Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System

A Study of Public Attitudes toward Shanghai's Image under the Influence of COVID-19: Evidence from Comments on Sina Weibo

With the advent of the Internet era, Chinese users tend to choose to express their opinions on social media platforms represented by Sina Weibo. The changes in people's emotions toward cities from the microblogging texts can reflect the image of cities presented on mainstream social media, and thus target a good image of cities. In this paper, we collected microblog data containing "Shanghai" from 1 January 2019 to 1 September 2022 by Python technology, and we used three methods: Term Frequency-Inverse Document Frequency keyword statistics, Latent Dirichlet Allocation theme model construction, and sentiment analysis by Zhiwang Sentiment Dictionary. We also explore the impact of the COVID-19 epidemic on Shanghai's urban image in the context of the "Shanghai Territorial Static Management", an important public opinion topic during the COVID-19 epidemic. The results of the study show that the "Shanghai-wide static management" of COVID-19 epidemic has significantly reduced the public's perception of Shanghai and negatively affected the city's image. By analyzing the data results, we summarize the basic characteristics of Shanghai's city image and provide strategies for communicating Shanghai's city image in the post-epidemic era.

Evaluating the public acceptance of sustainable mobility interventions responding to Covid-19: The case of the Great Walk of Athens and the importance of citizen engagement

COVID-19, the most wide-spread and disruptive pandemic in over a century, enforced emergency urban design responses meaning to recalibrate transport provision globally. This is the first work that systematically evaluates the 'public acceptance' as a proxy for 'policy success' and 'potential for longer-term viability' of the high-profile sustainable transport intervention package introduced in 2020 in the capital city of Greece known as the Great Walk of Athens (GWA). This is achieved through a twin statistical analysis of an e-survey that looked into the attitudes and urban mobility experiences of Athenians accessing the area of the trial daily. The research enabled a comparison between the pre- and post-implementation traffic situations and provided details about specific measures packaged in the GWA project. Our results suggest that walking and cycling uptake were only marginally improved. Traffic delays for car users were considerable. Car usage declined somewhat, with the exception of ride-sharing. Public transport ridership numbers suffered a lot because of concerns about sharing closed space with many others during a pandemic. Men and people on low income were more likely to agree with the 'change'. Naturally this was the case for people identified as primarily cyclists and pedestrians. The most impactful package elements in terms of car lane sacrifices (i.e., the redevelopment of Panepistimiou Street) had the lowest acceptability rates. A key reason that underpinned people's hesitation to approve the GWA initiative was the lack of public consultation in the decision-making that shaped the project. Our study provides evidence-based generalisable lessons for similar metropolitan environments looking to implement more or evaluate for possibly making permanent 'rushed' anti-Covid street redevelopment measures.

The impact of Covid-19 pandemic on public transit use: Case study of Konya city

Since March 2019, Turkey has been enforcing various measures on the policies based on the trends in the COVID-19 cases. To restrain the spread of virus, policies limiting the mobility of people (i.e. lock downs, remote working and travel bans) were applied as in many other countries. Furthermore, social distancing calls for health concern directly caused a major reduction in public transit (PT) use. However, economic activities and new normal conditions required return of a part of the commute travels, which brought the issue of use of PT modes. This study focuses on the comparison of the PT mobility during the month of April in the 2019 (pre-pandemic), in 2020 during restrictions and in 2021 under new normal condition using the Smart Card (SC) data in Konya, Turkey. Monthly, daily and hourly distribution of ridership patterns are compared as well as usage patterns and characteristics of different bus lines are examined in detail. The results suggested that during the restrictions, the ridership was about one eight of the pre-pandemic periods, while it increased to 2.5 million ridership in 2021 which is still very low. Daily ridership in 2020 showed no PT mobility due to lockdowns, while during weekdays, hourly ridership distributions were changed parallel to changes in the work/education activity schedules. Evaluation of the bus lines having highest ridership in 2019 with 2020 and 2021 showed some of the bus lines were cancelled during the pandemic and routes/frequencies changed. The results showed the importance of PT management during pandemic which is very challenging due to economic loss and fear of infection by public. However, it should be emphasized the importance of continuation of public transportation in terms of accessibility and equity for all.

Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre- and Peri-COVID-19 Pandemic Retrospective Study

BACKGROUND

The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior.

OBJECTIVE

In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19.

METHODS

We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression.

RESULTS

We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval.

CONCLUSIONS

The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep.

Using Maps to Boost the Urban Proximity: Analysis of the Location of Public Facilities According to the Criteria of the Spanish Urban Agenda

Developing analysis models that promote the sustainability, compactness and social balance of cities is particularly important in addressing post-pandemic urban planning. In this context, the population’s proximity to public facilities is essential for achieving these objectives. Based on this framework, this paper analyses the city of Valladolid (Spain) under the criteria of distance between the population and public facilities proposed by the Spanish Urban Agenda. Specifically, the focus is on calculating the coverage of population with access to the facilities within the recommended distance thresholds using GIS techniques. The methods used relate the facilities with the distance to the population in the census sections, a highly detailed statistical unit. The results have been mapped as a decision making support tool for the city, and show how general coverage of access to facilities for the urban area as a whole is adequate, especially in terms of public transport services, and meets the recommendations of the Spanish Urban Agenda. Maps also reveal how some areas of the city are not covered by most public facilities.

Travel Behavior before and during the COVID-19 Pandemic in Brazil: Mobility Changes and Transport Policies for a Sustainable Transportation System in the Post-Pandemic Period

This article was motivated by the urban mobility changes observed at the onset of the COVID-19 pandemic in Brazil. We aim to analyze travel behavior before and during the COVID-19 pandemic in Brazil considering two samples of revealed preference online data, independent samples tests, multinomial logit models (MNL), and mixed logit models (ML). The analysis shows a decrease in Urban Public Transport (UPT) use. Comfort and frequency of the UPT service were important factors to attract users during the pandemic period. Ridesourcing services were used for leisure purposes before the pandemic. During the pandemic, they were used for health purposes. Active modes were used more for shopping and leisure purposes during the pandemic. Regarding car users, such as drivers, it was found that they used ridesourcing less often during the pandemic than before. The main contribution of this research concerns the changes in travel behavior that might remain and how these analyses can shape sustainable transportation public policies in the future. Therefore, for a Brazilian study case, this article suggests an increase in the quality of UPT services, a reform on pricing regulations for UPT, an increase in the infrastructure for active modes, an implementation of car demand management strategies, and more strategies to support teleworking as a form of traffic demand management.