scholarly journals DISASTER COMMUNICATION PATTERNS AND BEHAVIORS ON SOCIAL MEDIA: A STUDY SOCIAL NETWORK #BANJIR2020 ON TWITTER

2020 ◽  
Vol 8 (4) ◽  
pp. 27-36
Author(s):  
Nuriyati Samatan ◽  
Ahmad Fatoni ◽  
Sri Murtiasih
Keyword(s):  
Social Network ◽  
Information Dissemination ◽  
Communication Patterns ◽  
Flood Disaster ◽  
Communication Behaviors ◽  
Unexpected Event ◽  
Disaster Communication ◽  
Twitter User ◽  
And Behaviors ◽  
Twitter Users

Purpose of the study: The purpose of this research is to analyze the disaster communication patterns and behaviors of Twitter users. Flood disaster in the Jabodetabek area became an unexpected event in early 2020. The flood inundated 23 areas in Bekasi, two regions in Bogor, and 17 areas in Jakarta. Information about floods became a trending topic on the 1st of January 2020. Methodology: The method used is social network analysis and text analysis #Banjir2020 on Twitter, using Netlytic and Gephi. The sample analyzed 1000 tweets from 304 users and 670 edges. The data was selected from the 10th to 13th of January 2020. Netlytic.org limits that we can only retrieve tweets data from Twitter for less than 2 weeks due to API limitations. Main Findings: The result shows that #Banjir2020 disaster communication patterns on Twitter formed five significant clusters on its network. The communication occurred as one-way communication. A low level of network density showed that the quiet intensity of communication and slow information to be able to spread throughout vast networks. Every twitter user involved can directly provide information to others. Judging from the messages conveyed, the most formed behavior is the behavior of information dissemination regarding this flood. The next significant response is the defense of DKI Jakarta Governor. Implications of this study: The disaster communication behaviors on #Banjir2020 is dominated by flood disaster information in some areas. Communication patterns form vast networks but still lack in terms of intensity, two-way communication, and slow information to move throughout the system. Novelty/Originality of this study: The research of #banjir2020 through Twitter using the analysis of SNA and disaster communication behavior has never been done by other researchers.

A Social Network Analysis of Pro-maskers and Anti-maskers on Twitter during the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Wasim Ahmed ◽  
Laeeq Khan ◽  
Aqdas Malik
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Dissemination ◽  
News Stories ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Social Media Platforms ◽  
Twitter Account ◽  
Twitter Users

BACKGROUND Online debates surrounding face masks as a precautionary measure against COVID-19 pandemic have been raging on social media platforms like Twitter. Users against or in favor of masks have be vocal in sharing their opinions about the masks wearing, practices, and consequences. Through a social network analysis (SNA), we unearth the important contributors to the debate on Twitter surrounding pro and anti-masks. OBJECTIVE The aim of this study is to develop an understanding of the content and influencers on Twitter related to anti-mask and pro-mask debates. METHODS In this study 18,000 tweets related to keywords that were either pro mask and anti-mask from 12th December 2020, to 18th December 2020, and 18,000 tweets from 9th of December to the 18th of December 2020. The two datasets were analysed using social network analysis in Gephi, meanwhile NodeXL was used to produce network metrics and identify the key users, websites, and content within the data. RESULTS Discussions for both the cases revolved around prominent political figures and their supporters. The anti-mask discussions contained frequent mentions to Donald Trump’s Twitter account which made the account the most influential within the anti-mask network. On the contrary, for tweets that were pro-mask the most influential user was Joe Biden. Our network visualization further identified several clusters within the network and found that influential accounts appeared with sizeable clusters. Keywords used by Twitter users for pro-masking revolved around words that were in support of masking. Whereas tweets related to anti-masks appeared to be broader and included words related to news stories of popular figures not wearing face masks. For pro-mask discussions, popular websites also appeared to support the idea of wearing masks whereas the anti-mask data contained references to news stories of popular figures not wearing masks and of news stories related to a study which noted not wearing a mask was safer than wearing a used mask. CONCLUSIONS The current study highlights how discussions around pro-mask and anti-mask revolved around the key political figures. Our results are likely to be of interest to public health authorities involved in encouraging the public to wear masks. The study highlighted key users that could be targeted for information dissemination about face masks. The study further provided insight into narratives and online resources shared through pro-masking and anti-masking accounts.

Understanding Online Conversation about COVID-19 Vaccine on Twitter: Data Mining Approach (Preprint)

2021 ◽  
Author(s):  
Ekaterina Malova
Keyword(s):  
Online Social Networks ◽  
Information Seeking ◽  
Information Dissemination ◽  
Vaccine Development ◽  
Online Communication ◽  
Vaccine Safety ◽  
Interaction Patterns ◽  
Twitter Users ◽  
The Government ◽  
Online Conversation

BACKGROUND Timely vaccination against COVID-19 can prevent a large number of people from getting infected. However, given the disease novelty and fast vaccine development, some people are hesitant to vaccinate. Online social networks like Twitter produce huge amounts of public health information and impact peoples' vaccination decisions. Hence, it is important to understand the conversation around the COVID-19 vaccination through the lens of social media. OBJECTIVE The present study aimed to define the nature of a larger Twitter conversation around the COVID-19 vaccine and explored interaction patterns between Twitter users engaged in such a conversation. METHODS Data collection took place in November 2020 on the wave of the news about the COVID-19 vaccine breakthrough. In total, 9600 Twitter posts were analyzed using a combination of text and network analysis. RESULTS Results of this study show that mixed-emotions reactions and discussions about potential side effects and vaccine safety dominated the online conversation. Twitter was primarily used for two purposes: information dissemination and opinion expression. Overall, the communication network was sparse, non-reciprocal, decentralized, and highly modular. Four main network clusters highlighted different groups of conversation stakeholders. CONCLUSIONS This study provides important insights into public sentiments, information-seeking behaviors, and online communication patterns during a major COVID-19 crisis. Given the popularity of Twitter among different types of communities and its power for rapid information dissemination, it can be an effective tool for vaccination promotion. Thus, it should be actively used to promote safe and effective vaccination through major stakeholders in the government, science, and health sectors.

scholarly journals Analisis Akun Twitter Berpengaruh terkait Covid-19 menggunakan Social Network Analysis

2021 ◽  
Vol 5 (4) ◽  
pp. 697-704
Author(s):  
Aprillian Kartino ◽  
M. Khairul Anam ◽  
Rahmaddeni ◽  
Junadhi
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Dissemination ◽  
Interaction Network ◽  
Analysis Method ◽  
The Social ◽  
The World ◽  
High Degree

Covid-19 is a disease of the virus that is shaking the world and has been designated by WHO as a pandemic. This case of Covid-19 can be a place of dissemination of disinformation that can be utilized by some parties. The dissemination of information in this day and age has turned to the internet, namely social media, Twitter is one of the social media that is often used by Indonesians and the data can be analyzed. This study uses the social network analysis method, conducted to be able to find nodes that affect the ongoing interaction in the interaction network of information dissemination related to Covid-19 in Indonesia and see if the node is directly proportional to the value of its popularity. As well as to know in identifying the source of Covid-19 information, whether dominated by competent Twitter accounts in their fields. The data examined 19,939 nodes and 12,304 edges were taken from data provided by the web academic.droneemprit.id on the project "Analisis Opini Persebaran Virus Corona di Media Sosial", using the period of December 2019 to December 2020 on social media Twitter. The results showed that the @do_ra_dong account is an influential actor with the highest degree centrality of 860 and the @detikcom account is the actor with the highest popularity value of follower rank of 0.994741605. Thus actors who have a high degree of centrality value do not necessarily have a high follower rank value anyway. The study ignores if there are buzzer accounts on Twitter.  

scholarly journals A Measurement Model of Mutual Influence for Information Dissemination

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 725
Author(s):  
Liang Zhang ◽  
Yong Quan ◽  
Bin Zhou ◽  
Yan Jia ◽  
Liqun Gao
Keyword(s):  
Social Network ◽  
Information Dissemination ◽  
Mutual Influence ◽  
Synthetic Data ◽  
Measurement Model ◽  
Mobile Internet ◽  
Information Propagation ◽  
Information Cascade ◽  
Data Set ◽  
Access Information

The recent development of the mobile Internet and the rise of social media have significantly enriched the way people access information. Accurate modeling of the probability of information propagation between users is essential for studying information dissemination issues in social networks. As the dissemination of information is inseparable from the interactions between users, the probability of propagation can be characterized by such interactions. In general, there are differences in the dissemination modes of information that carry different topics in a real social network. Using these factors, we propose a method (TMIVM) to measure the mutual influence between users at the topic level. The method associates two vectorization parameters for each user—an influence vector and a susceptibility vector—where the dimensions of the vector represent different topic categories. The magnitude of the mutual influence between users on different topics can be obtained by the product of the corresponding elements of the vectors. Specifically, in this article, we fit a social network historical information cascade data through Survival Analysis to learn the parameters of the influence and susceptibility vectors. The experimental results on a synthetic data set and a real Microblog data set show that this method better measures the propagation probability and information cascade predictions compared to other methods.

Six Degrees of Information: Using Social Network Analysis to Explore the Spread of Information Within Sport Social Networks

2012 ◽  
Vol 5 (1) ◽  
pp. 16-34 ◽  
Author(s):  
Marion E. Hambrick
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Home Pages ◽  
Sporting Event ◽  
Sport Industry ◽  
Twitter Users ◽  
Promotional Messages ◽  
Share Information

Sport industry groups including athletes, teams, and leagues use Twitter to share information about and promote their products. The purpose of this study was to explore how sporting event organizers and influential Twitter users spread information through the online social network. The study examined two bicycle race organizers using Twitter to promote their events. Using social network analysis, the study categorized Twitter messages posted by the race organizers, identified their Twitter followers and shared relationships within Twitter, and mapped the spread of information through these relationships. The results revealed that the race organizers used their Twitter home pages and informational and promotional messages to attract followers. Popular Twitter users followed the race organizers early, typically within the first 4 days of each homepage’s creation, and they helped spread information to their respective followers. Sporting event organizers can leverage Twitter and influential users to share information about and promote their events.

INFORMATION EXCHANGES OF SOCIAL MEDIA EVANGELISTS DURING FLOOD: A SOCIAL NETWORK ANALYSIS

2016 ◽  
Vol 78 (9-3) ◽  
Author(s):  
Abdus-samad Temitope Olanrewaju ◽  
Rahayu Ahmad ◽  
Kamarul Faizal Hashim
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Information Dissemination ◽  
Network Structures ◽  
Analysis Technique ◽  
Types Of Information ◽  
Information Brokers

Information dissemination during disaster is very crucial, but inherits several complexities associated with the dynamic characteristics of the disaster. Social media evangelists (activists) play an important role in disseminating critical updates at on-site locations. However, there is limited understanding on the network structure formed and its evolution and the types of information shared. To address these questions, this study employs Social Network Analysis technique on a dataset containing 157 social media posts from an influential civilian fan page during Malaysia’s flood. The finding demonstrates three different network structures emerged during the flood period. The network structure evolves depending on the current state of the flood, the amount of information available and the need of information. Through content analysis, there were seven types of information exchanges discovered. These information exchanges evolved as the scale and magnitude of flood changes. In conclusion, this study shows the emergence of different network structures, density and identification of influential information brokers among civilians that use social media during disaster. Despite the low number of influential information brokers, they successfully manage their specific cluster in conveying information about the disaster and most importantly coordinating the rescue mission.

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