scholarly journals DAKWAH DI TENGAH PANDEMI (STUDI TERHADAP RESPONS DAI DI MEDIA SOSIAL)

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Diajeng Laily Hidayati ◽  
Reza Fahlevi
Keyword(s):  
Social Media ◽  
Social Life ◽  
Virus Disease ◽  
Affective Response ◽  
Real Life ◽  
Face Mask ◽  
Positive Thinking ◽  
Social Media Data ◽  
The Government ◽  
Media Data

The Covid-19 (Corona Virus Disease-19) outbreak has managed to change the patterns of social life on society, including the change regarding the way Da’wa is performed; from conventional direct Da’wa to mediated Da’wa through internet. This article aims at describing responses of the Da’is (proselytizers) in social media pertaining to the spread of the Covid-19. This article applied qualitative method to analyse Da’wa contents related to Covid-19 on social media. Data were collected through observation and documentation. Findings show that there are at least three types of responses from the Da’is; cognitive, affective, and behavioural responses.  Cognitive response is manifested in the form of delivering information regarding Covid-19 from the general and medical perspective such as promoting frequent hand-washing, maintaining good hygiene, obeying the government measurements, maintaining healthy level of gratitude and praying to God to be saved from the outbreak. Affective response is manifested in the form of promoting empathy, positive thinking, and avoiding panic. Behavioural response is manifested in the form of giving real-life example such as performing online congregation (pengajian online), wearing face mask, applying appropriate disinfection, and helping those heavily affected by the outbreak.Keywords: Covid-19, Dai, responses, and social media. 

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.

scholarly journals Public Engagement and Government Responsiveness in the Communications About COVID-19 During the Early Epidemic Stage in China: Infodemiology Study on Social Media Data

10.2196/18796 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e18796 ◽  
Author(s):  
Qiuyan Liao ◽  
Jiehu Yuan ◽  
Meihong Dong ◽  
Lin Yang ◽  
Richard Fielding ◽  
...  
Keyword(s):  
Social Media ◽  
Public Engagement ◽  
Latent Class ◽  
Early Stage ◽  
Government Agency ◽  
Instrumental Support ◽  
Social Media Data ◽  
The Government ◽  
Government Responsiveness ◽  
Media Data

Background Effective risk communication about the outbreak of a newly emerging infectious disease in the early stage is critical for managing public anxiety and promoting behavioral compliance. China has experienced the unprecedented epidemic of the coronavirus disease (COVID-19) in an era when social media has fundamentally transformed information production and consumption patterns. Objective This study examined public engagement and government responsiveness in the communications about COVID-19 during the early epidemic stage based on an analysis of data from Sina Weibo, a major social media platform in China. Methods Weibo data relevant to COVID-19 from December 1, 2019, to January 31, 2020, were retrieved. Engagement data (likes, comments, shares, and followers) of posts from government agency accounts were extracted to evaluate public engagement with government posts online. Content analyses were conducted for a random subset of 644 posts from personal accounts of individuals, and 273 posts from 10 relatively more active government agency accounts and the National Health Commission of China to identify major thematic contents in online discussions. Latent class analysis further explored main content patterns, and chi-square for trend examined how proportions of main content patterns changed by time within the study time frame. Results The public response to COVID-19 seemed to follow the spread of the disease and government actions but was earlier for Weibo than the government. Online users generally had low engagement with posts relevant to COVID-19 from government agency accounts. The common content patterns identified in personal and government posts included sharing epidemic situations; general knowledge of the new disease; and policies, guidelines, and official actions. However, personal posts were more likely to show empathy to affected people (χ21=13.3, P<.001), attribute blame to other individuals or government (χ21=28.9, P<.001), and express worry about the epidemic (χ21=32.1, P<.001), while government posts were more likely to share instrumental support (χ21=32.5, P<.001) and praise people or organizations (χ21=8.7, P=.003). As the epidemic evolved, sharing situation updates (for trend, χ21=19.7, P<.001) and policies, guidelines, and official actions (for trend, χ21=15.3, P<.001) became less frequent in personal posts but remained stable or increased significantly in government posts. Moreover, as the epidemic evolved, showing empathy and attributing blame (for trend, χ21=25.3, P<.001) became more frequent in personal posts, corresponding to a slight increase in sharing instrumental support, praising, and empathizing in government posts (for trend, χ21=9.0, P=.003). Conclusions The government should closely monitor social media data to improve the timing of communications about an epidemic. As the epidemic evolves, merely sharing situation updates and policies may be insufficient to capture public interest in the messages. The government may adopt a more empathic communication style as more people are affected by the disease to address public concerns.

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.

scholarly journals Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

2020 ◽  
Vol 13 (4) ◽  
pp. 985-1017
Author(s):  
Marco Adelfio ◽  
Leticia Serrano-Estrada ◽  
Pablo Martí-Ciriquián ◽  
Jaan-Henrik Kain ◽  
Jenny Stenberg
Keyword(s):  
Social Media ◽  
Urban Areas ◽  
Social Life ◽  
Social Activity ◽  
Third Places ◽  
Social Media Data ◽  
The City ◽  
Media Data ◽  
Urban Activity ◽  
Activity Areas

Abstract This research focuses on the intermediate city, composed of urban areas located right outside the city center typically maintaining an in-between urban/suburban character. It aims to explore the degree to which this segment of the city exhibits urban activity and social life through the identification of activity areas in the so-called Third Places. Four intermediate city neighborhoods in Gothenburg, Sweden are adopted as case areas and are analyzed using a twofold approach. First, socio-economic statistics provide a quantitative understanding of the case areas and, second, geolocated Social Media Data (SMD) from Foursquare, Google Places and Twitter makes it possible to identify the intermediate city’s urban activity areas and socially preferred urban spaces. The findings suggest that a) the four analyzed intermediate city areas of Gothenburg all have a degree of social activity, especially where economic activities are clustered together; b) Third Places in more affluent areas tend to be linked to commodified consumption of urban space while neighborhoods with lower income levels and higher ethnic diversity seem to emphasize open public space as Third Places; and c) nowadays the typology of Third Places has evolved from the types identified in previous decades to include additional types of places, such as those you pass on the way to something else (e.g. gas and bus stations). The study has verified the value of SMD for studies of urban social life but also identified a number of topics for further research. Additional sources of SMD should be identified to secure a just representation of Third Places across diverse social groups. Furthermore, new methods for effective cross validation of SMD with other types of data are crucial, including e.g. statistics, on-site observations and surveys/interviews, not least to identify Third Places that are not frequently present (or are misrepresented) in SMD.

scholarly journals The climate change movement and political parties: Mechanisms of social media and interaction during the 2019 electoral period in Finland

2021 ◽  
Vol 3 (1) ◽  
pp. 40-60
Author(s):  
Sonja Savolainen ◽  
Tuomas Ylä-Anttila
Keyword(s):  
Climate Change ◽  
Social Media ◽  
Social Movements ◽  
Political Parties ◽  
Comparative Studies ◽  
Social Media Data ◽  
Interaction Dynamics ◽  
The Government ◽  
Media Data

Abstract Building on the framework of electoral contention, we investigate the interaction dynamics between social movements and political parties during elections. We argue that social media today is an important venue for these interactions, and consequently, analysing social media data is useful for understanding the shifts in the conflict and alliance structures between movements and parties. We find that Twitter discussions on the climate change movement during the 2019 electoral period in Finland reveal a process of pre-election approaching and post-election distancing between the movement and parties. The Greens and the Left formed mutually beneficial coalitions with the movement preceding the elections and took distance from one another after these parties entered the government. These findings suggest that research on movement-party interaction should pay more attention to social media and undertake comparative studies to assess whether the approaching-distancing process and its constituent mechanisms characterise movements beyond the climate strikes in Finland.

scholarly journals Temporal Characteristics of Waterfronts in Wuhan City and People’s Behavioral Preferences Based on Social Media Data

2019 ◽  
Vol 11 (22) ◽  
pp. 6308
Author(s):  
Jing Wu ◽  
Xirui Chen ◽  
Shulin Chen
Keyword(s):  
Social Media ◽  
Sustainable Urban Development ◽  
Social Media Data ◽  
Temporal Characteristics ◽  
Space Design ◽  
The Social ◽  
Behavioral Preferences ◽  
Cultural Themes ◽  
The Government ◽  
Media Data

The appeal and vibrancy of urban waterfronts are catalysts for urban progress and sustainable urban development. This study aims to thoroughly explore the temporal characteristics of waterfront vibrancy and explore people’s behavioral preferences for various types of waterfronts at various times. On the basis of social media data, this study uses the seasonal index analysis method to classify waterfronts. Then, the kernel density estimation was used to analyze the spatial structure of different types of waterfronts. Finally, temporally weighted regression was used to indicate people’s preferences for various types of waterfronts. In general, results show the different temporal characteristics of users in waterfronts at different times and their behavioral preferences for waterfronts as the reasons behind these preface characteristics. First, on weekdays, people tend to visit daily waterfronts close to residences, and people find it convenient to walk after 18:00 and engage in recreational activities dominated by consumption and exercise, which reach a peak at 22:00–24:00. Second, on weekends, people prefer the weekend waterfronts with complete entertainment facilities and cultural themes. The natural seasonal waterfronts with seasonal landscapes attract people in various seasons, such as spring and autumn, whereas the social seasonal waterfront may be more attractive during high seasons, especially in March and June, due to big water events or nearby colleges and universities. Therefore, the government should improve the facilities of various types of waterfronts to satisfy people’s preferences at different times and help in proposing targeted suggestions with reference to future city waterfront planning and space design, contributing to the waterfronts’ vitality improvement, urban features, and promotion of urban sustainable development.

scholarly journals Public Engagement and Government Responsiveness in the Communications About COVID-19 During the Early Epidemic Stage in China: Infodemiology Study on Social Media Data (Preprint)

2020 ◽  
Author(s):  
Qiuyan Liao ◽  
Jiehu Yuan ◽  
Meihong Dong ◽  
Lin Yang ◽  
Richard Fielding ◽  
...  
Keyword(s):  
Social Media ◽  
Public Engagement ◽  
Latent Class ◽  
Early Stage ◽  
Government Agency ◽  
Instrumental Support ◽  
Social Media Data ◽  
The Government ◽  
Government Responsiveness ◽  
Media Data

BACKGROUND Effective risk communication about the outbreak of a newly emerging infectious disease in the early stage is critical for managing public anxiety and promoting behavioral compliance. China has experienced the unprecedented epidemic of the coronavirus disease (COVID-19) in an era when social media has fundamentally transformed information production and consumption patterns. OBJECTIVE This study examined public engagement and government responsiveness in the communications about COVID-19 during the early epidemic stage based on an analysis of data from Sina Weibo, a major social media platform in China. METHODS Weibo data relevant to COVID-19 from December 1, 2019, to January 31, 2020, were retrieved. Engagement data (likes, comments, shares, and followers) of posts from government agency accounts were extracted to evaluate public engagement with government posts online. Content analyses were conducted for a random subset of 644 posts from personal accounts of individuals, and 273 posts from 10 relatively more active government agency accounts and the National Health Commission of China to identify major thematic contents in online discussions. Latent class analysis further explored main content patterns, and chi-square for trend examined how proportions of main content patterns changed by time within the study time frame. RESULTS The public response to COVID-19 seemed to follow the spread of the disease and government actions but was earlier for Weibo than the government. Online users generally had low engagement with posts relevant to COVID-19 from government agency accounts. The common content patterns identified in personal and government posts included sharing epidemic situations; general knowledge of the new disease; and policies, guidelines, and official actions. However, personal posts were more likely to show empathy to affected people (χ<sup>2</sup><sub>1</sub>=13.3, <i>P</i>&lt;.001), attribute blame to other individuals or government (χ<sup>2</sup><sub>1</sub>=28.9, <i>P</i>&lt;.001), and express worry about the epidemic (χ<sup>2</sup><sub>1</sub>=32.1, <i>P</i>&lt;.001), while government posts were more likely to share instrumental support (χ<sup>2</sup><sub>1</sub>=32.5, <i>P</i>&lt;.001) and praise people or organizations (χ<sup>2</sup><sub>1</sub>=8.7, <i>P</i>=.003). As the epidemic evolved, sharing situation updates (for trend, χ<sup>2</sup><sub>1</sub>=19.7, <i>P</i>&lt;.001) and policies, guidelines, and official actions (for trend, χ<sup>2</sup><sub>1</sub>=15.3, <i>P</i>&lt;.001) became less frequent in personal posts but remained stable or increased significantly in government posts. Moreover, as the epidemic evolved, showing empathy and attributing blame (for trend, χ<sup>2</sup><sub>1</sub>=25.3, <i>P</i>&lt;.001) became more frequent in personal posts, corresponding to a slight increase in sharing instrumental support, praising, and empathizing in government posts (for trend, χ<sup>2</sup><sub>1</sub>=9.0, <i>P</i>=.003). CONCLUSIONS The government should closely monitor social media data to improve the timing of communications about an epidemic. As the epidemic evolves, merely sharing situation updates and policies may be insufficient to capture public interest in the messages. The government may adopt a more empathic communication style as more people are affected by the disease to address public concerns.

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