A smart city aims to become a citizen-centered city where citizens actively engage in urban operation and share urban data within a high-tech ICT infrastructure. It premises ‘smart citizens’ who can understand and utilize digital technology to adapt well to the various infrastructure of smart cities. In reality, however, not only tech-savvy citizens but those who are not likely to coexist in a smart city. Disadvantaged groups with relatively insufficient awareness and the necessity of technologies might be excluded from smart cities’ various benefits. The smart city innovations need to encompass diverse social groups’ engagements to claim their legitimacy and sustainability from a long-term perspective. In this light, this study examines the potential digital divide in the advanced technologies used in the emerging 5G smart city era. Using survey data, we investigate if the smart citizens’ social and technical readiness in terms of the use experience and necessity of new technologies can predict the adoption and use of the emerging 5G smart city innovations. The empirical results from this study can inform the digital divide between the general public and technology-disadvantaged groups. The findings can also guide policymakers in prioritizing technologies that are accessible and beneficial to all potential residents of smart cities in the future. Finally, the results yield specific policy implications for practitioners who design more inclusive and sustainable smart cities in the 5G era.

An important task of public health officials is to keep track of health issues, such as spreading epidemics. In this paper, we are addressing the issue of spreading public concern about epidemics. Public concern about a communicable disease can be seen as a problem of its own. Keeping track of trends in concern about public health and identifying peaks of public concern are therefore crucial tasks. However, monitoring public health concerns is not only expensive with traditional surveillance systems, but also suffers from limited coverage and significant delays. To address these problems, we are using Twitter messages, which are available free of cost, are generated world-wide, and are posted in real time. We are measuring public concern using a two-step sentiment classification approach. In the first step, we distinguish Personal tweets from News (i.e., Non-Personal) tweets. In the second step, we further separate Personal Negative from Personal Non-Negative tweets. Both these steps consist themselves of two sub-steps. In the first sub-step (of both steps), our programs automatically generate training data using an emotion-oriented, clue-based method. In the second sub-step, we are training and testing three different Machine Learning (ML) models with the training data from the first sub-step; this allows us to determine the best ML model for different datasets. Furthermore, we are testing the already trained ML models with a human annotated, disjoint dataset. Based on the number of tweets classified as Personal Negative, we compute a Measure of Concern (MOC) and a timeline of the MOC. We attempt to correlate peaks of the MOC timeline to peaks of the News (Non-Personal) timeline. Our best accuracy results are achieved using the two-step method with a Naïve Bayes classifier for the Epidemic domain (six datasets) and the Mental Health domain (three datasets).

An important task of public health officials is to keep track of spreading epidemics, and the locations and speed with which they appear. Furthermore, there is interest in understanding how concerned the population is about a disease outbreak. Twitter can serve as an important data source to provide this information in real time. In this paper, we focus on sentiment classification of Twitter messages to measure the Degree of Concern (DOC) of the Twitter users. In order to achieve this goal, we develop a novel two-step sentiment classification workflow to automatically identify personal tweets and negative tweets. Based on this workflow, we present an Epidemic Sentiment Monitoring System (ESMOS) that provides tools for visualizing Twitter users' concern towards different diseases. The visual concern map and chart in ESMOS can help public health officials to identify the progression and peaks of concern for a disease in space and time, so that appropriate preventive actions can be taken. The DOC measure is based on the sentiment-based classifications. We compare clue-based and different Machine Learning methods to classify sentiments of Twitter users regarding diseases, first into personal and neutral tweets and then into negative from neutral personal tweets. In our experiments, Multinomial Naïve Bayes achieved overall the best results and took significantly less time to build the classifier than other methods.