From citizens to government policy-makers: Social media data analysis

A social media Data-Driven analysis for transport policy response to the COVID-19 pandemic outbreak in Wuhan, China

Non-pharmacological interventions (NPI) such as social distancing and lockdown are essential in preventing and controlling emerging pandemic outbreaks. Many countries worldwide implemented lockdowns during the COVID-19 outbreaks. However, due to the lack of prior experience and knowledge about the pandemic, it is challenging to deal with short-term polices decision-making due to the highly stochastic and dynamic nature of the COVID-19. Thus, there is a need for the exploration of policy decision analysis to help agencies to adjust their current policies and adopt quickly. In this study, an analytical methodology is developed to analysis urban transport policy response for pandemic control based on social media data. Compared to traditional surveys or interviews, social media can provide timely data based on the feedback from public in terms of public demands, opinions, and acceptance of policy implementations. In particular, a sentiment-aware pre-trained language model is fine-tuned for sentiment analysis of policy. The Latent Dirichlet Allocation (LDA) model is used to classify documents, e.g., posts collected from social media, into specific topics in an unsupervised manner. Then, entropy weights method (EWM) is used to extract public policy demands based on the classified topics. Meanwhile, a Jaccard distance-based approach is proposed to conduct the response analysis of policy adjustments. A retrospective analysis of transport policies during the COVID-19 pandemic in Wuhan, China is presented using the developed methodology. The results show that the developed policymaking support methodology can be an effective tool to evaluate the acceptance of anti-pandemic policies from the public's perspective, to assess the balance between policies and people's demands, and to further perform the response analysis of a series of policy adjustments based on online feedback.

Quantified multidimensional public sentiment characteristics on social media for public opinion management: Evidence from the COVID-19 pandemic

Background

Public sentiments arising from public opinion communication pose a serious psychological risk to public and interfere the communication of nonpharmacological intervention information during the COVID-19 pandemic. Problems caused by public sentiments need to be timely addressed and resolved to support public opinion management.

Objective

This study aims to investigate the quantified multidimensional public sentiments characteristics for helping solve the public sentiments issues and strengthen public opinion management.

Methods

This study collected the user interaction data from the Weibo platform, including 73,604 Weibo posts and 1,811,703 Weibo comments. Deep learning based on pretraining model, topics clustering and correlation analysis were used to conduct quantitative analysis on time series characteristics, content-based characteristics and audience response characteristics of public sentiments in public opinion during the pandemic.

Results

The research findings were as follows: first, public sentiments erupted after priming, and the time series of public sentiments had window periods. Second, public sentiments were related to public discussion topics. The more negative the audience sentiments were, the more deeply the public participated in public discussions. Third, audience sentiments were independent of Weibo posts and user attributes, the steering role of opinion leaders was invalid in changing audience sentiments.

Discussion

Since the COVID-19 pandemic, there has been an increasing demand for public opinion management on social media. Our study on the quantified multidimensional public sentiments characteristics is one of the methodological contributions to reinforce public opinion management from a practical perspective.

Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications

Negation and speculation are universal linguistic phenomena that affect the performance of Natural Language Processing (NLP) applications, such as those for opinion mining and information retrieval, especially in biomedical data. In this article, we review the corpora annotated with negation and speculation in various natural languages and domains. Furthermore, we discuss the ongoing research into recent rule-based, supervised, and transfer learning techniques for the detection of negating and speculative content. Many English corpora for various domains are now annotated with negation and speculation; moreover, the availability of annotated corpora in other languages has started to increase. However, this growth is insufficient to address these important phenomena in languages with limited resources. The use of cross-lingual models and translation of the well-known languages are acceptable alternatives. We also highlight the lack of consistent annotation guidelines and the shortcomings of the existing techniques, and suggest alternatives that may speed up progress in this research direction. Adding more syntactic features may alleviate the limitations of the existing techniques, such as cue ambiguity and detecting the discontinuous scopes. In some NLP applications, inclusion of a system that is negation- and speculation-aware improves performance, yet this aspect is still not addressed or considered an essential step.

Identification of research trends in emerging technologies implementation on public services using text mining analysis

Purpose

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

Design/methodology/approach

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

Findings

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

Practical implications

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

Social implications

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Originality/value

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.

Digital government transformation in turbulent times: Responses, challenges, and future direction

We are living in turbulent times, with the threats of COVID-19 and related social conflicts. Digital transformation is not an option but a necessity for governments to respond to these crises. It has become imperative for governments worldwide to enhance their capacity to strategically use emerging digital technologies and develop innovative digital public services to confront and overcome the pandemic. With the rapid development of digital technologies, digital government transformation (DGT) has been legitimated in response to the pandemic, contributing to innovative efficacy, but it also has created a set of challenges, dilemmas, paradoxes, and ambiguities. This special issue's primary motive is to comprehensively discuss the promises and challenges DGT presents. It focuses on the nature of the problems and the dilemmatic situation in which to use the technologies. Furthermore, it covers government capacity and policy implications for managerial and institutional reforms to respond to the threats and the uncertainty caused by disruptive digitalization in many countries. To stimulate discussion of the theme of this special issue, this editorial note provides an overview of previous literature on DGT as a controlling measure of the pandemic and the future direction of research and practice on DGT.

Quantitative Evaluation of Waste Separation Management Policies in the Yangtze River Delta Based on the PMC Index Model

Numerous policies have been formulated and implemented to strengthen waste separation management activities in many countries. Waste separation management policies (WSMPs) must be evaluated as the precondition for reducing deviations from policy implementation and improving waste separation performance. Based on text mining technology and the construction of a policy modeling consistency (PMC) index model, we conducted a quantitative evaluation of 22 WSMPs issued by central governmental departments and provinces in the Yangtze River Delta, China from 2013 to 2021 and analyzed their optimization paths. The results suggest that the PMC index of the selected WSMPs has an upward trend. The average PMC index of 22 WSMPs was 6.906, indicating good quality in the policy texts. The PMC index identified seven, nine, five, and one of the policies as being perfect, excellent, good, and acceptable, respectively. The characteristics of WSMPs were further illustrated through PMC surface charts. Based on this, optimization paths for WSMPs with lower PMC indexes are proposed, which indicate that existing WSMPs have great potential for optimization in terms of harsher constraint regulations, context-appropriate incentives, and cultivation of market participants. Finally, this study provides a beneficial reference for similar cities or countries to improve their performance in the management of waste separation and environmental protection.

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

COVID-19 pandemic has caused a global health crisis, resulting in endless efforts to reduce infections, fatalities, and therapies to mitigate its after-effects. Currently, large and fast-paced vaccination campaigns are in the process to reduce COVID-19 infection and fatality risks. Despite recommendations from governments and medical experts, people show conceptions and perceptions regarding vaccination risks and share their views on social media platforms. Such opinions can be analyzed to determine social trends and devise policies to increase vaccination acceptance. In this regard, this study proposes a methodology for analyzing the global perceptions and perspectives towards COVID-19 vaccination using a worldwide Twitter dataset. The study relies on two techniques to analyze the sentiments: natural language processing and machine learning. To evaluate the performance of the different lexicon-based methods, different machine and deep learning models are studied. In addition, for sentiment classification, the proposed ensemble model named long short-term memory-gated recurrent neural network (LSTM-GRNN) is a combination of LSTM, gated recurrent unit, and recurrent neural networks. Results suggest that the TextBlob shows better results as compared to VADER and AFINN. The proposed LSTM-GRNN shows superior performance with a 95% accuracy and outperforms both machine and deep learning models. Performance analysis with state-of-the-art models proves the significance of the LSTM-GRNN for sentiment analysis.

Lessons for science and technology policy? Probing the Linkedin network of an RDI organisation

In this paper, we seek to examine the network of the Greek National Documentation Centre (EKT) as formed by its LinkedIn followers. By applying specific data collection and processing techniques, we explore the network of all the individuals that follow EKT’s LinkedIn page. Significant manual and automatic approaches have been implemented with regard to data extraction, data curation and data homogenization. The aim is to identify the network’s advancement over time, the institutions involved and the countries. The timeframe of the study spans from when the relevant LinkedIn page was constructed in 2015 to 2020. Findings indicate that there is a steady increase in the number of new followers, peaking in 2020. On an international scale, the evolution of the network of followers is imprinted and distributed in worldwide maps. In total, 68 countries have followed EKT over the examined time period. Also, in terms of followers’ institutional sector the Business Sector (BES) stands out (46.5%). Higher Education (HES) and Government Sector (GOV) are associated with 26.4 and 22.2% of the followers, respectively. Lastly, this paper provides a first institutional and country-level mapping of who constitutes the organisation’s interlocutors in the national and global RDI ecosystem.

Evaluating water quality using social media and federal agency data

United States Environmental Protection Agency (USEPA) drinking water violation report is currently one of the most reliable measures of evaluating United States drinking water quality. While states continuously strive to comply with federal water quality standards making this documentation continuously relevant, consumers are likely to perceive water quality through sensory aesthetics or physical and virtual social networks. This research quantifies the relationship between consumer perceptions and government-reported drinking water quality to provide insights to state water managers and policymakers. We evaluated consumer perceptions of tap water using weekly social media data. The online search returned 898,709 mentions and 799,035 posts. Net sentiment, measured as the number of negative posts minus the number of positive posts divided by the number of posts expressing sentiment, was determined and ranged from -100 to 100. Net sentiment was uncorrelated with USEPA weekly water quality violations for most states. Net sentiment was correlated with violations related to arsenic standards (-0.223) and a total number of violations (-0.220) for Washington. For California, net sentiment was correlated with violations related to disinfectants and other organic compounds (-0.295). In many cases, water violations in one city became national news, which eclipsed local water issues circulating on social media.

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions.

A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology

Background

South Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends.

Objective

The aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues.

Methods

An ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea’s Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Protégé (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals.

Results

A determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future.

Conclusions

The determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility.

Modeling Impact of Word of Mouth and E-Government on Online Social Presence during COVID-19 Outbreak: A Multi-Mediation Approach

Although social presence plays an essential role under general conditions, its role becomes significant for societal protection during the quarantine period in epidemic outbreak. In this study, we attempted to identify the role of E-government and COVID-19 word of mouth in terms of their direct impact on online social presence during the outbreak as well as their impacts mediated by epidemic protection and attitudes toward epidemic outbreaks. For this purpose, a unique multi-mediation model is proposed to provide a new direction for research in the field of epidemic outbreaks and their control. Through random sampling, an online survey was conducted and data from 683participants were analyzed. Partial least squares structural equation modeling was used to test the relationships between the variables of interest. The study results revealed that the roles of E-government and COVID-19 word of mouth are positively related to online social presence during the outbreak. Epidemic protection and attitude toward epidemic outbreak were found to positively moderate the impact of the role of E-government and COVID-19 word of mouth on online social presence during the outbreak. The key findings of this study have both practical and academic implications.

Qualitative analysis to determine decision-makers’ attitudes towards e-government services in a De-Facto state

Purpose

The manner in which people, businesses and governments perform is changing because of the spread of technology. Digitalization of governments can be considered a necessity as we are now entering the era of the Internet-of-Things. The advantages and disadvantages of electronic governments have been examined in several research studies. This study aims to examine the attitudes of decision-makers towards e-government. The research aims are as follows: to determine the problems related with e-government usage, to establish the factors which decrease the usage of e-government services and to propose recommendations for the effective application of e-government practices.

Design/methodology/approach

Qualitative research has been used for the study. Participants were chosen by the snowball sampling method, and face-to-face in-depth interviews were conducted with all decision-makers. In-depth interviews are more efficient and enable the acquisition of better qualitative information, in-depth knowledge and statistics, as the distance between the interviewer and interviewee is reduced (Stokes and Bergin, 2006). Questions asked can be categorized under two sections, where the questions in the first section are related to the decision-maker’s management style/managerial proposition, and in the second section, technological questions are asked in terms of the preferred communication method and the decision-makers’ attitudes towards e-government practices.

Findings

Decision-makers perceive electronic government to be important, while the level of importance is observed to be different among the decision-makers. Chronic problems exist in many countries, such as nepotism, where the decision-makers have conflicting arguments about e-government and the resulting effect on nepotism. Furthermore, the study also indicates that decision-makers are aware of the importance of mobile government, although they acknowledge that more time is required, as their country is still developing. Electronic voting is also perceived to be important, although the decision-makers believe that security and privacy issues need to be solved before related projects can be initiated.

Originality/value

This research can be a benchmark study for the decision-makers of small island developing states by means of e-government. The impediments preventing the effective application of e-government practices are also discussed in the study. This study will be useful to highlight the triggers and obstacles for e-government development in the context of a developing country. Internet penetration has increased significantly since the 2000s, and therefore, decision-makers need to consider the shift in citizens’ behaviour, such as the high usage of smartphones and the emergence of the Internet-of-Things (Kaya and Bicen, 2016; Kumar et al., 2017).