Media Intelligence Laboratory: A new research initiative at Arcada

Media serves a crucial societal function, and if the media isn't doing well, democracy isn't doing well either. Media's information dissemination is also vital for societal renewal and development. Without new impulses, innovations and businesses don't arise.

In today's digital age, the abundance of digital news media content presents both opportunities and challenges for society and businesses alike. The Media Intelligence Laboratory project aims to harness the power of free-to-use and/or cost-efficient paid web tools to automate content analysis of digital media.

By doing so, the project intends to contribute to the betterment of society by generating valuable insights from digital content, and aiding companies, public sector and non-governmental organisations in making informed decisions. Furthermore, a central goal of this project is to develop tools that Arcada students in media management and elsewhere can utilise for media analysis in their master’s degree education and thesis work especially.

News platforms and social media

By digital news media we refer to the dissemination and consumption of news content through digital platforms and technologies. It encompasses the online versions of traditional news outlets (such as news outlets and television networks), as well as news-focused social media platforms like Facebook, Instagram, X (ex-Twitter) and TikTok. Overall, digital news media has transformed the way people access and engage with news information, offering immediacy, interactivity, and accessibility that were not possible with traditional news formats.

Social media has brought about many positive changes in how we communicate, connect, and share information, but it also comes with several downsides from a societal perspective, such as spread of fake news, polarisation and hate speech. In the project we also trace and locate mechanisms that generate these downsides.

Quantitative content analysis is a research method that involves systematically analysing textual, visual, or audio content using numerical data and statistical techniques. It aims to uncover patterns, trends, and relationships within a large volume of media or communication materials. By coding and categorising content based on predefined criteria, researchers can generate quantitative insights into topics, themes, frequencies, and other measurable aspects, providing a structured and relatively objective approach to understanding media content and its effects.

We believe quantitative content analysis and this research project can play a role in addressing problems associated with social media. We aim also to support businesses with data-driven insights that inform decision-making, enhance brand management, and drive overall growth and success.

There is a long tradition in traditional media research of examining and analysing media content. In fact, the classic research method in media and communication research is quantitative content analysis. However, in the 1990s, qualitative research methods gained prominence. Hence, this project builds on traditional quantitative analysis of media and internet content (Honkanen 2020; 2021; Tana, Eirola & Nylund 2020), as well as discourse analytical investigations of media and public discourse (Honkanen 1999; Nylund 2000).

When the internet became mainstream in the mid-1990s, the volume of information exploded. A subsequent information explosion occurred in the early 2000s with the advent of social media. Currently, we are experiencing a third information explosion with AI-based tools for automated content production (NVIDIA 2023; Davenport and Mittal 2022).

Media analysis in a social context

This has naturally affected media research in many ways. Qualitative analysis of media content has continued, but the material for qualitative studies is limited, and the volume of information in the digital environment is vast, leading to significant challenges regarding generalisation claims. This led many researchers who had been studying media content to shift their focus. New directions emerged, such as media management, which studied media as organisations and often aimed to support their survival in the digital environment by researching new business models and technological opportunities (Nylund 2013).

The reason for this shift was not an abandonment of the idea of media's importance for democracy and society, but quite the opposite. Recently, traditional media have struggled, particularly against digital and global giants like Google, Apple, Facebook, Amazon, and Microsoft (the so-called GAFAM platform companies). These giants encroached on both the media's advertising and audience markets. The goal educators and researchers in media management have is to contribute to supporting the media and its societal functions.

The development of data analytics and AI has provided researchers with new opportunities to study media content using Big Data analysis. Often, researchers with backgrounds and/or expertise in IT and machine learning lead this development. Researchers who employed traditional social science methods inevitably felt left behind. Some of them introduced the concept of small data analysis, but this research did not gain the same traction as big data studies.

However, the problem with big data studies is that they often focus too much on methodology. Research questions are framed based on available research data and methodology rather than societal context. The results are often presented using flashy data visualisation but are not problematised from a societal analysis or societal context perspective.

For instance, Google Trends has been established as a research tool for analysing search term popularity over time (Tana et. al 2021). It is a wonderful tool for identifying emerging trends, assess public interest, and to understand regional and demographic variations. However, it lacks altogether contextual information. Google Trends provides data on search volume, but doesn't explain why certain trends occur and how they are created. Thus, the explanatory power of Google Trends is rather weak, and therefore the tool makes it challenging to draw meaningful conclusions.

The purpose of this project is to address shortcomings like this. Our assessment is that the time is now ripe for researchers with traditional social science research training to begin utilising data analytical tools to conduct big data media analysis and interpret the results within a societal context. The Media Intelligence Laboratory aims to identify the available tools, test them, and develop a research approach that enables media analyses that respond to the needs of society and the business world.

Sustainable media strategies

By building on the achievements and insights gained from previous projects, this initiative aims to address the evolving needs of media analysis, empower students, and contribute to Arcada, the academia in general and industry. The outcome of this research will foster a deeper understanding of digital media content and its impact on various societal and business sectors.

The word “laboratory” indicates that this project aims to create not just results and knowledge, but something that is more sustainable. The professional needs in media business is related to a rapid obsolescence of knowledge. Therefore, it is crucial to promote the intellectual development of the students as learners. University students in media management can enhance their learning in a media analysis laboratory by engaging in hands-on, practical experiences that allow them to analyse and critically assess various forms of media. Our ambition is to create a learning environment where students can analyse real-life cases, but the important thing is that the Media Intelligence Laboratory creates students that are equipped with skills and knowledge that are attractive to businesses and the industry as a whole.

Media Intelligence Laboratory also aims to develop a research methodology that has great value for years to come and is also updated when new technological tools emerge. It is important to stress that in the master’s degree in media management we do not educate students to create media content, but to analyse the media industry on a strategic level, and, consequently, to develop as media analysis specialist and media leaders.

The Media Intelligence Laboratory started at Arcada University of Applied Sciences in January 2024 and has been provided funding for one year by A.F Lindstedt Foundation.

Sources

Davenport, T. and Mittal, N. (2022) How Generative AI Is Changing Creative Work. Harvard Business Review. https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work External link

Honkanen, Petri (2021): Kuinka lohkoketjuekosysteemejä hallitaan? Arcada Working Papers 2/2021. https://www.theseus.fi/bitstream/handle/10024/500989/AWP_2_2021_Honkanen.pdf?sequence=1&isAllowed=y External link

Honkanen, Petri (2020): Lohkoketjuteknologian massa-adoption mahdollisuutta tutkimassa. Arcada Working Papers 2/2020. https://www.theseus.fi/bitstream/handle/10024/352818/AWP_2_2020_Honkanen.pdf?sequence=1&isAllowed=y External link

Honkanen, Petri (1999): Lainsäätäjä jumalan armosta? Yhteiskunta koulutuskomiteoiden mietinnöissä. Turun yliopisto. Turku.

NVIDIA (2023) Generative AI: Revolutionizing The Way Enterprises Work. E-book. https://lore.com/gen-ai-ebook External link

Nylund, M (2000) Iscensatt Interaktion: Strukturer och strategier i politiska mediesamtal. (Enacted Interaction: Structures and strategies in Broadcast Discourse). Helsinki: Svenska litteratursällskapet i Finland 622. https://digi.kansalliskirjasto.fi/teos/binding/2253695?page=1 External link

Nylund, M. (2013) Challenges in Media Management. In Karlsson, J & Westerlund, M (eds) BITA’13, Proceedings of Seminar on Current Topics in Business, Information Technology and Analytics, pp. 45-48. Arcada publikation 2/2013. https://www.theseus.fi/bitstream/handle/10024/70463/ArcadaPublikation_2_2013.pdf?sequence=1&isAllowed=y External link

Tana, J. Eirola, E. & Nylund, M. (2021) When is prime-time in streaming media platforms and video-on-demands services? New media consumption patterns and real-time economy. European Journal of Communication. https://journals.sagepub.com/doi/full/10.1177/0267323119894482 External link