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

Driss, Olfa Belkahla; Mellouli, Sehl; Trabelsi, Zeineb

doi:10.1016/j.giq.2019.05.002

Cited by 84 publications

(34 citation statements)

References 30 publications

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“…People involved in policy-making are also inspired by social media [29]. Factors involved in sharing information are not described in this literature [30].…”

Section: Role Of E-govtmentioning

confidence: 99%

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

Yasir

Ahmad

et al. 2020

IJERPH

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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. health protection. This phenomenon encouraged us to formulate a theoretical model based on social presence theory and the role of social media to help examine safety and participation in quarantine. The conceptual model is presented in Figure 1.Word of mouth can be defined as "the intention to share a certain story using one's own social media account" [2]. Many researchers proved that communication has more of an impact through social media with regards to different health issues, and message sharing was found to be the best predictor of emotive and passionate response [3]. Online health information is regularly updated [4]. Nowadays, universal and worldwide flow cannot be ignored [5] and search engines are the most effective method to find information [6]. The Internet is also a useful source for people in the medical field for discussions and interactions about medical issues [7]. A nine-year big data study showed that the use of computers projected emotive improvement [8]. From the health protection point of view, many factors were found to affect the dispersal of cancer information, including anxiety, courage, anticipation, and sharing of experience [9].A study proved that the gestation time of infection (corona virus) was assessed at about 2-14 days [7]. So, a research question was raised about protection during an epidemic outbreak. Social media campaigns with complete research frameworks with theoretical and statistical support for these time periods are essentially to protect society during emergencies. To address this need, our research model includes the factors that trigger safety campaigns and the main effects (direct) of direct relations and the association of mediators on online social presence.The literature shows that for different disease management programm...

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“…People involved in policy-making are also inspired by social media [29]. Factors involved in sharing information are not described in this literature [30].…”

Section: Role Of E-govtmentioning

confidence: 99%

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

Yasir

Ahmad

et al. 2020

IJERPH

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“…Analyzing such unstructured data requires not only a structured framework expressing a systematic domain classification and terminology, but also knowledge of the semantic relationships between concepts [16]. A framework based on semantic analysis is required to extract information from social media data that will be valuable to government policy makers [17]. In this study we propose an ontology as a framework for the analysis of social media data.…”

Section: Introductionmentioning

confidence: 99%

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

Lee¹,

Park²,

Song³

2021

J Med Internet Res

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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.

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“…One of the media that can be used by the government to build interaction with the community quickly is through social media. Because social media platforms can be a place to communicate government achievements or create space for discussion with the public (Belkahla Driss, Mellouli, & Trabelsi, 2019).…”

Section: Resultsmentioning

confidence: 99%

Central Government Communication With Regions in Handling Covid-19 in Dki Jakarta Through Social Media

Pratiwi

Nofrima

Purwaningsih

2021

wacana

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This article aims to find out the communication relationship between the central government and local government in handling Covid-19. In analyzing the data using descriptive qualitative methods and processed using NVIVO 12 Plus is a processed data application to analyze online and social media data so that researchers can use the application to manage the data until there is no set time limit. Handling the spread of coronavirus is the responsibility of the central government. However, since the decentralization in Indonesia, it is also the authority of the local government itself to determine the policies that must be carried out related to the handling of the coronavirus or covid-19 outbreak. DKI Jakarta is one of the areas exposed to corona virus with the most confirmed cases. Policies carried out by local governments are also in a race against the policies of the central government. So that there is a good communication intergration between the central government and local government.

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

Cited by 84 publications

References 30 publications

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

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

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

Central Government Communication With Regions in Handling Covid-19 in Dki Jakarta Through Social Media

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