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Social Media Behaviors in Asia

Problem Analysis

Asia is a very diverse region with lots of nationalities and cultural traditions. In a globalized world, Asia started to play one of the major roles in shaping markets due to its immense productive powers. China, India, Singapore, Japan, South Korea, and other countries pour goods and services to the international market. A large portion of that trade happens through the means of the Internet. Asia has a huge number of active internet users. According to statistics, South East Asia accounts for 300 million of active users alone (Hollander, 2017). The market share of internet businesses is estimated at 50 billion dollars and shows rapid growth (Hollander, 2017). In China, there are more than 500 million active users. All this makes Asia an outstanding market for internet sales. Social media (SM) occupy a significant part of the time people spend on the Internet. According to RVC, about 42% of the population in China alone use local social media networks such as Weibo, QQ, and Qzone (RVC, 2016).

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The standing problem is with capturing and analyzing trends social media usage in Asia. Different users can exhibit different behavior, which needs to be studied and used for better product marketing strategy for Asian regions (Cheng, Liang, & Leung, 2015). As such, scientists and research centers claim that there is a constant need for fresh data on social media usage as trends on the Internet are changing rapidly (Boulianne, 2015; Poushter, 2016). Therefore, the present study will be explorative in nature and try to capture general behaviors of Asian people on social media platforms. The research question is as follows: What are the users’ favorable behaviors in different social media platforms in different countries in Asia, and how can it be applied to develop a better marketing strategy?

Overall Research Design

The major research designs are correlational, descriptive, experimental, systematic review and meta-analysis. A correlational study is aimed to define the fact and, possibly, nature between two variables. This design does not seem to fit too well for the current objective of this study. At present, two specific variables needed for this type of research are not identified. Therefore, there is no need for correlational study. Experimental studies such as filed experiment are majorly used to predict and measure the effect of a certain notion on a specific population. Since the present study focuses on identifying the phenomena that may be linked to a population, there is no specific baseline information that could require semi-experimental design.

Systematic review and meta-analysis are both aimed at studying literary sources to identify patterns in a specific research area, summarize results, measure the research progress, and so on. This design is rather useful in organizing research data and identifying new areas to study. Yet it adds almost no new information. This is why this research design cannot be applied in the present project. The aim here is to update previous information and find new patterns of behavior among the users of social media platforms.

Descriptive studies focus on observation and finding patterns or characteristics in a particular population. This research design is frequently used when the area of study or population is largely under-researched or when the author needs to outline a basis for further, more focused study of a separate phenomenon (Shields & Rangarajan, 2013). This type of research design appears to fit the present research question as the study will be exploratory in nature and there is no specific concept that needs to be analyzed. In addition, the descriptive study also produces results through analysis of statistic data, which is going to be generated in this research.

Variables and Measurements

The key variables in this research are types of social media platform used, type of content, sharing, likes, and comments (or subscribers). The independent variables will be the type of content and social media platform. The research will focus on what platforms are typically used in different Asian countries and what content usually gathers the most powerful response from users. The response will consist of sharing, tagging, and comments. By quantitatively measuring the dependent variables such as numbers of shares, likes, and comments it will be possible to identify specific behaviors in SM users (Parveen, Jaafar, & Ainin, 2015).

The type of content can be divided into several groups such as recreational (with sub-divisions such as videos, text, images), social (help requests, lost and found, etc.), political (announcements, world, and domestic news), commercial (advertisements of all sorts). This independent variable can be analyzed through frequency and percentage of dependent response variables in comparison to other groups of content (Jalil, 2013).

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Social media platform can be measured by the number of daily active users and compared to other platforms. All the dependent variables across platforms that are going to be used in the study will be gathered separately. Type of content will also be measured separately at each SM platform typical for an Asian country in question. Dependent variables such as likes, shares, and comments will be gathered through observation and measured quantitatively (documenting the exact numbers of each under a specific type of content). All quantitative data will be analyzed through SPSS statistics tool to define mean, mode, and standard deviation.


In the social media research that will be conducted specific people cannot be taken as research subjects. Instead, a collective image of people’s behavior gathered through likes, shares, and comments (Zafarani & Liu, 2014). Therefore, the sample will consist of a certain number of posts with a specific type of content. In order to produce statistically viable results, there is a need to analyze as much data as possible. Again, due to the time limit, the research will realistically include 20 posts of each content type in 3 major Asian local social media platforms such as Weibo (China), Line (Japan), and KakaoTalk (South Korea). In these networks, people can share posts, like and comment content. In addition, there are tools for viewing the number of members of different channels. It would be easier to gather a sample data on Facebook, Twitter, etc. Yet the in a major Asian country such as China, these SM networks are blocked by the government. In addition, there appear to be no tools in Facebook to sort posts, likes, and shares in accordance with a chosen region. Therefore, the sample should be drawn from local SM platforms.

All in all, the total sample will include 80 (20 – recreational, 20 – politics-related, 20 – social, and 20 – commercial) posts or messages in public chats. Each group should include the same or approximately the same number of posts or messages in order to establish sample validity and enhance the statistical significance of the analysis. The total number of posts that the author is able to analyze is limited by the time allocated for the project. Generally, the bigger the sample size is, the more generalizable the results become (Martínez-Mesa, González-Chica, Bastos, Bonamigo, & Duquia, 2014). Ideally, the research sample should be quantitatively related to the target population, which is the total amount of users in these three Asian networks (Martínez-Mesa et al., 2014). The author cannot possibly measure even one percent of the content generated by millions of users to establish statistically viable and generalizable behavioral patterns. Yet, the sample chosen should be enough to be used as a preliminary outline for bigger studies.

In order to access the sample, which consists from posts and messages, there is a need to access the messaging platforms themselves. Weibo, Line, and KakaoTalk are available for free download at App Store for iPhones, and Google Play for Android smartphones (Cheng et al., 2015). The messengers are also available in desktop versions that can be downloaded from official websites. The next step to accessing posts and messages will be to enter group chats or channels where content for mass users is produced. In case of Weibo (an analog of Facebook), there is a need to register a profile and enter a community that produces one of four types of content needed for the purposes of this research. The difficulty is that Weibo, unlike other apps does not apparently have an English version. Therefore, Google translate will be used to facilitate the access to the sample.

Ethical Issues

The current research appears to be free of ethical issues generally related to studies that measure behavioral patterns. Since the sample consists not of actual users, it does not require their consent to participate. The study will be mainly observational and use the data that is free from copyright. The posts and messages to be analyzed are placed by users or group administrators of their own volition to the digital space of the platform. The ownership rights will not be violated as the study will not discuss the content or include it in any form to the text of research. As per quantitative response metrics for posts and messages, this information is considered open-source and does not require a permission to be used in research.

To establish precautionary measures against copyright laws, in the discussion of the sample and results of the analysis, the author will blur the screenshots of the posts or messages in order not to display content. Blurring or otherwise protecting the information from being recognized by third parties is also essential in the process of gathering and describing advertisement posts. Identity protection will not likely become an issue. However, certain applications display portraits and names of the users who shared or commented the post or message the last. In these situations, there is a need for identity protection. Gathering consent in each of those cases will not be an option due to a large number of posts that will be chosen for analysis. In this case, it will also be of use to apply blurring to screenshots, making names and faces of users not recognizable. Such approach will relieve the author from unnecessary privacy or copyright laws violation.


Boulianne, S. (2015). Social media use and participation: A meta-analysis of current research. Information, Communication & Society, 18(5), 524-538.

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Cheng, Y., Liang, J., & Leung, L. (2015). Social network service use on mobile devices: An examination of gratifications, civic attitudes and civic engagement in China. New Media & Society, 17(7), 1096-1116.

Hollander, R. (2017). Southeast Asia could be a leader in mobile internet usage next year. Business Insider. Web.

Jalil, M. M. (2013). Practical Guidelines for Conducting Research – Summarising Good Research Practice in Line with the DCED Standard (SSRN Scholarly Paper No. ID 2591803). Rochester, NY: Social Science Research Network. Web.

Martínez-Mesa, J., González-Chica, D. A., Bastos, J. L., Bonamigo, R. R., & Duquia, R. P. (2014). Sample size: how many participants do I need in my research? Anais Brasileiros de Dermatologia, 89(4), 609–615.

Parveen, F., Jaafar, N. I., & Ainin, S. (2015). Social media usage and organizational performance: Reflections of Malaysian social media managers. Telematics and Informatics, 32(1), 67-78.

Poushter, J. (2016). Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies. Pew Research Center, 22. 1-30.

RVC. (2016). The state of social media and messaging in Asia Pacific: Trends and statistics. Web.

Shields, P. & Rangarajan, N. (2013). A playbook for research methods: Integrating conceptual frameworks and project management. Stillwater, OK: New Forums Press.

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Zafarani, R., & Liu, H. (2014). Behavior Analysis in Social Media. IEEE Intelligent Systems, 29(4), 1-4.

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