An AI-enabled approach for improving advertising identification and promotion in social networks

Shi, Baisheng; Wang, Hao

doi:10.1016/j.techfore.2022.122269

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…Pan and Wang 6 used machine learning methods to conduct text analysis of the annual report information of media companies to identify the relationship between digital transformation and the value of cultural enterprises. Shi and Wang 30 focused on the advertising industry, combining artificial neural network (ANN) algorithms to achieve intelligent evaluation and predictive analysis of advertising publishing and click results, and to optimize the resource utilization efficiency of the advertising industry. Sun et al 31 used text mining and natural language processing (NLP) technology to conduct an emotional analysis on negative reports on the operation and financial status of media companies and established a warning mechanism for adverse impact on financial status.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning

Wang,

Li,

Zhao

et al. 2024

Sci Rep

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Chinese media companies are facing opportunities and challenges brought about by digital transformation. Media economics takes the evaluation of the business results of media companies as the main research topic. However, overcoming the internal differences in the industry and comprehensively predicting the digital transformation of Chinese media companies from multiple dimensions has become an important issue to be understood. Based on the “TOE-I” theoretical framework, this study innovatively uses machine learning methods to predict the digital transformation of Chinese media companies and to analyze specific modes of the main driving factors affecting the digital transformation, using data from China’s A-share-listed media companies from 2010 to 2020. The study found that environmental drivers can most effectively and accurately predict the digital transformation of Chinese media companies. Therefore, under sustained and stable economic and financial policies, guiding inter-industry competition and providing balanced digital infrastructure conditions are keys to bridging internal barriers in the media industry and promoting digital transformation. In the process of transformation from traditional content to digital production, media companies should focus on policy changes, economic benefits, the decision-making role of core managers, and the training and preservation of digital technology talent.

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Section: Literature Reviewmentioning

confidence: 99%

Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning

Wang,

Li,

Zhao

et al. 2024

Sci Rep

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“…Shi and Wang shed light on the potential of the genetic algorithm back propagation (GABP) neural network in transforming the advertising sector [53]. Their research revealed that the GABP network can be instrumental in predicting the click-through rate (CTR) of web ads, offering advertisers a precise tool to refine their promotional strategies.…”

Section: Digital Advertising Clustermentioning

confidence: 99%

Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review

Ziakis,

Vlachopoulou

2023

Information

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Artificial intelligence (AI) has rapidly emerged as a transformative force in multiple sectors, with digital marketing being a prominent beneficiary. As AI technologies continue to advance, their potential to reshape the digital marketing landscape becomes ever more apparent, leading to profound implications for businesses and their digital outreach strategies. This research seeks to answer the pivotal question: “How could AI applications be leveraged to optimize digital marketing strategies”? Drawing from a systematic literature review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, this study has identified 211 pertinent articles. Through a comprehensive bibliometric analysis, the findings were categorized into distinct clusters, namely: AI/ML (Machine Learning) Algorithms, Social Media, Consumer Behavior, E-Commerce, Digital Advertising, Budget Optimization, and Competitive Strategies. Each cluster offers insights into how AI applications can be harnessed to augment digital marketing efforts. The conclusion synthesizes key findings and suggests avenues for future exploration in this dynamic intersection of AI and digital marketing. This research contributes to the field by providing a comprehensive bibliometric analysis of AI in digital marketing, identifying key trends, challenges, and future directions. Our systematic approach offers valuable insights for businesses and researchers alike, enhancing the understanding of AI’s evolving role in digital marketing strategies.

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The Interplay Between Social Media, Moral Perceptions, and the Proliferation of Mimetic Desires Among Youth in South Asia

Udayanga

2023

The Palgrave Handbook of Global Social Change

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An AI-enabled approach for improving advertising identification and promotion in social networks

Cited by 9 publications

References 36 publications

Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning

Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning

Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review

The Interplay Between Social Media, Moral Perceptions, and the Proliferation of Mimetic Desires Among Youth in South Asia

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