Since the outbreak ofCOVID-19, China has adopted comprehensive and strict prevention and control measures. Most of them have to stay at home and work, study and entertain through the Internet. During this period, no doubt that digital content distribution platforms claim most attention. Some data shows that the daily active users of digital content distribution platforms with a customized recommender system like Tik Tok and Mango IV have grown rapidly.
However, excessive use of these platforms will lead to problems such as addiction and the Filter Bubble.
Source: Baidu Pictures
Many students spend more than reasonable amount of time on the platforms unconsciously at home, sometimes at the expense of hindering learning progress, and the customized recommender system tends to confine users only to their favorite topics, limiting their chance of exploring more information. In the long run, the problems may harm people’s way of thinking and perception about the outside world. Therefore, it is high time that we should pay attention to the excessive use of digital content distribution platforms.
Digital content distribution platform
A digital content distribution platform provides users with digital content products through the Internet. These products mainly include news, music, and videos.
The customized recommender system
The concept of recommender systems was first proposed by Paul Resnick and Hal R. Varian as an information filtering technology in the Internet era, which can predict user preferences and supply information accordingly, saving users from information overload.
For online platforms, the recommender system will help maintain user adhesion since the quality information it provides not only increase uses’ dependence on the platforms, but also their loyalty. The system has been widely used in e-commerce platforms and digital content distribution platforms.
Factors accounting for the excessive use of digital content distribution platforms
There are many reasons for the excessive use of digital contentdistributionplatforms. Understanding the reasons will help us avoid this phenomenon. Hasan conducted an empirical study on the excessive use of online video streaming services and divided the factors affecting their excessive use into three major categories (motivation, psychological factors, and recommender systems)
(1) Motivation
Motivation allows users to meet expectations from devices or media, which will bring satisfaction. Existing studies divide motivation into three sub-factors: information search, recreation, and entertainment. Information search means that users tend to collect information by using platforms or media sources. Recreation refers to human activities that consume leisure time without an obvious aim. Entertainment refers to people’s needs to enjoy themselves.
(2) Psychological factors
As a source of information and entertainment, the use of video streaming sites is also susceptible to individual factors, such as their psychological characteristics. The most important psychological factors are self-control and self-esteem. Although self-control is an important factor to ensure that users avoid spending too much time on these platforms, self-esteem defines how people perceive their role in the society. People with poor self-control are likely to be excessively involved in video streaming services. Similarly, people with low self-esteem are more likely to overuse video streaming sites without peer pressure or judgment.
(3) Recommender systems
The recommender system reinforces its interaction with the platform by recommending interesting and relevant content to platform users, which also causes people to become addicted to the platform. This targeted content recommender can extend the use of the platform beyond the expected time limit.
Disadvantages of excessive use of digital content distribution platforms
Users often inadvertently loaf away their time on the platforms while they are enjoying the convenience and desirable experience brought by the recommender system. Their addiction then may lead to the spread of low-quality information content, causing a downside effect on the Network information content ecology. Furthermore, it will narrow down and simplify users’ cognition.
Conclusions and Suggestions
To make people aware of the excessive use of digital content distribution platforms, we introduced the concepts of digital content distribution platforms and customized recommenders, and pointed out the affecting factors and negative effects of excessive use. To cope with the excessive use of the platforms, we offer the following suggestions:
(1) For digital content distribution platform operators
Platform operators should further improve the algorithm of customized recommender systems and the product recommendation functions, provide as many diversified recommendations as possible, and avoid information narrowing caused by a single recommender.
At the same time, platform vendors should improve the check-up mechanism, strengthen content supervision, and consciously integrate diverse information content, and timely filter false and vulgar information that does not conform to mainstream values.
(2) For students who study at home
First, formulate a learning plan to ensure learning efficiency. Studying at home requires self-control. Students are advised to formulate a detailed daily study plan, complete it step by step, and try to balance their study, work and entertainment.
Second, avoid excessive playing and entertain themselves in diversified ways. Instead of resorting to the Internet, students can also settle to read books, and watch movies, etc.
JUNPENG GUO (guojp@tju.edu.cn) is a professor at the Department of Information Management and Management Science. He received his Ph.D. in management science and engineering from Tianjin University. His research interests are recommender systems, social media, and operation research. His work has appeared in Journal of Management Information Systems, Decision Support Systems, Information & Management and several other journals.
By the College of Management and Economics
Editor: Eva Yin
