The future of research, the research of the future

Last week, we held the official opening events for iLab (Communication Research and Application Laboratory) at the Marmara University Faculty of Communication. Over three days, we hosted wonderful sessions, presentations, and workshops. 

I began my career as an assistant thirty-six years ago at ISKAR (Center for Statistics and Quantitative Research), founded by my professors Ateş Vuran and Ahmet Lütfü Orkan. Until it closed in 1997, we provided computer-aided analysis support services to hundreds of researchers from both within and outside the university. I gained significant experience in research there and have tried to share it with my students and colleagues as much as I could. However, I have always felt the absence of the collective work and collaboration we achieved at that center. 

Now, with this laboratory we have opened in our faculty, we will strive to fulfill two elements that are crucial in the field of social sciences, and especially in communication sciences. 

The first is to conduct projects by addressing research methods with a mixed methods approach and to inform young people pursuing graduate education. The second is to better understand our transforming (and largely digitized) social life and the (digital) media sector, which plays a leading role in making this life visible and sharing it. 

To achieve this, we need to understand that our social life is a network of interconnected events and to create insight by seeing the patterns in this network. 

So, how will we do this? We can make this very general question more specific: How do we teach communication research in academia, and how should we teach it?

To give a quick and short answer, we can say it is through a mixed methods approach that uses both quantitative and qualitative methods together. We cannot yet fully and precisely measure variables such as love, anger, intention, fear, belief, commitment, jealousy, and perhaps hundreds of other types of emotions in social events. However, we can roughly rank them as “less – more – least – most.” We use Computer-Assisted Qualitative Data Analysis Software to analyze such data and reveal patterns. 

On the other hand, because we spend more and more time on the internet and social media platforms, our digital footprints—that is, our posts—in these channels are becoming quantitatively measurable. General artificial intelligence technologies are making significant progress in this regard. The latest product from OpenAI, GPT-4o, is a good example of this. The “o” in its name stands for “omni,” meaning “all.” We use Statistical Software to analyze quantitative data and reveal patterns. 

At the intersection of these two types of analysis methods lies Social Network Analysis Software, also known as sociograms. This is inevitable because we live our social relationships in networks consisting of the people and institutions we are connected to. Fields of study such as the development of activist movements, social polarization, surveillance capitalism and the data economy, advertising and customer networks, market structures, and inequalities fall within this scope. Understanding social relationships is synonymous with understanding networks. 

So, where does all this lead us? That is, what is in the future of research? What will the research of the future be like?

To know where we are going, we must first know where we came from. One of the significant developments in the field of scientific research methods in the 1950s was the computer revolution and “exploratory data analysis,” pioneered by John Tukey. The second revolution was the Internet revolution, and “connectivity” in social life began to shift to the virtual environment, albeit slowly. The third revolution followed immediately with the “interaction” revolution in social media. Now, we are at the beginning of the fourth revolution process: General artificial intelligence, and we are deeply concerned about this situation. 

All these revolutions give me an idea about what the research of the future will be like: It has now become inevitable for “traditional” sociology to transform into “digital” sociology. There are already footsteps of this. Very important work is being done in a field called Computational Social Sciences. With these studies, we are, in a sense, increasing our measurement precision. We can measure better, and thus we can understand better. Perhaps this could be the lever in Archimedes’ quote, “Give me a lever long enough and a fulcrum on which to place it, and I shall move the world.”