Examining Public Concerns and Attitudes toward Unfair Events Involving Elderly Travelers during the COVID-19 Pandemic Using Weibo Data

Abstract

The Chinese government has launched a digital health code system to detect people potentially exposed to the coronavirus 2019 (COVID-19) disease and to curb its spread. Citizens are required to show the health code on their smartphones when using public transport. However, many seniors are not allowed to use public transport due to their difficulties in obtaining health codes, leading to widespread debates about these unfair events. Traditionally, public perceptions and attitudes toward such unfair events are investigated using analytical methods based on interviews or questionnaires. This study crawled seven-month messages from Sina Weibo, the Chinese version of Twitter, and developed a hybrid approach integrating term-frequency–inverse-document-frequency, latent Dirichlet allocation, and sentiment classification. Results indicate that a rumor about the unfair treatment of elderly travelers triggered public concerns. Primary subjects of concern were the status quo of elderly travelers, the provision of transport services, and unfair event descriptions. Following the government’s responses, people still had negative attitudes toward transport services, while they became more positive about the status quo of elderly travelers. These findings will guide government authorities to explore new forms of automated social control and to improve transport policies in terms of equity and fairness in future pandemics.

Managing COVID‐19‐related knowledge: A smart cities perspective

“…Moreover, it has been claimed that our increasingly digital world has arguably “left behind” those in older generations who are still offline (BritainThinks, 2015 ). As a result of being not online or not having smartphones, many senior citizens around the world are becoming further disadvantaged due to their inability to access and use information and communication technologies (ITCs) (e.g., obtaining the digital health codes that have become an essential tool during the COVID‐19 pandemic) (X. Liu et al, 2021 ).…”

Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period—Temporal and Spatial Differences in Activity Topics

“…Liu et al [7] identified the five following topics in the Chinese Weibo discussion: "description of unfair events" (19.8%), "Provision of transportation events" (33.4%), "Status quo of elderly people" (33.9%), "Pandemic prevention" (7.3%), and "Government response" (5.6%). The proposed topics are specific and narrowly targeted.…”

“…The approach to semantic analysis might be supervised, e.g., by using predefined topics with a predefined set of words typical to these topics or unsupervised using topic modelling techniques, e.g., LDA [25]. For determining the relevance of tweets to predefined topics, machine learning techniques are often used, for example, Almohammad and Georgakis [2] used the Random Forest model [26] or TFIDF (Term-Frequency-Inverse-Document-Frequency) algorithm implemented by [7].…”

“…Currently, one of the major problems in this method of data collection is the separation of social groups by different types of active social media usage. Social exclusion of the elderly is traditionally documented [5][6][7], but differences in the number of users within each ageing adult population group decreases. While the older population is more active on older types of media, such as Facebook, generation Y and Z prefer Instagram, Tik-Tok and YouTube [8].…”

Safety of People with Special Needs in Public Transport

“…Modern systems of this type are also equipped with a hands-free car kit that can be activated by a blind person using a button. It is also worth noting that such modern boards can be very useful for deaf-mute people, because they can also provide current information on sudden changes in routes, or changes in the platform to which the expected train will arrive [51,52].…”