What Is Computational Communication Research?

Introduction

To talk about computational social science, we need to look at a very short history of science. The entire history of science has been working on three kinds of problems which are the problem of simplicity, the problem of disorganized complexity, and organized complexity (Weaver, 1948). The problem of simplicity was largely concerned with two variable problems, which are formulated how one variable to another variable relate each other with keeping small number of involved variables. For example, the relation between temperature and pressure, or the relation between population and time. Such as how the population changed over time in a community. The problem of disorganized complexity or the average was emerged with developing powerful techniques of probability theory to deal with a problem in which the number of variables is very large. For example, analyzing the motion of two or even of three balls on a billiard table. With the disorganized complexity, we cannot trace the action of a ball, but with probability theory, some important questions can be answered with useful precision, such as the average of how many balls per second hit a given stretch of rail or on the average how far does a ball move before it is hit by some other ball (Weaver, 1948). The organized complexity is dealing simultaneously with a sizable number of factors that are interrelated into an organic whole; problems in the economic and political sciences cannot be handled with the statistical techniques so effective in describing average behavior. For example, why can one particular genetic strain of microorganism synthesize within its minute body certain organic compounds that another strain of the same organism cannot manufacture complexity (Weaver, 1948). The development of the new type of electronic computing devices has a tremendous impact on science and they make it possible to deal with problems which previously were too complicated, and more importantly, they justify and inspire the development of new methods of analysis applicable to these new problems (Weaver, 1948). Therefore, the quantitative experimental methods and the mathematical analytical methods of the physical sciences are being applied to the social sciences as never before. This and the technological development make the emergence of new methods which allow computational social science today.

Computational social science refers to computational methods and approaches to study the social sciences, particularly incorporating big data, social network analysis, social media content, and spatial data. Therefore, we need to understand the key factor, which is the digital revolution. The digital revolution happened extremely quickly and profoundly. In the late 80s, 99 percent of all information that we had as humankind externally stored, was in analog format, for example, on paper. The digital format of information progressed after the year 2000 and in 2002, for the first time, the word was able to store more digital than analog information. Hard disks made up the lion share of storage in 2007 (52% in total), optical storage contributed more than a quarter (28%), and digital tape roughly 11% (Hilbert & Lopez, 2011, p. 62).The store digital data has doubled every 2.5 years, and as of 2014, the store digital data is around five zettabytes (5×10²¹bytes).

We have spent most of our daily life in the network, from getting information from different sources and socializing with friends to shopping for our needs and checking medical examination results. All these online actions have left 'digital traces' that provide unprecedented opportunities for researchers to reveal, theorize, and test hypotheses about the way how humans percept, think, believe, behave, and communicate (Lazer et al., 2009; van Atteveldt et al., 2019). This situation led to the emergence of the phenomenon called big data, and the potential of big data helps researchers shed a strong light on human behavior (Abanoz, 2020). The main factors that provide this feature of big data are the significant change in volume, velocity, variety (Rains, 2020). The adjective 'big' refers here less to the size of the datasets and more to their spatial and temporal resolution, meaning that the data are more prosperous than before and that they span several levels of analysis, from the individual to the collective (González-Bailón, 2013, p. 147).