink slime journalism is a practice where news outlets publish low-quality or inflammatory partisan articles, claiming to be local news networks. This paper examines the spread of pink slime sites on Facebook using public posts from Pages and Groups. We evaluate the trends of sharing pink slime sites on Facebook and patterns regarding the advertisements purchased by the parent organizations of the pink slime news networks. Our analysis discovers that while the number of pink slime posts on Facebook pages have decreased over the years, advertising dollars have increased. The increase in advertising dollars influences an increase in Facebook group posts. Further, the advertising expenditure increases during election years, but contentious topics are still discussed during non-election years. By illustrating the patterns and themes from US election years of 2020, 2022, and 2024, this research offers insights into potentially dangerous journalism tactics, and provides predictions for future US Presidential Elections.
Social Media Advertisement has emerged as an effective approach for promoting the brands of a commercial house. Hence, many of them have started using this medium to maximize the influence among the users and create a customer base. In recent times, several companies have emerged as Influence Provider who provides views of advertisement content depending on the budget provided by the commercial house. In this process, the influence provider tries to exploit the information diffusion phenomenon of a social network, and a limited number of highly influential users are chosen and activated initially. Due to diffusion phenomenon, the hope is that the advertisement content will reach a large number of people. Now, consider that a group of advertisers is approaching an influence provider with their respective budget and influence demand. Now, for any advertiser, if the influence provider provides more or less influence, it will be a loss for the influence provider. It is an important problem from the point of view of influence provider, as it is important to allocate the seed nodes to the advertisers so that the loss is minimized. In this paper, we study this problem, which we formally referred to as Regret Minimization in Social Media Advertisement Problem. We propose a noble regret model that captures the aggregated loss encountered by the influence provider while allocating the seed nodes. We have shown that this problem is a computationally hard problem to solve. We have proposed three efficient heuristic solutions to solve our problem, analyzed to understand their time and space requirements. They have been implemented with real world social network datasets, and several experiments have been conducted and compared to many baseline methods.
Online advertising auctions are fundamental to internet commerce, demanding solutions that not only maximize revenue but also ensure incentive compatibility, high-quality user experience, and real-time efficiency. While recent learning-based auction frameworks have improved context modeling by capturing intra-list dependencies among ads, they remain limited in addressing global externalities and often suffer from inefficiencies caused by sequential processing. In this work, we introduce the Non-autoregressive Generative Auction with global externalities (NGA), a novel end-to-end framework designed for industrial online advertising. NGA explicitly models global externalities by jointly capturing the relationships among ads as well as the effects of adjacent organic content. To further enhance efficiency, NGA utilizes a non-autoregressive, constraint-based decoding strategy and a parallel multi-tower evaluator for unified list-wise reward and payment computation. Extensive offline experiments and large-scale online A/B testing on commercial advertising platforms demonstrate that NGA consistently outperforms existing methods in both effectiveness and efficiency.
AI systems have increasingly become our gateways to the Internet. We argue that just as advertising has driven the monetization of web search and social media, so too will commercial incentives shape the content served by AI. Unlike traditional media, however, the outputs of these systems are dynamic, personalized, and lack clear provenance -- raising concerns for transparency and regulation. In this paper, we envision how commercial content could be delivered through generative AI-based systems. Based on the requirements of key stakeholders -- advertisers, consumers, and platforms -- we propose design principles for commercially-influenced AI systems. We then outline high-level strategies for end users to identify and mitigate commercial biases from model outputs. Finally, we conclude with open questions and a call to action towards these goals.
Understanding multimodal video ads is crucial for improving query-ad matching and relevance ranking on short video platforms, enhancing advertising effectiveness and user experience. However, the effective utilization of multimodal information with high commercial value still largely constrained by reliance on highly compressed video embeddings-has long been inadequate. To address this, we propose SUMMA (the abbreviation of Summarizing MultiModal Ads), a multimodal model that automatically processes video ads into summaries highlighting the content of highest commercial value, thus improving their comprehension and ranking in Douyin search-advertising systems. SUMMA is developed via a two-stage training strategy-multimodal supervised fine-tuning followed by reinforcement learning with a mixed reward mechanism-on domain-specific data containing video frames and ASR/OCR transcripts, generating commercially valuable and explainable summaries. We integrate SUMMA-generated summaries into our production pipeline, directly enhancing the candidate retrieval and relevance ranking stages in real search-advertising systems. Both offline and online experiments show substantial improvements over baselines, with online results indicating a statistically significant 1.5% increase in advertising revenue. Our work establishes a novel paradigm for condensing multimodal information into representative texts, effectively aligning visual ad content with user query intent in retrieval and recommendation scenarios.
The study examines the latest communication technologies of the digital age, which are changing the ways and effectiveness of conveying information to the media audience. Scientific literature shows fragmentation in defining the boundaries, terminology, and classification of means of conveying information in the digital age, which necessitates a unified approach to analyzing the latest communication practices. The purpose of this article is to review existing research on contemporary technologies in mass communication, to classify the latest technologies for conveying information in the digital environment and to identify the key vectors of their application in the context of mass communication. The authors consider social media, streaming services, and the latest television, some advertising platforms, automatic translation tools, analytics, and crowdsourcing platforms as part of the latest communication technologies. The methodology is based on theoretical analysis, content analysis, typological, and comparative methods. The study proposes a typological structure of the contemporary communication technologies based on the following criteria: communication format, degree of automation, role of the audience, nature of personalization, and algorithmic control. The results of the study can be applied in future detailed studies on the development of media management, information policy, and digital security strategies. They also contribute to the formation of a scientific basis for further research in media communications in the context of the modern digital transformations.
S.M. Sayem, Zayad Islam, Afia Hossain Anushka
et al.
This study investigates the factors influencing Generation Z's green purchase decisions in Bangladesh, with a particular focus on the mediating role of green purchase attitude. It is among the first to focus exclusively on this demographic in an emerging market context. Drawing upon constructs such as green trust, subjective norms, hedonic value, environmental advertising, and environmental sustainability awareness, the research applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data collected from Generation Z respondents. The theoretical foundation integrates the Theory of Planned Behavior, Value–Belief–Norm framework, Stimulus–Organism–Response model, and Attitude–Behavior–Context theory. The results reveal that subjective norms, hedonic value, environmental advertising, and green purchase attitude significantly influence green purchase decisions, while green trust and environmental sustainability awareness do not exhibit a significant direct impact. Notably, green purchase attitude mediates only the relationship between hedonic value and green purchase decision. These findings offer valuable insights for marketers, policymakers, and sustainability advocates aiming to promote environmentally responsible consumption behaviors among young consumers in emerging markets, particularly by leveraging social influence, enhancing the experiential value of green products, and implementing trust-building mechanisms to overcome skepticism.
History of scholarship and learning. The humanities, Social sciences (General)
Elena Kokthi, Iliriana Miftari, Arbenita Hasani
et al.
Abstract Unhealthy diets, overweight, and obesity have become significant public health challenges, particularly among adolescents whose dietary patterns are shifting away from traditional Mediterranean diets toward increased fast-food consumption (FFC). Industrialisation, urbanisation, and globalisation have transformed food environments, making ultra-processed, energy-dense foods such as fast food more accessible and affordable than fresh, nutritious alternatives. A total of 296 adolescents aged 12–18 years participated in the study. Data were analysed using Partial Least Squares Structural Equation Modelling, integrating habitual automaticity, environmental cues, and contextual triggers alongside traditional TPB constructs. The results show that higher maternal education reduces the use of fast food as a meal replacement; however, it does not significantly impact overall consumption frequency, indicating that external social and environmental factors may outweigh parental influence. Moreover, FFC persists regardless of adolescents’ exposure to advertising or promotions, underscoring the habit-driven nature of this behaviour. These findings emphasise the need for habit-focused interventions rather than relying solely on traditional awareness campaigns to curb adolescent fast-food consumption. Given the influence of behavioural automaticity and contextual triggers, policymakers should prioritise systemic interventions that reshape food environments, ensuring that healthier, sustainable options become the default choice in schools, public spaces, and urban settings.
Rupa Peter, Veni Roshith, Sheethal Lawrence
et al.
IntroductionGen Z, a tech-savvy consumer group, has highly evolved in its approach to new-age advertising. The rise of Generative Artificial Intelligence (Gen AI) has revolutionized advertising by enabling hyper-personalized content, making it essential to understand its influence on Generation Z (Gen Z) population. This study explores the responses of Gen Z participants in India to Generative Artificial Intelligence based, hyper-personalized advertisements, with a specific focus on emotional responses and brand interactions which are significant predictors of advertisement success.MethodsUsing qualitative research methods, semi-structured interviews were conducted with 40 Gen Z participants. Thematic analysis of the data was performed to understand the major themes pertaining to emotional responses and brand interactions to this form of Gen AI-driven advertising.ResultsTwo major themes and five sub-themes were revealed through thematic analysis. The first theme, diverse emotional responses, encompassed two sub-themes, curiosity and interest as well as fear and suspicion. The second major theme, enhanced brand experience, encompassed three sub-themes of advanced targeted marketing; initial attraction and brand engagement; and brand connection and loyalty, as perceived by the participants.DiscussionFindings imply that brands can harness Gen AI-driven, hyper-personalized advertisements to evoke meaningful emotions, enhancing consumer loyalty and building stronger, more personal connections with their audience.
The representation of disabled people in TV series remains limited, and characterised by stereotypical portrayals. However, over the past decade VOD platforms have promoted a qualitative and quantitative increase in the production of series featuring such characters. This can be attributed to the enhanced visibility of mental health in contemporary society, as well as the obligation of television to serve as a reflection and socialisation instrument. This paper presents a review of the presence and representation of disability in television fiction series produced in the United States. The main objective of this research is to determine the mental health representation in the character of King George III in the TV series Queen Charlotte. A Bridgerton Story (Netflix, 2023). From a qualitative methodology, we review the related literature and conduct an in-depth textual analysis of the character as a person, role, and actant. Moreover, we examine the scenes which reveal the symptoms of the king’s mental health. The findings show the historical licences adopted by the fiction in favour of the narrative. The symptoms expressed by the king are indicative of a Bipolar I Disorder and Dissociative Identity Disorder: anxiety, verbiage, “races of thoughts,” and identity crisis. In conclusion, this representation stands out for a creative and effective purpose, rather than for being realistic. However, it contributes to the visibility of cognitive disability in contemporary audiovisual projects.
The market acts as the most important component of the institution of the modern world. The market means not only the relationship between consumers and sellers, but also the external environment in which they are carried out. In turn, such market relations are significantly influenced by the totality of human choices and decisions (both rational and irrational).To study the characteristics and degree of influence of various factors (emotional, social, etc.) on decision–making by economic entities, as well as their impact on the market as a whole, a new field of economics — behavioral economics — was institutionalized by economists. In this case, economic entities can be understood as groups of people, a single person, and corporations, enterprises, companies. Behavioral economics extends standard economic theory by taking into account the probability and impact of irrational human behavior on the market.Thanks to the development of behavioral economics, which is based on the achievements of economic theory, psychology, (more management psychology, economic psychology), sociology, philosophy (attitude to work, money, spending, educated family and cultivated society), marketing (advertising, marketing research) and management (management decision–making, alignment and adjustment strategies, etc.), within the framework of market assessment, more objective results can be obtained, which will contribute to the improvement of decisions made.