The Role of Smart Technologies to Support Citizen Engagement and Decision Making

2022 ◽  
pp. 687-703
Author(s):  
Gabriela Viale Pereira ◽  
Gregor Eibl ◽  
Constantinos Stylianou ◽  
Gilberto Martínez ◽  
Haris Neophytou ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Real Life ◽  
Digital Technologies ◽  
Data Driven ◽  
Social Media Data ◽  
Life Problems ◽  
Smart Technologies ◽  
High Level ◽  
Media Data

Smart government relies both on the application of digital technologies to enable citizen's participation in order to achieve a high level of citizen centricity and on data-driven decision making in order to improve the quality of life of citizens. Data-driven decisions in turn depend on accessible and reliable datasets, which open government and social media data are likely to promise. The SmartGov project uses digital technologies by integrating open and social media data in Fuzzy Cognitive Maps to model real life problems and simulate different scenarios leading to better decision making. This research performed a multiple-case analysis in two pilot cities. Both municipalities use the technologies to find the best routes: Limassol to improve the garbage collection and Quart de Poblet to improve the walking routes of chaperones guiding children to school. The article proposes a generic framework for Smart City Governance focusing on the inputs and outcomes of this process in the use of technologies for policy making built based on the analysis of the SmartGov.

The Role of Smart Technologies to Support Citizen Engagement and Decision Making

2018 ◽  
Vol 14 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Gabriela Viale Pereira ◽  
Gregor Eibl ◽  
Constantinos Stylianou ◽  
Gilberto Martínez ◽  
Haris Neophytou ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Real Life ◽  
Digital Technologies ◽  
Fuzzy Cognitive Maps ◽  
Data Driven ◽  
Social Media Data ◽  
Life Problems ◽  
Smart Technologies ◽  
Media Data

Smart government relies both on the application of digital technologies to enable citizen's participation in order to achieve a high level of citizen centricity and on data-driven decision making in order to improve the quality of life of citizens. Data-driven decisions in turn depend on accessible and reliable datasets, which open government and social media data are likely to promise. The SmartGov project uses digital technologies by integrating open and social media data in Fuzzy Cognitive Maps to model real life problems and simulate different scenarios leading to better decision making. This research performed a multiple-case analysis in two pilot cities. Both municipalities use the technologies to find the best routes: Limassol to improve the garbage collection and Quart de Poblet to improve the walking routes of chaperones guiding children to school. The article proposes a generic framework for Smart City Governance focusing on the inputs and outcomes of this process in the use of technologies for policy making built based on the analysis of the SmartGov.

scholarly journals Stock Price Prediction: LSTM Based Model

2021 ◽  
Author(s):  
Ranjan Kumar Roy ◽  
Koyel Ghosh ◽  
Apurbalal Senapati
Keyword(s):  
Social Media ◽  
Stock Price ◽  
Real Life ◽  
Government Bonds ◽  
Social Media Data ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Multiple Factors ◽  
System Building ◽  
Media Data

Stock price prediction is a critical field used by most business people and common or retail people who tried to increase their money by value with respect to time. People will either gain money or loss their entire life savings in stock market activity. It is a chaos system. Building an accurate model is complex as variation in price depends on multiple factors such as news, social media data, and fundamentals, production of the company, government bonds, historical price and country's economics factor. Prediction model which considers only one factor might not be accurate. Hence incorporating multiple factors news, social media data and historical price might increase the model's accuracy. This paper tried to incorporate the issue when someone implements it as per the model outcome. It cannot give the proper result when someone implements it in real life since capital market data is very sensitive and news-driven. To avoid such a situation, we use the hedging concept when implemented.

Citizen Engagement and Social Media

2019 ◽  
Vol 10 (2) ◽  
pp. 24-43
Author(s):  
Rodrigo Sandoval-Almazan ◽  
Juan Carlos Montes de Oca Lopez
Keyword(s):  
Social Media ◽  
Political Campaigns ◽  
Election Campaigns ◽  
Weekly Basis ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
High Level ◽  
Media Data

Social media has transformed election campaigns around the world. While it is difficult to determine to what extent social media influence voters' decisions, there is no doubt that social media platforms impact on candidate advertising and public debate during elections. This research, the methodological formulation of which is based on a case study, seeks to investigate the use of social media during political campaigns to collect signatures of support. In the elections of 2018, aspiring candidates for presidential election required a certain number of signatures of support in order to register as official candidates. We collected social media data on a weekly basis from the Twitter, Facebook, and YouTube accounts of seven candidates and contrasted this data with the number of signatures validated by the electoral authority. We found no relationship between the level of support received and the use of social media in the case of any of the candidates. However, we observed candidates who did achieve the required number of signatures and who did receive official presidential candidate status as a result of their high level of visibility. This research contributes methodologically to the current literature and provides empirical evidence regarding independent candidates in Mexico.

scholarly journals Identifying Public Perceptions toward Emerging Transportation Trends through Social Media-Based Interactions

2021 ◽  
Vol 1 (3) ◽  
pp. 794-813
Author(s):  
Md Rakibul Alam ◽  
Arif Mohaimin Sadri ◽  
Xia Jin
Keyword(s):  
Social Media ◽  
Built Environment ◽  
Large Scale ◽  
User Fees ◽  
Data Driven ◽  
Social Media Data ◽  
Shared Mobility ◽  
Vehicle Technology ◽  
Spatio Temporal ◽  
Media Data

The objective of this study is to mine and analyze large-scale social media data (rich spatio-temporal data unlike traditional surveys) and develop comparative infographics of emerging transportation trends and mobility indicators by adopting natural language processing and data-driven techniques. As such, first, around 13 million tweets for about 20 days (16 December 2019–4 January 2020) from North America were collected, and tweets closely aligned with emerging transportation and mobility trends (such as shared mobility, vehicle technology, built environment, user fees, telecommuting, and e-commerce) were identified. Data analytics captured spatio-temporal differences in social media user interactions and concerns about such trends, as well as topics of discussions formed through such interactions. California, Florida, Georgia, Illinois, New York are among the highly visible cities discussing such trends. Being positive overall, people carried more positive views on shared mobility, vehicle technology, telecommuting, and e-commerce, while being more negative on user fees, and the built environment. Ride-hailing, fuel efficiency, trip navigation, daily as well as shopping and recreational activities, gas price, tax, and product delivery were among the emergent topics. The social media data-driven framework would allow real-time monitoring of transportation trends by agencies, researchers, and professionals.

Tracking via Geotagged Social Media Data

2020 ◽  
Author(s):  
Anne Hardy
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Single Point ◽  
Application Programming Interface ◽  
Continuous Location ◽  
Social Media Data ◽  
Large Numbers ◽  
Application Programming ◽  
Media Data ◽  
Programming Interface

Over the past twenty years, social media has changed the ways in which we plan, travel and reflect on our travels. Tourists use social media while travelling to stay in touch with friends and family, enhance their social status (Guo et al., 2015); and assist others with decision making (Xiang and Gretzel, 2010; Yoo and Gretzel, 2010). They also use it to report back to their friends and family where they are. This can be done using a geotag function that provides a location for where a post is made. While little is known about why tourists choose to geotag their social media posts, Chung and Lee (2016) suggest that geotags may be used in an altruistic manner by tourists, in order to provide information, and because they elicit a sense of anticipated reward. What is known, however, is that the function offers researchers the ability to understand where tourists travel. There are two types of geotagged social media data. The first of these is discussed in this chapter and may be defined as single point geo-referenced data – geotagged social media posts whose release is chosen by the user. This includes data gathered from social media apps such as Facebook, Instagram, Twitter and WeiChat. The method of obtaining this data involves the collation of large numbers of discrete geotagged updates or photographs. Data can be collated via an application programming interface (API) provided by the app developer to researchers, by automated data scraping via computer programs, perhaps written in Python, or manually by researchers. The second type of data is continuous location-based data from applications that are designed to track movement constantly, such as Strava or MyFitnessPal. Tracking methods using this continuous location-based data are discussed in detail in the following chapter.

scholarly journals Cognitive Biases in Link Sharing Behavior and How to Get Rid of Them: Evidence from the 2019 Spanish General Election Twitter Conversation

2020 ◽  
Vol 6 (2) ◽  
pp. 205630512092845 ◽  
Author(s):  
Jordi Morales-i-Gras
Keyword(s):  
Social Media ◽  
Cognitive Biases ◽  
General Election ◽  
Data Driven ◽  
Fake News ◽  
False Information ◽  
Social Media Data ◽  
Sharing Behavior ◽  
Media Data ◽  
Selective Information

After a few years focusing on issues such as electoral prediction through social media data, many analysts turned their attention toward fake news spreading and misinformation. A coherent next step in elections research through social media data would be identifying what makes communities and individuals less open to manipulation. Misinformation is not simply bad or false information but selective information circulated among isolated and unconnected groups. Here, I will discuss common cognitive biases in link sharing behavior and its effects on politically shaped communities in the Twitter public debate on the 2019 Spanish general election campaign. Finally, I will present and discuss some data-driven mechanisms that may contribute to the mitigation of mass manipulation.

A data-driven framework for archiving and exploring social media data

Annals of GIS ◽  
2014 ◽  
Vol 20 (4) ◽  
pp. 265-277 ◽  
Author(s):  
Qunying Huang ◽  
Chen Xu
Keyword(s):  
Social Media ◽  
Data Driven ◽  
Social Media Data ◽  
Media Data
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