Artificial Intelligence-Powered Political Advertising: Harnessing Data-Driven Insights for Campaign Strategies

Introduction

Political advertising has long been a critical component of election campaigns, aiming to influence voters' opinions, attitudes, and decisions. Studies on social and political marketing have grown over time (Amifor, 2016). Few studies have looked at how sponsored mass media messages affect voters, despite the fact that political candidates have relied more and more on television advertising to educate and sway voters (Kraus & Berelson, 1969). In its traditional form, political advertising relied on broad-brush approaches, targeting entire demographics based on general assumptions and limited data. However, the rise of digitalization and the abundance of user-generated data have sparked a new era of data-driven advertising, presenting an opportunity to delve deeper into voter behavior and preferences. A person's capacity to retrieve names of candidates, traits, and educational achievements, to recognize electoral concerns and recent campaign progress, & comprehend linkages with candidates' opinions on various subjects are all examples of political knowledge. Numerous voting researches have concluded that general mass media campaign communications have an adverse effect on knowledge gains due to the consistently observed moderate connection between media exposure and campaign-related knowledge (Bhakri & Shri, 2021). Political advertising is frequently categorized into either positive or negative advertising. Positive advertisements are often those that candidates use to tout their qualifications and strong suits (Silva et al., 2020). Candidates use a range of communication techniques, such as advertising, speeches on the campaign trail, print and broadcast media, to educate and persuade voters. Additionally, over the recent years, we have seen politicians use new interaction tools like social media (Bhattacharya, 2018). Gaining attention on print media and on Televisions has been essential for election growth since, until recently, traditional media served as the main information source for politicians. Candidates additionally disseminated information about their candidature and policy aims through speeches they gave on the campaign trail and other public appearances (Brei, 2020). The applications of artificial intelligence in today's corporate environment are varied. Artificial intelligence, in the opinion of both professionals and academics, will shape our civilization in the future. Technology development has made the world a web of interconnected networks (Atkin & Heald, 1976).

Machine learning is a branch of artificial intelligence (AI) that focuses on creating statistical models and algorithms that let computers learn from data in order to become better at a given task without having to be explicitly programmed. In other words, machine learning enables AI systems to discover patterns and information in the data they are exposed to and make predictions or judgments based on those patterns and information. The application of technology generated investments in artificial intelligence (AI) for big data analytics to generate market intelligence. AI is continually being used to the advantage of numerous sectors. Artificial intelligence and other emerging technologies are developing concurrently as organisations advance towards Industry (Atkin & Heald, 1976) Machines (computers) that replicate the cognitive and affective functions of the human mind are referred to as artificial intelligence. Artificial intelligence (AI) and the Internet of Things (IoT) are two dynamic and supportive technologies that, when used together, can open up a variety of applications and opportunities (Dixit & Singh, 2022). Political advertising using IoT (Internet of Things) is a relatively young and developing field that has the potential to change how political campaigns interact and reach voters. Artificial intelligence has advanced tremendously over the past few decades because to the tireless work of professionals (Garcia-Jimeno & Yildirim, 2015). The work resulted in important developments, including applications for machine learning and big data analytics in many other domains and circumstances (Greening & Gray, 1994).