Resilient Consensus for Expressed and Private Opinions

The formation pattern, causes, and governance of network public opinion on university emergencies

Background

University emergencies, garnering significant public attention and shaping network opinions, pose a crucial challenge to universities' management and societal stability. Hence, network public opinion on university emergencies is a vital issue. Nevertheless, the underlying mechanism has not been fully explored and cannot be efficiently controlled. This study aimed to explore the formation pattern of network public opinion on university emergencies, analyze its causes, and provide scientific governance strategies for coping with this issue.

Methods

Based on a sample set of 204 cases from the Zhiwei Data Sharing Platform, this study classifies network public opinion on university emergencies into six types and visually analyzes their characteristics: time distribution, subject, duration, and emotion. By integrating the theory of the network public opinion field, this study develops a network public opinion field model of university emergencies to reveal its formation pattern. Furthermore, it analyzes the causes of network public opinion on university emergencies from the perspective of the public opinion lifecycle and proposes corresponding governance strategies.

Results

The sample consisted of 304 cases of real-life public opinion, and the visualization results show that public opinion on mental health and teacher-student safety constitutes the predominant types, accounting for 83.3%. High-occurrence subjects are public universities (88.24%) and students (48%). The most frequent months are July and December. 90.20% of the public opinions have a lifespan of less than 19 days, with an impact index ranging from 40 to 80. The public's emotional response to different types of public opinion varies, with negative emotions dominating.

Conclusion

This study provides novel insights for understanding their formation and dissemination. It also provides practical implications for relevant departments to govern network public opinion on university emergencies.

A Comment on and Correction to: Opinion Dynamics in the Presence of Increasing Agreement Pressure

We identify counterexamples to the consensus result given in Semonsen et al. (2018). We resolve the counterexamples by replacing Lemma 5 in the given reference with a novel variation of the Banach fixed-point theorem, which explains both the numerical results in the reference and the counterexample(s) in this note and provides a sufficient condition for consensus in systems with increasing peer-pressure. This work is relevant for other papers that have used the proof technique from Semonsen et al. and establishes the veracity of their claims assuming the new sufficient condition.

A Generic Graph-Based Method for Flexible Aspect-Opinion Analysis of Complex Product Customer Feedback

Product design experts depend on online customer reviews as a source of insight to improve product design. Previous works used aspect-based sentiment analysis to extract insight from product reviews. However, their approaches for requirements elicitation are less flexible than traditional tools such as interviews and surveys. They require costly data labeling or pre-labeled datasets, lack domain knowledge integration, and focus more on sentiment classification than flexible aspect-opinion analysis. Related works lack effective mechanisms for probing the customer feedback of complex configurable products. This study proposes a generic graph-based opinion mining and analysis method for product design improvement. First, a customer feedback data preprocessing and annotation pipeline that can incorporate designer-specified domain knowledge is proposed. Second, an intuitive opinion-aware labeled property graph data model is designed to ingest preprocessed feedback data and perform ad hoc opinion analysis. Applying the generic model to a real-world dataset demonstrates superior functionality and flexibility compared to related works. A wider range of analyses is supported in a single model without repeating data preprocessing and modeling. Specifically, the proposed method supports regular and comparative aspect-opinion analysis, aspect satisfaction/influence ranking, opinion trend extraction, and targeted aspect-opinion summarization.

Opinion Formation on the Internet: The Influence of Personality, Network Structure, and Content on Sharing Messages Online

Today the majority of people uses online social networks not only to stay in contact with friends, but also to find information about relevant topics, or to spread information. While a lot of research has been conducted into opinion formation, only little is known about which factors influence whether a user of online social networks disseminates information or not. To answer this question, we created an agent-based model and simulated message spreading in social networks using a latent-process model. In our model, we varied four different content types, six different network types, and we varied between a model that includes a personality model for its agents and one that did not. We found that the network type has only a weak influence on the distribution of content, whereas the message type has a clear influence on how many users receive a message. Using a personality model helped achieved more realistic outcomes.