Keywords

1 Introduction

Under the normalized situation of the prevention and control of COVID-19, news about the epidemic often occupies the hot search list of major Chinese websites. As the main force of the network, the self-expression of university students in the network is very likely to trigger the university network public opinion. In this context, it is important for university administrators to grasp the right of online discourse to guide the direction of online public opinion and maintain social stability.

Related scholars in China have conducted research in terms of opinion leaders and controllers of online discourse, and formed a map of online discourse control, in which algorithms are studied and aided by simulation experiments for verification. Fang Wei et al. [1], Wang Ping [2] and Liu Xiaobo [3] conducted theoretical and simulation simulation experimental research on the formation and evolution mechanism of online public opinion. Jiang Kan et al. [4], CHEN Yuan et al. [5], and Wang Zheng [6] conducted studies on the influence exerted by opinion leaders in online public opinion. Zeng Runxi [7] did studies on how opinion managers conduct online opinion guidance. Fu Zhuojing et al. [8, 9] and Wang Huancheng [10] made studies on improving the monitoring mechanism of online public opinion and grasping the right to master the discourse of public opinion guidance in universities.

Different studies have recognized the role that administrators play in online public opinion, so how specifically can we, as university administrators, master online discourse in the new situation where epidemics are normalized? In this paper, we will conduct simulation experiments based on survey data and previous studies to come up with targeted countermeasures.

2 The Questionnaire Survey

In mid-December 2020, we conducted a survey for college students in six universities in Shanghai. The survey focused on understanding the impact of the Internet on students’ study and life on campus during the epidemic. 351 people participated in the survey, with education levels involving senior, college, bachelor, master and doctoral degrees, and majors covering science and technology, arts, economics, management, law and medicine. The survey shows that as high as 89.17% of students choose to go online, and the Internet is more closely connected with the study and life of college students.

2.1 Mainstream Media Show Authority

The survey showed that at the beginning of the emergence of COVID-19, students were easily confused by the Internet rumors related to the epidemic, and only 35.5% of students did not have the experience of being confused. When there were more online rumors, 54.2% of students chose to actively search for relevant information, as many as 96.64% of students chose to clarify online rumors through official releases, 25.21% of students chose to clarify through online celebrities on social media platforms, 23.11% of students chose to clarify through teachers and parents, and 19.33% learned the truth through classroom learning. When the epidemic was more serious, 81.3% of students actively searched for relevant information, a figure that declined after the state released real-time developments of the epidemic. After the official release of the real-time news of the epidemic and the provision of a small platform for disinformation, up to 56.64% of students chose to stop believing the unofficial news forwarded by their friends and replaced it with the official news. As many as 72.9% of students trust the official information about the Newcastle Pneumonia outbreak, while only 0.27% of students do not trust it at all.

A whopping 79.67% of the respondents said that they browse social networking platforms multiple times a day. The main channel for students to get information about COVID-19 (multiple choices) was Weibo in the first place, accounting for 67.21%, followed by WeChat friend circle 57.72%, mainstream media public number 55.83% in the third place, mainstream media microblog 49.05% in the fourth place, and only 16.26% got the information through classroom. Mainstream media public numbers and mainstream media microblogs are the best channels for students to get authoritative information related to the epidemic.

2.2 Proactive Screening and Careful Forwarding

The survey showed that 69.65% of students had half-confidence in the authenticity and credibility of the unofficial information about the Newcastle pneumonia outbreak. Only 5.96% of students believe it completely, and even if they believe it completely or partially, the proportion of students who would forward it is only 38.35%. Up to 74.07% of students would choose to use online engines to search authoritative websites to get authoritative information; followed by finding answers from the news, accounting for 59.6%; at the bottom of the list is communicating with teachers of professional courses, accounting for only 12.12%, with more specialist, undergraduate and doctoral students choosing to communicate with their teachers. If university administrators can forward authoritative information immediately can control online rumors from the source of information, which is more helpful to prevent online public opinion.

A whopping 39.92% of students said that the school’s interpretation of relevant policies could ease their anxiety about the epidemic, and another whopping 47.29% said they would actively open news about the epidemic shared by their teachers in their class groups, a percentage second only to students who would actively view news with authoritative experts expressing their professional opinions (62.96%) and news that made it to the top of the list (58.69%), and is higher than WeChat’s precisely placed public service videos (30.77%).

3 Simulation Experiments

The experiment is based on the Netlogo platform [11], combined with the Language Change model [12], and is built on the basis of the communication model proposed by Zhuojing Fu et al. [8, 9], adapted to test the effectiveness of different measures taken by university administrators to grasp online discourse and influence online public opinion.

3.1 Model Design

It is assumed that the online information dissemination space is a 99 × 99 square and that students are in this space forming a social network with some linking hubs in the network. The dots represent a student and the links represent the connections and communication channels between them. White dots (0) represent students who are able to transmit positive energy in their online participation, black dots (1) represent students with more negative online feelings, and grey dots (0.5) represent students in a neutral state. Nodes with connection lines greater than or equal to 5 are shown as larger key dots, and the network participants represented by these dots are network opinion leaders or special network connectors in an active position, such as moderators, followers of comments, etc.

The parameters of the experiment were set according to the survey results; 46.72% of the students feel anxious and upset about the epidemic, which can be interpreted as a corresponding percentage of nodes with a black negative state in the initial state. In each system operation cycle, 38.35% of the nodes will disseminate their state to their neighbors, 5.96% of the nodes fully receive and adjust to the incoming state; 69.65% of the nodes will half believe the received message, of which 74.07% choose to corroborate their judgment by searching for authoritative information; if there is no valid authoritative information released at this time, the experiment shows that there will be 46.72% of the nodes would choose to receive messages that they believed half-heartedly before.

Judging from surveys and past experience, there are two basic measures that can help college and university administrators capture online discourse.

Measure 1 (C1): by publishing official authoritative information across the network, it makes a lot of positive information available on mainstream media, and most (72.9%) of the nodes will accept the positive information after querying, and another 0.27% of students will not accept it at all. The variable C1 is set in this model, taking the value range 0–100%, and the proportion of positive information coverage on the network can reach the level of C1 after taking this measure C1 (assuming that the rest is invalid information).

Measure 2 (C2): focus on network opinion leaders (key nodes), targeted push, and timely push messages to other nodes. The switch C2 is set in this model and turning on C2 means starting to implement measure 2. The experiment is set to select the larger dot after every 5 system times, assign a positive status to that dot, and propagate the positive message to its neighbors.

3.2 Initial Experiments

Simulates the initial state without any measures, with C1 at 0% and C2 off.

The experimental run was started and after 45 system times (T), the negative messages covered all network nodes. Figure 1 shows the results of the experiment without any measures: the world view window shows all dots as black and the statistical curve shows that the node state mean reaches 0 at T = 45 (0 is black, 0.5 is gray, 1 is white).

The initial experimental results show that if university administrators do not take measures to intervene during the outbreak of online public opinion, it will lead to the rapid spread of negative information such as online rumors, and the online public opinion will be out of control in a short period of time.

3.3 Comparative Experiments

Comparative Experiment 1.

This experiment tests the effect of publishing authoritative information across the network. The other settings are the same as the initial experiment, and the C1 ratio is turned up to 10%, 20%, 50%, and 100% in that order and run for observation. Figure 2 shows the results of the experiment with measure 1. The results show that only measure 1 makes all the dots white, and the rate of change increases in tandem with the percentage of positive messages in C1, but the increase slows down after C1 exceeds 50%.

The results of Comparative Experiment 1 shows that if measures 1 are taken alone, university administrators can improve the psychological state of the student group in a short time by publishing official authoritative information and making students search for authoritative information on mainstream media (coverage does not have to be high) as soon as possible, thus effectively guiding the direction of online public opinion until positive information dominates the Internet.

Comparative Experiment 2.

This experiment tests the directed push of authority information to key nodes. The other settings are the same as the initial experiment, and the C2 switch is turned on and run for observation. Figure 3 shows the results of the experiment for Measure 2. After several effective runs, when the system time reaches above 200–300, most of the nodes show white; while when the system time reaches around 400 interval, only individual end small groups are left black, and sometimes the dots can all be converted to white.

Fig. 1.
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Scenario when no measures are taken

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Fig. 2.
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Results of a typical run of Comparative Experiment 1

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Fig. 3.
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Results of a typical run of Comparative Experiment 2

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The results of Comparative Experiment 2 shows that if measure 2 is taken alone, university administrators directed to influence key nodes to ensure that the information they disseminate to surrounding nodes is positive and timely, and can also positively guide the direction of online public opinion, however, measure 2 is not as efficient as measure 1, as reflected by the long time spent and the small range of groups covered.

3.4 Conclusions of the Experiments

The above experimental situation shows that if university administrators do not take any measures, online public opinion will quickly get out of control; whereas, if conditions permit, prioritizing measure 1 to popularize authoritative information among students in general will quickly control the direction of online public opinion. In the stage when authoritative information is not yet available and online public opinion begins to emerge, adopting Measure 2 to target and influence online opinion leaders or relevant online participants in an active position can be an effective supplement when Measure 1 cannot be taken.

4 Countermeasures and Suggestions

In the context of normalized epidemic prevention and control, the authority trusted by Chinese college students is the mainstream media, and students pay attention to the information about the epidemic and the interpretation of relevant policies forwarded by their schools. In the network public opinion that may break out at any time, university administrators should take this opportunity to grasp the guidance of public opinion and build a mechanism to prevent university network public opinion.

4.1 Leverage the Power of Authority

In the COVID-19 outbeak, the scientific study of the epidemic by the authoritative expert group greatly relieved the anxiety and panic of Chinese social groups; the mainstream media’s notification of the case situation shattered all kinds of rumors about the epidemic, and the opinion leaders and authoritative views showed a high degree of integration. Leveraging authority by university administrators is the most effective way to guide online public opinion.

4.2 Focus on the Key Points

Online public opinion on COVID-19 usually matches the time of case confirmation, and is the stage of rapid spread of online rumors and the budding of online public opinion when authoritative information has not yet been released. Experiments have shown that when authoritative information is not yet in play, voices can be raised with the help of online opinion leaders or active online participants. For university administrators, firstly, they should establish a network management team and occupy the position of active network participants; secondly, they should screen out negative emotion groups and lock the key pushing targets; thirdly, they should carry out accurate pushing of network information, including pushing network information that conveys positive energy and publishing positive comments in the comment section.