Third party advertising and tracking (A&T) are pervasive across the web, yet user exposure varies significantly with browser choice, browsing location, and hosting jurisdiction. We systematically study how these three factors shape tracking by conducting synchronized crawls of 743 popular websites from 8 geographic vantage points using 4 browsers and 2 consent states. Our analysis reveals that browser choice, user location, and hosting jurisdiction each shape tracking exposure in distinct ways. Privacy focused browsers block more third party trackers, reducing observed A&T domains by up to 30% in permissive regulatory environments, but offer smaller relative gains in stricter regions. User location influences the tracking volume, the prevalence of consent banners, and the extent of cross border tracking: GDPR regulated locations exhibit about 80% fewer third party A&T domains before consent and keep 89-91% of A&T requests within the EEA or adequacy countries. Hosting jurisdiction plays a smaller role; tracking exposure varies most strongly with inferred user location rather than where sites are hosted. These findings underscore both the power and limitations of user agency, informing the design of privacy tools, regulatory enforcement strategies, and future measurement methodologies.
The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and neglected to efficiently process large-scale urban datasets. To address these challenges, this study proposes two complementary optimization methods: an enhanced greedy algorithm based on geometric modeling and spatial acceleration techniques, and a reinforcement learning approach using Proximal Policy Optimization (PPO). The enhanced greedy algorithm incorporates population-weighted road coverage modeling, employs a geometric series to capture diminishing returns from overlapping coverage, and integrates spatial indexing and parallel computing to significantly improve scalability and solution quality in large urban networks. Meanwhile, the PPO-based method models billboard site selection as a sequential decision-making process in a dynamic environment, where agents adaptively learn optimal deployment strategies through reward signals, balancing coverage gains and redundancy penalties and effectively handling complex multi-step optimization tasks. Experiments conducted on Wuhan’s road network demonstrate that both methods effectively optimize population-weighted billboard coverage under budget constraints while enhancing spatial distribution balance. Quantitatively, the enhanced greedy algorithm improves coverage effectiveness by 18.6% compared to the baseline, while the PPO-based method further improves it by 4.3% with enhanced spatial equity. The proposed framework provides a robust and scalable decision-support tool for urban advertising infrastructure planning and resource allocation.
Background and study aim. The human factor and referee errors are an inherent part of football. The number of mistakes can be significantly reduced, but it cannot be completely eliminated. At the same time, we must understand the nature of the error, what exactly influences the occurrence of the mistake, which part of the error is a subjective factor, and which part is an objective factor that is less susceptible to correction and does not depend on the referee's practice, experience, and training. This study is based on the procedure and factors that influence decision-making in the context of Law 11 (Offside). Decisions regarding the "offside" position are made by assistant referees and are among the most complex and discussed topics among experts and fans. The goal of the study is to determine the factors influencing the assistant referee's decisions when determining "offside" in football.
Material and methods. The study involved 33 football assistant referees of different qualifications, including assistant referees from the Youth Football League of Ukraine, the Professional Football League of Ukraine, and the Ukrainian Premier League. The following research methods were used during the study: non-interventional observation, where regular observation was conducted from the side to study the decision-making process on the football field; video content analysis, where the technical aspects of referees' behavior were assessed; statistical methods to process the quantitative data obtained during experiments and observations; and the modeling method, which involved creating 3D computer models to analyze the impact of assistant referees' positions on the assessment of situations related to the interpretation of Law 11.
Results. The conducted research allowed for the identification factors that influence the decision-making process regarding the determination of the "offside" position.
Conclusions. Factors influencing the decision-making process regarding the determination of the "offside" position have been identified: AR's position relative to the offside line, focus shift to the player making the pass, level of concentration, movement of attacking and defending players, attacking players in offside position and possible movement of defenders, number of attacking and defending players, type of AR movement along the sideline, head, torso, and body movements, distance between attacking and defending players, fatigue, color of stands and advertising boards, style of the football field’s grass cutting.
Anter Venus, Drina Intyaswati, Fitria Ayuningtyas
et al.
Various methods are used to foster political participation among young people, ensuring an active role in democratic life. Social media is a potential medium for distributing messages to Generation Z, effectively reaching and engaging young people. Therefore, this study aimed to examine the role of political influencers and advertising on political participation, mediated by political interest and efficacy. A survey was further conducted on young Indonesians (Generation Z) with purposive sampling, and the questionnaire was distributed online through social media networks (N = 559). The analysis results with Structural Equation Modeling (SEM) showed that political interest mediated the influence of influencers and advertising on participation. Additionally, internal political efficacy mediated the effect of advertising on participation. Political interest further strengthened internal efficacy in mediating the impact of influencers and advertising on political participation. Implication: Influencers may help political socialization go more smoothly and effectively. The government should establish more stringent regulations and ethical guidelines to ensure influencers maintain an objective and professional approach.
In online advertising, uncertainty calibration aims to adjust a ranking model's probability predictions to better approximate the true likelihood of an event, e.g., a click or a conversion. However, existing calibration approaches may lack the ability to effectively model complex nonlinear relations, consider context features, and achieve balanced performance across different data subsets. To tackle these challenges, we introduce a novel model called Monotonic Calibration Networks, featuring three key designs: a monotonic calibration function (MCF), an order-preserving regularizer, and a field-balance regularizer. The nonlinear MCF is capable of naturally modeling and universally approximating the intricate relations between uncalibrated predictions and the posterior probabilities, thus being much more expressive than existing methods. MCF can also integrate context features using a flexible model architecture, thereby achieving context awareness. The order-preserving and field-balance regularizers promote the monotonic relationship between adjacent bins and the balanced calibration performance on data subsets, respectively. Experimental results on both public and industrial datasets demonstrate the superior performance of our method in generating well-calibrated probability predictions.
Billboard Advertisement has emerged as an effective out-of-home advertisement technique where the goal is to select a limited number of slots and play advertisement content over there with the hope that this will be observed by many people, and effectively, a significant number of them will be influenced towards the brand. Given a trajectory and a billboard database and a positive integer $k$, how can we select $k$ highly influential slots to maximize influence? In this paper, we study a variant of this problem where a commercial house wants to make a promotion of multiple products, and there is an influence demand for each product. We have studied two variants of the problem. In the first variant, our goal is to select $k$ slots such that the respective influence demand of each product is satisfied. In the other variant of the problem, we are given with $\ell$ integers $k_1,k_2, \ldots, k_{\ell}$, the goal here is to search for $\ell$ many set of slots $S_1, S_2, \ldots, S_{\ell}$ such that for all $i \in [\ell]$, $|S_{i}| \leq k_i$ and for all $i \neq j$, $S_i \cap S_j=\emptyset$ and the influence demand of each of the products gets satisfied. We model the first variant of the problem as a multi-submodular cover problem and the second variant as its generalization. For solving the first variant, we adopt the bi-criteria approximation algorithm, and for the other variant, we propose a sampling-based approximation algorithm. Extensive experiments with real-world trajectory and billboard datasets highlight the effectiveness and efficiency of the proposed solution approach.
The popularity of e-commerce has given rise to fake advertisements that can expose users to financial and data risks while damaging the reputation of these e-commerce platforms. For these reasons, detecting and removing such fake advertisements are important for the success of e-commerce websites. In this paper, we propose FADAML, a novel end-to-end machine learning system to detect and filter out fake online advertisements. Our system combines techniques in multimodal machine learning and automated machine learning to achieve a high detection rate. As a case study, we apply FADAML to detect fake advertisements on popular Vietnamese real estate websites. Our experiments show that we can achieve 91.5% detection accuracy, which significantly outperforms three different state-of-the-art fake news detection systems.
This paper examines how regulation can push an oligopoly from one pricing regime to another. It uses rich data from Chilean pharmacy chains to study a ban on comparative price advertising. Before the ban, ads created demand spillovers across products, making aggressive loss-leader pricing profitable. Once these spillovers were removed, selling below cost became unattractive for any firm, and prices quickly shifted to a coordinated, higher level. A structural demand model shows that the ban reduced both price elasticity and cross-product spillovers, and counterfactuals indicate that the loss of spillovers, rather than just lower elasticity, mainly explains the move to the new coordinated pricing regime. The results show how well intentioned regulation can unintentionally promote price coordination by weakening the mechanisms that support competitive outcomes.
This study explores advertising students’ recognition and comprehension of in-feed native advertising on Instagram, a leading platform for social media native advertising. Data were collected from 303 U.S. college advertising students. It examines the impact of two key aspects of Instagram in-feed native advertising—an additional label (“Shop Now”) and ad-context congruency—through both quantitative and qualitative data. Findings reveal a significant deficit in native ad recognition among advertising students. Qualitative analysis identified four themes constituting Instagram in-feed advertising for advertising students: professional photo quality, branded products, persuasive elements, and excessive hashtags. Quantitative results indicate that high ad-context congruency led to higher perceptions of nativeness and more positive emotions, but did not significantly influence ad attitudes or negative emotions. The study underscores practical and theoretical implications for advertising educators and scholars, emphasizing the need for enhanced digital advertising literacy and discussing broader implications for advertising pedagogy and practices.
The journal publication rate for oral abstracts presented at the American College of Foot and Ankle Surgeons (ACFAS) is currently the highest reported for any national foot and ankle society, to date (80.7%). Conversion rates for abstracts presented at national society meetings has long served as an indirect proxy for the quality of the content presented. However, it is unclear why some abstracts ultimately fail to achieve publication. The purpose of the present study was to assess the publication barriers of oral abstracts from ACFAS: 2015–2019. Unpublished oral abstracts were procured from a database. A questionnaire was distributed to presenting authors to determine the current status of the abstract, as well as the reasons for the failure to achieve journal publication. Of the 22 oral abstracts that failed to achieve publication, 10 questionnaires were completed. At the time of the survey, none of the abstracts had been submitted for publication. An insufficient amount of time (30%), responsibility tasked to a co-author (30%), low perceived likelihood of acceptance (30%), and low perceived priority (10%), were citied by authors. The present study broadens our understanding regarding the barriers to the conversion of oral abstracts from ACFAS. Projects led by trainees accounted for 2/3′s of the unpublished abstracts; in some cases, the same author accounted for more than 1 unpublished abstract over subsequent years. The onus is on directors to ensure their trainees receive adequate research mentorship and disseminate clinically meaningful findings presented at ACFAS to a broader audience by journal publication.
The purpose of this study was to determine the effect of brand awareness and perceptions of halal label on purchasing decisions for personal care products among students at the Syiah Kuala University (USK). The research was conducted in July - October 2022. This research used a quantitative descriptive method, using a purposive sampling technique. The number of samples was 100 student respondents at USK who had used Kahf's personal care products. The instrument used is a questionnaire that has been tested for validity and reliability. The analytical method used is the classical assumption test analysis and multiple linear regression analysis. Data processing was carried out using SPSS software program version 25. The research data was normally distributed, and there were no symptoms of multicollinearity and heteroscedasticity. The results showed that brand awareness and perceptions of halal label have a posive and significant effect on purchasing decisions for personal care products, either partially or both simultaneously. Regarding the brand awareness variable, the existence of the product is realized mainly from advertising, easy to find, and because of its variety. Meanwhile, in the halal label perceptions variable, consumers trust the product because of the halal label, the guarantee of halalness and the guarantee of being free from the use of haram ingredients. The results of the coefficient of determination test show that the decision to purchase personal care products can be explained by brand awareness and perceptions of halal labels by 53.3%, while the remaining 46.7% is explained by other variables not examined in this study.
Keywords: brand awareness, consumer perception, halal label, personal care, purchasing decisions
Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities nowadays. However, the majority of these job sites are limited to offering fundamental filters such as job titles, keywords, and compensation ranges. This often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of listings. Thus, we propose well-coordinated visualizations to provide job seekers with three levels of details of job information: a skill-job overview visualizes skill sets, employment posts as well as relationships between them with a hierarchical visualization design; a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users' swift comprehension of the pertinent skills necessitated by respective positions; a post detail view lists the specifics of selected job posts for profound analysis and comparison. By using a real-world recruitment advertisement dataset collected from 51Job, one of the largest job websites in China, we conducted two case studies and user interviews to evaluate JobViz. The results demonstrated the usefulness and effectiveness of our approach.
Maciej Beręsewicz, Marek Wydmuch, Herman Cherniaiev
et al.
The goal of this paper is to develop a multilingual classifier and conditional probability estimator of occupation codes for online job advertisements in accordance with the International Standard Classification of Occupations (ISCO) extended with the Polish Classification of Occupations and Specializations (KZiS), which is analogous to the European Classification of Occupations. In this paper, we utilise a range of data sources, including a novel one, namely the Central Job Offers Database, which is a register of all vacancies submitted to Public Employment Offices. Their staff members code the vacancies according to the ISCO and KZiS. A hierarchical multi-class classifier has been developed based on the transformer architecture. The classifier begins by encoding the jobs found in advertisements to the widest 1-digit occupational group, and then narrows the assignment to a 6-digit occupation code. We show that incorporation of the hierarchical structure of occupations improves prediction accuracy by 1-2 percentage points, particularly for the hand-coded online job advertisements. Finally, a bilingual (Polish and English) and multilingual (24 languages) model is developed based on data translated using closed and open-source software. The open-source software is provided for the benefit of the official statistics community, with a particular focus on international comparability.
Ahmad Gholizadeh Lonbar, Hamidreza Hasanzadeh, Fahimeh Asgari
et al.
The proliferation of complex online media has accelerated the process of ideology formation, influenced by stakeholders through advertising channels. The media channels, which vary in cost and effectiveness, present a dilemma in prioritizing optimal fund allocation. There are technical challenges in describing the optimal budget allocation between channels over time, which involves defining the finite vector structure of controls on the chart. To enhance marketing productivity, it's crucial to determine how to distribute a budget across all channels to maximize business outcomes like revenue and ROI. Therefore, the strategy for media budget allocation is primarily an exercise focused on cost and achieving goals, by identifying a specific framework for a media program. Numerous researchers optimize the achievement and frequency of media selection models to aid superior planning decisions amid complexity and vast information availability. In this study, we present a planning model using the media mix model for advertising construction campaigns. Additionally, a decision-making strategy centered on FMEA identifies and prioritizes financial risk factors of the media system in companies. Despite some limitations, this research proposes a decision-making approach based on Z-number theory. To address the drawbacks of the RPN score, the suggested decision-making methodology integrates Z-SWARA and Z-WASPAS techniques with the FMEA method.
The article questions the efficiency of modern social advertising posters. The research aims to establish the reasons for the inefficiency of using precedent texts in social advertising and formulate the requirements that should be followed when using them. The material of the study was a collection of posters used in Russia for social advertising, selected by continuous sampling method from various media banks on the Internet, total of more than 14,000 items. The authors analyzed social advertising posters, of which contain various precedent texts (including the transformed ones). A psycholinguistic experiment was conducted to determine the degree of precedent text recognition in social advertising by recipients. It is shown that most of the precedent texts in social advertising do not meet the requirements for this type of advertising tools: they are not well-recognized; they are often not semantically related to the thesis being promoted; and represented in transformed and parcelled forms, which makes it difficult to identify them. The authors come to the conclusion that in order to increase the impact potential of social advertising with the help of precedent texts, it is necessary to take into account not only the very fact of using the precedent text, but also its place in the national culture. Moreover, constant updating of set expressions in the precedent text bank, addressee's cultural, historical and life experience are to be taken into consideration. It has been revealed that the most efficient source of precedent texts for social advertising posters are the works of art (both verbal and visual), since they allow especially high quality and complete reflection of fragments of reality in figurative form
Bandana Pandey, Guarav Kumar, Leila O. Algavi
et al.
In the modern world, people are too techno-friendly and dependent on technology using the Internet for every work. The same goes for the news. People are shifting from traditional mass media to digital news platforms and getting news through websites, news portals, social media, etc. If you are dependent on the Internet for every kind of information, then you will face false information on the Internet. False or fake news is defined as any information that does not have any credible and reliable source behind it or any misleading information that is likely to mislead the public. The aim behind fake news transmission is to damage a person's or entity's reputation or advertising revenue. If you want not to fall into the fake news you should know about fake news detection and media literacy. The main purpose of the study is to check the exposure of fake news awareness and fake news detection methods among social media users. In the current scenario, this is much necessary to know that social media users have the advisable knowledge of fake news detection and media literacy because people easily fall into the rumors. Mob lynching is one of the biggest rumors on the Indian Internet. In this research, the survey method and questionnaire for data collection were used. The questionnaire was distributed randomly over different social media platforms and emails to the intended respondents. The findings obtained reveal that most fake or false news in India is transmitted through WhatsApp, but social media users have adequate knowledge of fake news and media literacy.
Miguel Alcobendas, Jonathan Ji, Hemakumar Gokulakannan
et al.
Floors (also known as reserve prices) help publishers to increase the expected revenue of their ad space, which is usually sold via auctions. Floors are defined as the minimum bid that a seller (it can be a publisher or an ad exchange) is willing to accept for the inventory opportunity. In this paper, we present a model to set floors in first price auctions, and discuss the impact of its implementation on Yahoo sites. The model captures important characteristics of the online advertising industry. For instance, some bidders impose restrictions on how ad exchanges can handle data from bidders, conditioning the model choice to set reserve prices. Our solution induces bidders to change their bidding behavior as a response to the floors enclosed in the bid request, helping online publishers to increase their ad revenue. The outlined methodology has been implemented at Yahoo with remarkable results. The annualized incremental revenue is estimated at +1.3% on Yahoo display inventory, and +2.5% on video ad inventory. These are non-negligible numbers in the multi-million Yahoo ad business.
The terms and definitions involved in the research paper have been analysed and divided into two semantic groups. A historical reference on the history of ethics formation as a science has been given. On its basis the current state of ethics and its problems have been formed. The main trends in the Internet advertising market have been analysed, as a result of which the main directions of development in the sphere and the problems arising from this have been determined. A semantic and structural analysis of the existing ethical codes of related fields has been carried out. As a result, existing shortcomings have been revealed. Based on the work done, a template has been proposed, which in the future can serve as a basis for creating similar models in other areas.
In this paper, we present bAdvertisement, a novel attack method against advanced driver-assistance systems (ADASs). bAdvertisement is performed as a supply chain attack via a compromised computer in a printing house, by embedding a "phantom" object in a print advertisement. When the compromised print advertisement is observed by an ADAS in a passing car, an undesired reaction is triggered from the ADAS. We analyze state-of-the-art object detectors and show that they do not take color or context into account in object detection. Our validation of these findings on Mobileye 630 PRO shows that this ADAS also fails to take color or context into account. Then, we show how an attacker can take advantage of these findings to execute an attack on a commercial ADAS, by embedding a phantom road sign in a print advertisement, which causes a car equipped with Mobileye 630 PRO to trigger a false notification to slow down. Finally, we discuss multiple countermeasures which can be deployed in order to mitigate the effect of our proposed attack.
In the context of global changes in the media market under the influence of Internet technologies, it becomes necessary to identify the main trends of its development, so the characteristic of the market trends, the integration process of entrepreneurial structures in the media industry, aimed at increasing their competitiveness at the present stage of development of the market economy. In the context of an ever-changing, competitive digital ecosystem and against the background of General cuts in advertising budgets in the print press, radio, and television, advertising investments in the digital environment are increasing starting in 2019. Revenues of legacy news media organizations (also called old or traditional media organizations) are constantly declining due to the high degree of audience participation in new media due to the use of user-generated content, media automation, and new digital media organizations. Using mathematical-statistical and logical research methods, the article identifies specific features of the media business sphere, which include audience fragmentation, culture of creative destruction, and hypercompetition in the media market. In addition, key issues and current changes in the media industry in the context of global digitalization are highlighted, reflecting the introduction of innovative technologies that affect the media industry, and the decline in traditional methods of delivery and media consumption. Global digitalization has affected the growth of media platforms, resulting in an increase in the supply of content itself, as well as significant changes in its consumption by the advertising market.
Political institutions and public administration (General)