Abstract
The term “Big Data” has gained familiarity in the previous year in various academic forums, but its history dates to the early 1960s. Additionally, statistics have shown that “global citizens” became conversant with their data and analysis during those years (Asri, Mousannif, & Moatassime, 2019). What exactly does “Big Data” mean? This is a term used to represent the vast amount of structured and unstructured data that pervades our daily life (Dowling et al., 2019). The ability of a company to translate large and diverse amounts of data into usable information for its establishment is more important than just the amount of data itself. Therefore, certain methodologies should be employed in examining Big Data in the hopes of using it as a foundation for making better decisions and taking more strategic business initiatives. Subsequently, five key points were discovered by the author which were not published in other similar books. These key points include the following:
The first point is Emotional transmission, as emotional judgment which is the effect of disseminating information, is one of the aspects of measurement in this important field of communication research. The acceptance of data by users, particularly the emotional inclination of persuasive details like advertising and elections, is a key measure of information adoption. The author of the book primarily gives research references in this section on the possibility of emotions spreading with text.
Second is the law of modifying an individual’s emotions. The authors stressed the importance of using people’s social media language expressions, conducting extensive text analysis, and attempting to decipher the laws of individual emotional transformation.
Third is the extraction of text features, wherein the efficiency and effectiveness of disseminating information become an essential and fundamental issue in the study of communication which brings about a more detailed discussion on how to evaluate them. A network of semantic co-occurrences is constructed through content on social media, for instance, if two words exist in a document at the same time, it shows there is a relationship between them. Furthermore, emphasis is obtained by computing the value of each term’s “inverted document frequency,” and then a binary network is established to measure the efficiency of information transfer. This point may be disregarded but it is important to know the many ways in exploring the impact of text information and its characteristics on the efficiency of information transmission from a word-structure perspective. Additionally, word network-based analysis is an important research method in the processing of computational communication texts.
Fourth, the use of text to predict an individual’s behavior; the author studied social media, Twitter, and political activities in the United States in-depth. The results showed that with the proper covert research methods, a person’s behavior in the real world could be accurately predicted through monitoring of his social media activities.
The fifth key point is text and socio-cultural changes, as the authors implied that the main application is inclusive of social and cultural change alongside a specific research method, personal, emotional, behavioral changes, and predictions that had to do with the characteristics of the text itself. The differences in macro-social culture and media technology have been analyzed by researchers. These changes have shown to be different from conventional content analysis methods, using text analysis under Big data, combined with visualization, to provide more intuitive and accurate analytical results and reveal human social culture more objectively. Additionally, technology as well as the modifications in values, laws, and characteristics of evolution may also be discovered.
The book titled Introduction of Computational Communication is a current piece that discusses how Big Data is becoming more relevant in the modern world. The authors, Zhang Lun, an academic who has done a lot of research related to Computer in Human Behavior; Wang Chengjun, an expert on Social Network Analysis for Startups; and Xu Xiaoke, a communications professor and professional who won the first prize in the Alibaba Data Platform Contest, are all leading communication scientists and practitioners from China. Computational communication is an important branch of the social science of computing. Its analysis is based on the synergies of human communication behavior, as the richness and complexity of computational communication is a significant challenge. The book emphasizes the various quantifiable factors that influence communication behavior in social networks throughout its 11 chapters and its uses in networking science as its theoretical foundation for shaping practical knowledge frameworks with computational journalism and computational advertising.
In the introduction, the authors address the definition, or lack thereof, concerning how Big Data aims to transform the world. The opening/first chapter of this book explores how different large institutions around the world, including governments and commercial, have finally realized that this initiative may help them excel.
Companies around the world have long been looking for practical approaches to getting information about their customers, products, and services. For a company that has just started and has few customers, it might be easy and simple to analyze all customers who buy the same product in the same way (Piatetsky, 2019).
The companies and markets they monitor may become increasingly intricate as time passes. These organizations must offer a greater variety of products to survive or achieve a competitive advantage with customers, and they must understand how the product fits with the customer and how to deliver it to the customer.
According to the authors, Zhan, Wang, and Xu, certain company data was first generated and kept in a relational database such as SQL Server or MySQL and the company only stored important data such as product names and product prices. Whereas, other unstructured data, such as documents, customer service records, and even images and videos, may be difficult to manage, using a relational database or the company’s system. In this regard, companies must consider new data sources generated by cellular devices like sensors or other new sources of information generated by humans, such as data from social media and track record data obtained from their website interactions.
Although each data source may be managed and collected separately, the difficulty posed may be the struggle for businesses to grasp the relationships between all of these diverse forms of data and interpret them to meet their specific needs. It is impossible to think about a conventional way to manage data when dealing with so much information presenting in various forms. As a result of the preceding example, there may be the emergence of the Big Data trend.
The challenges that Big data analysis presents are numerous. Amongst them are the means of collecting, storing, analyzing, visualizing, and transferring data, alongside data sensitivity and veracity concerns. However, the greater the amount of data collected and examined, the more likely for the analysis to be valid. The results may also potentially provide access to previously unavailable information (p. 8).
With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet. Manufacturers also collect data about customer usage patterns and product performance. The advent of machine learning has also led to the production of more data. Meanwhile, the emergence of Facebook and other social media platforms in the 2000s made people begin to realize how important the data users in these platforms.
Therefore, the focus of the author was on communication studies, and currently, China is one of the countries with the highest internet connection, so the experts may provide a unique perspective on Big Data The citizens of China have a wide range of social media platforms. However, these citizens do not use mainstream social media such as Facebook, Twitter, or Instagram, as they employ alternative domestic social media which is widely used. This would bring an opportunity of gaining insights and new perspectives.
Generally, the flaws of the authors were minimal as there were only a few words that employed very philosophical terminology in Chinese. Therefore, foreign speakers may need to be accompanied by native speakers as they may find it difficult to understand some of the meanings. However, despite all these highlighted flaws, this book is generally perfect.
Additionally, the author also cautioned in the previous chapter that inadequate Big Data gathering and analysis tools may render the data acquired useless. There was an expression of several concerns raised by the author regarding the future development of Big data in addition to discussing its various potentials (p. 297). It is currently recognized that the size of big data doubles every 2 years. Such a fast rate may force professionals to think quickly for future technology capable to handle Big Data, as it subsequently becomes bigger else, the larger data will be rendered useless.
