scholarly journals Computational Propaganda in Hashtag Activism

2021 ◽  
Vol 13 (2) ◽  
pp. 251
Author(s):  
Andhika Kurniawan Pontoh
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Communication Networks ◽  
Network Structure ◽  
Media Coverage ◽  
Public Attention ◽  
Political Actors ◽  
The Public ◽  
Social Network Structure

The hashtag (#) has an important role in gathering Internet users' support for opinion and value. Computational propaganda has an important role in hashtag activism. This study wants to examine the role of computational propaganda actors such as anonymous political accounts, fake accounts and bot in social media that is able to mobilize the public and also increase the impression of Twitter audiences. The trend of Twitter hashtag activism #BebaskanIBHRS and #NegaraDamaiTanpaFPI began with the arrest of the chairman of the Islamic Defenders Front (FPI) Habib Rizieq Shihab (HRS); the two trending hashtags massively influenced public opinion on Twitter on December 13-14 2020. This study uses a sample of 1000 tweets or conversations on each hashtags and uses Social Network Analysis (SNA) with the Netlytic tool which is able to provide quantitative values of communication networks, through the social network structure of #BebaskaniBHRS and #NegaraDamaiTanpaFPI. This study reveals how the network structure and what factors are carried out by anonymous political actors in carrying out computational propaganda. The results of this study reveal the hashtags activism #BebaskaniBHRS is much more capable of mobilizing the public and is able to generate greater impressions than #NegaraDamaiTanpaFPI. This is because #BebaskaniBHRS has a computational propaganda message in the form of a loaded language with a clear frame and the choice of words directly invites the Twitter public to get involved through a retweet another finding in this research shows computational propaganda movement in hashtag activism was carried out by large groups consisting of anonymous accounts and bot accounts on other side online media coverage about the trending of these hashtag's activism was also able to increasing public attention. Tagar (#) memiliki peran penting dalam mengumpulkan dukungan pengguna Internet terhadap suatu opini dan nilai. Komputasi propaganda memiliki peran penting dalam aktivisme tagar. Penelitian ini ingin mengkaji peran aktor komputasi propaganda seperti akun anonim politik, akun palsu dan bot di media sosial yang mampu memobilisasi publik dan juga meningkatkan kesan khalayak Twitter. Tren aktivisme tagar Twitter #BebaskanIBHRS dan #NegaraDamaiTanpaFPI dimulai dengan penangkapan ketua Front Pembela Islam (FPI) Habib Rizieq Shihab (HRS); kedua tagar yang sedang trending tersebut secara masif memengaruhi opini publik di Twitter pada 13-14 Desember 2020. Penelitian ini menggunakan sampel 1000 tweet atau percakapan pada masing-masing tagar serta menggunakan Social Network Analysis (SNA) dengan alat Netlytic yang mampu memberikan nilai kuantitatif jaringan komunikasi. Melalui struktur jejaring sosial #BebaskaniBHRS dan #NegaraDamaiTanpaFPI, kajian ini mengungkap seperti apa struktur jaringan komunikasi dan hal apa saja yang dilakukan oleh aktor politik anonim dalam melakukan komputasi propaganda. Hasil penelitian ini mengungkapkan bahwa aktivisme tagar #BebaskaniBHRS jauh lebih mampu memobilisasi publik dan mampu menghasilkan impresi yang lebih besar dibandingkan #NegaraDamaiTanpaFPI. Hal ini dikarenakan #BebaskaniBHRS memiliki pesan komputasi propaganda dalam bentuk bahasa yang sarat dengan bingkai yang jelas dan pilihan kata secara langsung mengajak publik Twitter untuk terlibat melalui retweet.Temuan lain dalam penelitian ini menunjukkan gerakan komputasi propaganda dalam aktivisme  tagar dilakukan oleh kelompok besar yang terdiri dari akun anonim dan akun bot di sisi lain liputan media daring tentang tren aktivisme tagar ini juga mampu meningkatkan atensi publik.

scholarly journals Chronic health conditions and adolescent friendship: perspectives from social network analysis

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Emily Long ◽  
Tyson Barrett ◽  
Ginger Lockhart
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Chronic Health Conditions ◽  
Health Conditions ◽  
Exponential Random Graph Models ◽  
Chronic Health ◽  
Social Network Structure ◽  
Adolescent Friendship

Abstract Objective The current study uses methods from social network analysis to examine the relationship between chronic health conditions (CHCs) and adolescent friendships. Particular attention is given to the processes of peer marginalization, peer withdrawal and homophily related to CHCs. Methods Exponential random graph models were used to investigate the extent to which a CHC is associated with patterns in adolescent friendship connections, while controlling for important social network properties and covariates. The study uses cross-sectional data from six small US high schools (n = 461) within the National Longitudinal Study of Adolescent to Adult Health. Results Findings demonstrate no significant differences between adolescents with CHCs and adolescents without CHCs in the number of incoming friendship nominations (peer marginalization) or outgoing friendship nominations (peer withdrawal). In addition, similarity in CHCs (homophily) was not significantly related to friendship between two individuals. Conclusions In sum, the presence of an adolescent CHC was not significantly associated with adolescent social network structure, including peer marginalization, peer withdrawal, and homophily related to CHCs, after controlling for alternative social network processes. Although previous literature suggests that adolescents with CHCs experience negative social consequences, the current findings demonstrate that the social network structure of adolescents with CHCs did not differ significantly from that of their peers without CHCs. Thus, findings from the current study suggest that CHCs are not related to objective reductions in social connections.

scholarly journals Identifying Integration and Differentiation in a Hospital’s Logistical System: A social network analysis of a case study

2020 ◽  
Author(s):  
Annelies van der Ham ◽  
Frits Van Merode ◽  
Dirk Ruwaard ◽  
Arno Van Raak
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Organization Theory ◽  
Exploratory Study ◽  
Case Study Research ◽  
Study Research ◽  
And Performance

Abstract Background Integration, the coordination and alignment of tasks, has been promoted widely in order to improve the performance of hospitals. Both organization theory and social network analysis offer perspectives on integration. This exploratory study research aims to understand how a hospital’s logistical system works, and in particular to what extent there is integration and differentiation. More specifically, it first describes how a hospital organizes logistical processes; second, it identifies the agents and the interactions for organizing logistical processes, and, third, it establishes the extent to which tasks are segmented into subsystems, which is referred to as differentiation, and whether these tasks are coordinated and aligned, thus achieving integration.Methods The study is based on case study research carried out in a hospital in the Netherlands. All logistical tasks that are executed for surgery patients were studied. Using a mixed method, data were collected from the Hospital Information System (HIS), documentation, observations and interviews. These data were used to perform a social network analysis and calculate the network metrics of the hospital network.Results This paper shows that 23 tasks are executed by 635 different agents who interact through 31,499 interaction links. The social network of the hospital demonstrates both integration and differentiation. The network appears to function differently from what is assumed in literature, as the network does not reflect the formal organizational structure of the hospital, and tasks are mainly executed across functional silos. Nurses and physicians perform integrative tasks and two agents who mainly coordinate the tasks in the network, have no hierarchical position towards other agents. The HIS does not seem to fulfill the interactional needs of agents. Conclusions This exploratory study reveals the network structure of a hospital. The cross-functional collaboration, the integration found, and position of managers, coordinators, nurses and doctors suggests a possible gap between organizational perspectives on hospitals and reality. This research sets a basis for further research that should focus on the relation between network structure and performance, on how integration is achieved and in what way organization theory concepts and social network analysis could be used in conjunction with one another.

Social Network Analysis

2011 ◽  
pp. 1460-1469
Author(s):  
David J. Dekker ◽  
Paul H.J. Hendriks
Keyword(s):  
Knowledge Management ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Development Project ◽  
Knowledge Networks ◽  
Knowledge Processes ◽  
Social Network Structure ◽  
Product Development Project ◽  
Practice Knowledge

In knowledge management (KM), one perspective is that knowledge resides in individuals who interact in groups. Concepts as communities-of-practice, knowledge networks, and “encultured knowledge” as the outcome of shared sense-making (Blackler, 1995) are built upon this perspective. Social network analysis focuses on the patterns of people’s interactions. This adds to KM theory a dimension that considers the effects of social structure on for example, knowledge creation, retention and dissemination. This article provides a short overview of consequences of social network structure on knowledge processes and explores how the insights generated by social network analysis are valuable to KM as diagnostic elements for drafting KM interventions. Relevance is apparent for management areas such as R&D alliances, product development, project management, and so forth.

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.

Mobility and structural change through traumatic organizational events

2014 ◽  
Vol 10 (3) ◽  
pp. 382-408 ◽  
Author(s):  
R. Drew Sellers ◽  
Timothy J. Fogerty ◽  
Larry M. Parker
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Professional Service ◽  
Public Accounting ◽  
Content Type ◽  
The Public ◽  
Arthur Andersen ◽  
Practical Implications

Purpose – This paper aims to, using evidence from a former office of the public accounting firm Arthur Andersen, to study the importance of the relational content and structure of individuals’ social connections as they transitioned to subsequent employment. The paper also examines the maintenance of their social networks through time. Implications for careers in the accounting field are offered. Practicing accountants’ connections with other individuals have often been recognized as an important resource that influences career success. However, these social networks have escaped systematic academic study in accounting. Design/methodology/approach – Social network analysis, built on survey data. Findings – The results show that who one was connected to in a previous employment was more important than one’s overall network position when deciding whether to stay or exit public accounting. However those who exited public accounting did not demonstrate a handicap in maintaining network structures after the disbanding of the firm. Research limitations/implications – This study is limited to firm members, and to a single office of a firm. Social network analysis was used as a research tool for the sociology of public accounting. Practical implications – Implications are for careers in public accounting, and the management of human resources in public accounting is offered. Social implications – The paper has implications for the successfulness of professional service provision in a general sense. Originality/value – Almost a decade of social connection is studied with a method that has not appeared in the discipline but is well regarded in management studies.

Estimating Policy Positions Using Social Network Data

2016 ◽  
Vol 35 (1) ◽  
pp. 53-67 ◽  
Author(s):  
Alejandro Ecker
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Science Research ◽  
Policy Position ◽  
Policy Positions ◽  
Political Actors ◽  
External Data ◽  
Social Network Structure ◽  
Big Data Applications ◽  
The Social

Social network site (SNS) data provide scholars with a plethora of new opportunities for studying public opinion and forecasting electoral outcomes. While these are certainly among the most promising big data applications in political science research, a series of pioneering studies have started to uncover the vast potential of such data to estimate the policy positions of political actors. Adding to this emerging strand in the scholarly literature, the present article explores the validity of (individual) policy positions derived from the social network structure of the microblogging platform Twitter. At the aggregate party level, cross-validation with external data sources suggests that SNS data provide valid policy position estimates. In contrast, the empirical analysis reveals only a moderate connection between individual policy positions retrieved from the social network structure and those retrieved from members of parliament individual voting record. These results thus highlight the potential as well as important limitations of SNS data in indicating the policy positions of political parties and individual legislators.

Sign in / Sign up

Export Citation Format

Share Document