Advertising Platform Research Articles

Improving network resilience against DDoS attacks: A fuzzy TOPSIS-based quantitative assessment approach

Security in data transmission is becoming problematic due to the internet's explosive growth as well as the spread of social media and digital advertising platforms, which have produced enormous volumes of data every day. In order to safeguard sensitive data, network security is becoming more and more important as technology is adopted. Effective information security measures are needed to protect against a variety of dangers, given the expanding number of users. A key factor in improving security is network security attributes, which include message encryption, password breaking, safeguarding wireless networks, and finding security holes. This paper adopts the Multi-Criteria Decision Making (MCDM) approach, utilising a fuzzy TOPSIS-based method, with the objective of systematically evaluating resilience in network security, especially against DDoS attacks. The research assesses different security attributes and vulnerabilities to offer actionable insights on how best to strengthen organizational cybersecurity frameworks. The methodology encompasses conducting network security audits to identify vulnerabilities that could compromise commercial operations or expose sensitive data. The findings provide critical insights that can inform targeted actions to address these vulnerabilities and enhance resource protection. The results indicate that network N6 is the most secure under DDoS attacks, followed by networks N1, N5, N3, N4, and N2. This research is significant as it aids strategic decision-making, strengthens network defenses, and enhances overall cybersecurity resilience in an era of evolving cyber threats. By addressing the complexities of network security assessments, this study makes a crucial contribution to the ongoing efforts of organisations to safeguard their data, employees, and customer information from sophisticated cyber threats.

Balancing Innovation and Profitability: Technological Diversification in Iran’s Insurance Industry

Abstract This study explores how technological diversification affects financial performance in Iran’s insurance industry. Using semi-structured interviews with six experts, including CEOs and faculty members, key indicators of technological diversification were identified, such as new underwriting software and digital advertising platforms. A 33-item questionnaire was developed based on these insights and distributed to top managers of Iranian insurance companies, with secondary financial data sourced from the Central Insurance of Iran. The study employes regression analysis and Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the impact of technological diversification on financial performance, controlling for firm size, debt ratio, and company age. Findings indicate a significant negative relationship between technological diversification and both Return on Equity (ROE) and Return on Assets (ROA), suggesting that the costs of adopting new technologies may outweigh their short-term financial benefits. Additionally, high debt ratios were found to adversely affect ROA, highlighting the financial risks of excessive borrowing.

Pharmaceutical Advertising and Public Perceptions in Saudi Arabia

Introduction: As the pharmaceutical advertising landscape evolves with digital advancements, this study examines public awareness and perceptions of medication advertisements in Saudi Arabia. It focuses on the effects of regulatory frameworks and evaluates how they influence public understanding and attitudes toward these advertisements. Method: A cross-sectional study was conducted using an electronic survey in Saudi Arabia in December 2023. The survey was distributed on social media platforms and reached a diverse sample of 440 participants. It covered public perception and attitudes toward drug advertisements, knowledge of regulatory laws, and preferences regarding advertising mediums. Results: Out of the 440 participants in the study, who were primarily employees with bachelor’s degrees, there was a clear awareness of drug advertisements. The average age of the group was 33 years, and a significant portion (71.1%) held a bachelor’s degree, with 51.1% being employed. The findings revealed that 25.5% of participants frequently noticed drug ads, while 22.7% saw them very often. Although many found the ads informative, there were significant concerns about unrealistic expectations and the risk of overmedication; 89.8% believed the ads set unrealistic expectations about the effectiveness of medications. Additionally, 60.7% thought that celebrity endorsements might mislead the audience, and 91.1% felt that ads should provide more detailed information about potential risks and side effects. Regarding preferred advertising platforms, mobile apps and websites were favored (47%), followed closely by social media (46.4%). A striking 93.2% of participants believed that drug ads on social media should be subject to stricter regulations, and 96.4% wanted more proactive monitoring of online advertising. Many also reported using other sources, such as medical review sites, to verify medication information. Conclusions: Pharmaceutical advertising in Saudi Arabia must balance ethical transparency with educational value. The influence of digital platforms underscores the necessity for stricter regulation and accurate information dissemination. A collaborative approach is essential to align advertising practices with public health interests and regulatory standards.

Cross Functional Leadership in Product Development for Programmatic Advertising Platforms

Cross Functional Leadership in Product Development for Programmatic Advertising Platforms

Parallel Experimentation and Competitive Interference on Online Advertising Platforms

Parallel Experimentation and Competitive Interference on Online Advertising Platforms

To Be or Not To Be (an Ad): Advertising Students’ Understanding of Instagram In-Feed Native Advertising

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.

Challenges to Radio Advertising in the Face of Emerging Social Media and Digital Advertising

The traditional dominance of radio advertising in marketing strategies is increasingly challenged by the rapid evolution and widespread adoption of social media and digital advertising platforms. This research paper explores the nuanced challenges that radio advertising encounters in this shifting landscape, comparing its strengths and weaknesses against emerging digital alternatives. Drawing on comprehensive research and industry insights, the paper provides strategic recommendations for radio advertisers to effectively navigate and harness opportunities amidst these transformative changes.

Companies inadvertently fund online misinformation despite consumer backlash

The financial motivation to earn advertising revenue has been widely conjectured to be pivotal for the production of online misinformation1–4. Research aimed at mitigating misinformation has so far focused on interventions at the user level5–8, with little emphasis on how the supply of misinformation can itself be countered. Here we show how online misinformation is largely financed by advertising, examine how financing misinformation affects the companies involved, and outline interventions for reducing the financing of misinformation. First, we find that advertising on websites that publish misinformation is pervasive for companies across several industries and is amplified by digital advertising platforms that algorithmically distribute advertising across the web. Using an information-provision experiment9, we find that companies that advertise on websites that publish misinformation can face substantial backlash from their consumers. To examine why misinformation continues to be monetized despite the potential backlash for the advertisers involved, we survey decision-makers at companies. We find that most decision-makers are unaware that their companies’ advertising appears on misinformation websites but have a strong preference to avoid doing so. Moreover, those who are unaware and uncertain about their company’s role in financing misinformation increase their demand for a platform-based solution to reduce monetizing misinformation when informed about how platforms amplify advertising placement on misinformation websites. We identify low-cost, scalable information-based interventions to reduce the financial incentive to misinform and counter the supply of misinformation online.

Assessing Timely Migration Trends Through Digital Traces: A Case Study of the UK Before Brexit

Digital trace data presents an opportunity for promptly monitoring shifts in migrant populations. This contribution aims to determine whether the number of European migrants in the United Kingdom (UK) declined between March 2019 and March 2020, using weekly estimates derived from the Facebook Advertising Platform. The collected data is disaggregated according to age, level of education, and country of origin. To examine the fluctuation in the number of migrants, a simple Bayesian trend model is employed, incorporating indicator variables for age, education, and country. The Facebook data indicates a downward trend in the number of European migrants residing in the UK. This result is further confirmed by the data from the Labour Force Survey. Notably, the outcomes reveal that in the run-up to Brexit, the most significant decline occurred among the age group of 20 to 29 years old – the largest migrant group – and the tertiary educated. This analyses could not be implemented with traditional data sources such as the Labour Force Survey, because this level of disaggregation is not provided. However, there are also important limitations associated with digital trace data, such as algorithm changes and representativeness. These limitations need to be addressed by employing sound statistical methodologies. Nevertheless, this research shows the potential of digital trace data in anticipating migration trends at a timely granularity and informing policymakers.

Fenomena Judi Online di Kalangan Generasi Muda

Online gambling games are popular among teenagers nowadays. This is caused by very sophisticated technological advances, socio-economic pressures, and the role of social media as an advertising platform. This research aims to understand the reasons why the younger generation plays online gambling and solve the problems of this case. In collecting data, researchers obtained data through observation and interviews. The results of this research explain the factors of playing online gambling such as environmental, cognitive and perception factors. This case has an impact on finances (material values), vital values, spiritual values ​​(mental health and social relationships). Playing online gambling games is caused by technological accessibility, socio-economic pressures, lack of public awareness about the laws and risks of online gambling.

Integrating Traditional and Social Media Data to Predict Bilateral Migrant Stocks in the European Union

Although up-to-date information on the nature and extent of migration within the European Union (EU) is important for policymaking, timely and reliable statistics on the number of EU citizens residing in or moving across other member states are difficult to obtain. In this paper, we develop a statistical model that integrates data on EU migrant stocks using traditional sources such as census, population registers and Labour Force Survey, with novel data sources, primarily from the Facebook Advertising Platform. Findings suggest that combining different data sources provides near real-time estimates that can serve as early warnings about shifts in EU mobility patterns. Estimated migrant stocks match relatively well to the observed data, despite some overestimation of smaller migrant populations and underestimation for larger migrant populations in Germany and the United Kingdom. In addition, the model estimates missing stocks for migrant corridors and years where no data are available, offering timely now-casted estimates.

Impact of Digital Advertising on Brand Visibility in B2B at Petrocoat Speciality Chemical Private Limited

In the digital era, business-to-business (B2B) businesses’ visibility is greatly influenced by digital advertising. Through a review of previous research, empirical studies, and industry data, this study investigates the ways in which digital advertising methods affect the visibility of brands within the B2B sector. It examines several digital advertising platforms and strategic factors to make campaigns successful. This research seeks to assist B2B marketers in leveraging digital advertising to improve their brand’s visibility and achieve a competitive edge by providing useful insights and advice.

Leveraging Digital Advertising Platforms for Consumer Research

Abstract Digital advertising platforms have emerged as a widely utilized data source in consumer research; yet, the interpretation of such data remains a source of confusion for many researchers. This article aims to address this issue by offering a comprehensive and accessible review of four prominent data collection methods proposed in the marketing literature: “informal studies,” “multiple-ad studies without holdout,” “single-ad studies with holdout,” and “multiple-ad studies with holdout.” By outlining the strengths and limitations of each method, we aim to enhance understanding regarding the inferences that can and cannot be drawn from the collected data. Furthermore, we present seven recommendations to effectively leverage these tools for programmatic consumer research. These recommendations provide guidance on how to use these tools to obtain causal and non-causal evidence for the effects of marketing interventions, and the associated psychological processes, in a digital environment regulated by targeting algorithms. We also give recommendations for how to describe the testing tools and the data they generate and urge platforms to be more transparent on how these tools work.

Expectations and concerns regarding medical advertisements via large commercial medical platform advertising companies: a legal perspective

Advertising in the medical and legal fields, which are among Korea's leading professions, has increasingly utilized major advertising platforms such as LawTalk and UNNI—two of the most prominent and contentious platforms in their respective fields. While it is generally unproblematic for professionals like lawyers and doctors to promote public interest through advertising on these commercial platforms, the creation of a profit-driven structure has the potential to undermine their professional ecosystems. This article explores the issues associated with advertising in the medical field through large commercial platforms, drawing on notable examples from the legal and medical fields in Korea. Specifically, we analyze two of the most popular yet controversial platforms in these sectors, LawTalk and UNNI. In Korea, the format and method of advertising are legal as long as they do not involve referring or soliciting clients, thereby making platform advertising lawful when used solely for that purpose. Nevertheless, it is crucial to prevent medical advertising platforms from establishing market monopolies by skirting various profit regulations and laws. In response to these concerns, the Korean Bar Association has prohibited all advertisements by platform companies. The medical community should closely examine the rationale and process behind this decision. Given the significant social influence of large corporate platforms and the unique social responsibilities of the medical and legal professions, future platform advertising should be subject to distinct legal and institutional regulations that differ from those applied to general services.

A Security-enhanced Advertising Platform based on Blockchain and Edge Computing in Generative AI

ABSTRACT With the development of advertising technology, especially the emergence of generative artificial intelligence, the generation and editing of advertising content has become more convenient and efficient. However, this also brings new security risks to digital advertising. For example, in order to generate personalized advertising content, AI can collect user behavior data without the user’s knowledge and automatically generate ads that are beneficial to advertisers. In addition, the main issue regarding outdoor advertising is whether the outdoor advertising platform can deliver the advertising content required by the advertiser at the designated place and time according to the actual requirements of the customer. Combining blockchain and edge computing technology, this paper builds a security-enhanced outdoor advertising platform. The decentralized, non-tamperable and traceable features of blockchain technology provide the advertising platform with credibility between users and advertisers, as well as credibility between advertisers and the advertising platform. At the same time, the terminal controller with edge processing capabilities improves the efficiency of advertising and enhances the task balance between the server and the playback side. Experimental results show that the platform can effectively manage user identities and maintain advertising plans. It not only improves data security and credibility, but also has good performance.

Computer-Aided Technology in Product Communication and Promotion Strategy

In today's competitive marketplace, effective in-product communication and promotion strategies are essential for capturing consumer attention and driving product adoption. These strategies involve utilizing various channels within the product itself to convey key messages, showcase features, and encourage user engagement. Computer-aided promotion strategies leverage digital tools and technologies to enhance marketing efforts and reach target audiences more effectively. By utilizing data analytics, automation, and digital advertising platforms, businesses can optimize promotional campaigns, personalize messaging, and target specific demographic segments with precision. This paper proposes an innovative approach to product communication and promotion strategy utilizing computer-aided technology, specifically tailored for the Weibo platform, named the Weibo Stacked Communication Classification Strategy (WeSCCS). The WeSCCS framework integrates advanced computational techniques with Weibo's communication dynamics to classify and categorize promotional content effectively. With machine learning algorithms and natural language processing, the system analyzes user engagement patterns, content preferences, and social interactions to identify optimal communication strategies. WeSCCS classifies user interactions with promotional content such as "positive engagement" (70% of interactions), "neutral engagement" (20%), and "negative engagement" (10%). Additionally, automated content generation and sentiment analysis enable marketers to generate relevant promotional content with a sentiment score of +0.8, indicating high positivity. Real-time monitoring facilitates dynamic adjustments to promotional strategies based on user feedback, ensuring responsiveness to evolving consumer preferences. By integrating computer-aided technology, WeSCCS empowers marketers to maximize promotional impact, increase audience engagement, and drive conversions effectively on Weibo and similar social media platforms.

Stop, in the Name of COVID! Using Social Media Data to Estimate the Effects of COVID-19-Related Travel Restrictions on Migration

In the wake of the COVID-19 pandemic, the International Organization for Migration has postulated that international migrant stocks fell short of their pre-pandemic projections by nearly 2 million as a result of travel restrictions. However, this decline is not testable with migration data from traditional sources. Key migration stakeholders have called for using data from alternative sources, including social media, to fill these gaps. Building on previous work using social media data to analyze migration responses to external shocks, we test the hypothesis that COVID-related travel restrictions reduced migrant stock relative to expected migration without such restrictions using estimates of migrants drawn from Facebook's advertising platform and dynamic panel models. We focus on four key origin countries in North and West Africa (Côte d'Ivoire, Algeria, Morocco, and Senegal) and on their 23 key destination countries. Between February and June 2020, we estimate that a destination country implementing a month-long total entry ban on arrivals from Côte d'Ivoire, Algeria, Morocco, or Senegal might have expected a 3.39% reduction in migrant stock from the restricted country compared with the counterfactual in which no travel restrictions were implemented. However, when broader societal disruptions of the pandemic are accounted for, we estimate that countries implementing travel restrictions might paradoxically have expected an increase in migrant stock. In this context, travel restrictions do not appear to have effectively curbed migration and could have resulted in outcomes opposite their intended effects.

Multi-Behavior Graph Neural Networks for Recommender System.

Recommender systems have been demonstrated to be effective to meet user's personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recent years have witnessed the emerging success of many deep-learning-based recommendation models for augmenting collaborative filtering (CF) architectures with various neural network architectures, such as multilayer perceptron and autoencoder. However, the majority of them model the user-item relationship with single type of interaction, while overlooking the diversity of user behaviors on interacting with items, which can be click, add-to-cart, tag-as-favorite, and purchase. Such various types of interaction behaviors have great potential in providing rich information for understanding the user preferences. In this article, we pay special attention on user-item relationships with the exploration of multityped user behaviors. Technically, we contribute a new multi-behavior graph neural network (MBRec), which specially accounts for diverse interaction patterns and the underlying cross-type behavior interdependencies. In the MBRec framework, we develop a graph-structured learning framework to perform expressive modeling of high-order connectivity in behavior-aware user-item interaction graph. After that, a mutual relationship encoder is proposed to adaptively uncover complex relational structures and make aggregations across layer-specific behavior representations. Through comprehensive evaluation on real-world datasets, the advantages of our MBRec method have been validated under different experimental settings. Further analysis verifies the positive effects of incorporating the multi-behavioral context into the recommendation paradigm. In addition, the conducted case studies offer insights into the interpretability of user multi-behavior representations. We release our model implementation at https://github.com/akaxlh/MBRec.

Analyses of Business Attributes and Facebook Utilization: Marketing Insights

Microenterprises have a significant impact on the nation's economic development. Given the current trends in marketing, numerous organizations have adopted social media marketing. This study analyzed Facebook's business attributes and perceived effectiveness as an advertising tool for microenterprises. The researchers employed a descriptive-causal research design through random sampling of 320 registered microenterprises that utilize Facebook as an advertising tool. Findings revealed that participants were mostly under sole proprietorship, having one to three employees with an estimated asset size of less than 1,000,000 pesos and being in operation for one to three years. Customer brand awareness, product promotion, engagement reach, and sales volume were found to be highly effective in the perceived effectiveness of Facebook use. Remarkably, the business profile length of operation showed significant differences in terms of volumes of sales, whereas the number of staff in terms of customer brand awareness, engagement reach, and volume of sales; average asset size in terms of customer brand awareness and product promotion indicated significant differences to the perceived effectiveness of Facebook utilization as an advertising platform. Hence, the researchers recommended that microenterprises focus on targeted advertising, allocating advertising investment, and optimizing Facebook's advertising features.

Simultaneous Optimization of Bid Shading and Internal Auction for Demand-Side Platforms

Online advertising has been one of the most important sources for industry's growth, where the demand-side platforms (DSP) play an important role via bidding to the ad exchanges on behalf of their advertiser clients. Since more and more ad exchanges have shifted from second to first price auctions, it is challenging for DSPs to adjust bidding strategy in the volatile environment. Recent studies on bid shading in first-price auctions may have limited performance due to relatively strong hypotheses about winning probability distribution. Moreover, these studies do not consider the incentive of advertiser clients, which can be crucial for a reliable advertising platform. In this work, we consider both the optimization of bid shading technique and the design of internal auction which is ex-post incentive compatible (IC) for the management of a DSP. Firstly, we prove that the joint design of bid shading and ex-post IC auction can be reduced to choosing one monotone bid function for each advertiser without loss of optimality. Then we propose a parameterized neural network to implement the monotone bid functions. With well-designed surrogate loss, the objective can be optimized in an end-to-end manner. Finally, our experimental results demonstrate the effectiveness and superiority of our algorithm.