scholarly journals Network analysis of Youtube videos based on keyword search with graph centrality approach

2021 ◽  
Vol 22 (2) ◽  
pp. 780
Author(s):  
Edi Surya Negara ◽  
Ria Andryani ◽  
Riyan Amanda
Keyword(s):  
Social Media ◽  
Graph Theory ◽  
Social Network ◽  
Network Analysis ◽  
Betweenness Centrality ◽  
Keyword Search ◽  
Degree Centrality ◽  
Graph Centrality ◽  
A Value ◽  
Youtube Videos

<p>Youtube is a social media that has billions of users, with this can be used as a promotional media, trends, business, and so forth. This study aims to analyze the correlation between Youtube videos by utilizing hashtags on video using graph theory. Data collection in this study uses scraping techniques taken from the Youtube website in the form of links, titles, keywords, and hashtags. The method used in this research is Social Network Analysis, the measurements used in this study are degree centrality and betweenness centrality. The results of this study indicate that the most popular hashtags with the keyword search for "viruses" are #KidflixPT, #Portugues, and #Mondo with degree centrality values equal to 0.071875. and the correlation between the most closely related videos about #Coronavirus with a value of betweenness centrality of 0.082626.</p>

scholarly journals The Social Engagement to Agricultural Issues using Social Network Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 1-7
Author(s):  
Tanty Yanuar Widiyanti ◽  
Teguh Bharata Adji ◽  
Indriana Hidayah
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Engagement ◽  
Betweenness Centrality ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
The Government

Twitter is one of the micro-blogging social media which emphasizes the speed of communication. In the 4.0 era, the government also promotes the distribution of information through social media to reach the community from various lines.  In previous research, Social Network Analysis was used to see the relationship between actors in a work environment, or as a basis for identifying the application of technology adoption in decision making, whereas no one has used SNA to see trends in people's response to agricultural information. This study aims to see the extent to which information about agriculture reaches the community, as well as to see the community's response to take part in agricultural development.  This article also shows the actors who took part in disseminating information. Data was taken on November 13 to 20, 2020 from the Drone Emprit Academic, and was taken limited to 3000 nodes. Then, the measurements of the SNA are represented on the values of Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality. @AdrianiLaksmi has the highest value in Eigenvector Centrality and Degree Centrality, he has the greatest role in disseminating information and has many followers among other accounts that spread the same information. While the @RamliRizal account ranks the highest in Betweenness Centrality, who has the most frequently referred information, and the highest Closeness Centrality is owned by the @baigmac account because of the fastest to re-tweet the first information.

scholarly journals Peran Pers Sebagai Aktor Gerakan Digital Tagar #SolidaritasUntukNTT di Twitter

2021 ◽  
Vol 5 (1) ◽  
pp. 98
Author(s):  
Gema Nusantara Bakry ◽  
Ika Merdekawati Kusmayadi
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Betweenness Centrality ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Personal Network

Peristiwa banjir bandang yang diakibatkan Siklon Seroja telah mengundang perhatian dan simpati masyarakat Indonesia. Berbagai upaya telah dilakukan untuk berkontribusi dalam upaya penanggulangan dampak yang diterima oleh masyarakat NTT. Salah satu upaya yang dilakukan oleh masyarakat adalah mengampanyekan gerakan sosial digital #SolidaritasUntukNTT di Twitter. Gerakan sosial digital melalui pesan-pesan tertentu dapat menggugah kesadaran bagi penggunanya. Untuk mengetahui efektivitas penyebaran pesan dalam gerakan sosial digital dapat divisualisasikan menggunakan metode Social Network Analysis (SNA).  Penelitian ini bertujuan untuk memvisualisasikan peran pers dalam mendistribusikan pesan gerakan sosial digital dengan tagar #SolidaritasUntukNTT. Metode penelitian yang digunakan adalah analisis jaringan sosial dengan teori graf di Twitter. Hasil analisis dan visualisasi jaringan dilakukan di aplikasi Gephi dengan algoritma Yifan Hu untuk melihat distribusi pola pesan dan peran pers pada tagar #SolidaritasUntukNTT. Penelitian ini menggambarkan tipe jaringan two mode yang terdiri dari interaksi antara individu dan organisasi dengan pola komunikasi radial personal network yang memiliki ciri jaringan terbuka dan kohesivitas yang rendah dengan arah relasi directed dan asimetris. Analisis peran pers diukur melalui sentralitas aktor untuk mengetahui degree centrality, closeness centrality, betweenness centrality dan eigenvector centrality. Aktor @vice_id diketahui sebagai aktor yang memiliki degree dan eigenvector centrality tertinggi dibandingkan dengan aktor pers lainnya. Aktor @idntimes dan @detikcom memiliki nilai closeness dan betweenness centrality yang lebih tinggi dari media lainnya. Analisis jaringan sosial memberikan pemahaman terkait distribusi pesan dalam media sosial untuk mengetahui efektivitas pesan yang didistribusikan oleh beberapa aktor jaringan, khususnya peran pers dalam mengampanyekan gerakan sosial di media. Oleh karena itu, metode SNA dapat digunakan untuk penelitian jurnalisme data. 

scholarly journals Ekstraksi Knowledge tentang Penyebaran #Ratnamiliksiapa pada Jejaring Sosial (Twitter) menggunakan Social Network Analysis (SNA)

2019 ◽  
Vol 6 (6) ◽  
pp. 677
Author(s):  
Lyonly Tomasoa ◽  
Ade Iriani ◽  
Irwan Sembiring
Keyword(s):  
Social Media ◽  
Communication Network ◽  
Social Network ◽  
Betweenness Centrality ◽  
Information Exchange ◽  
The Political ◽  
Degree Centrality ◽  
Boundary Spanner ◽  
Political Issues ◽  
Twitter Users

<p class="Abstrak">Memasuki tahun politik 2018-2019, Indonesia mengalami darurat <em>hoax</em>  dimana isu-isu politik menyebar dengan sangat cepat terutama pada jejaring sosial yang merupakan wadah untuk menghubungkan setiap individu di seluruh dunia. <em>Twitter</em> sebagai salah satu jejaring sosial yang sering dipakai masyarakat Indonesia, menyebabkan isu-isu politik pun ikut terbawa dalam bentuk tagar (#). Tagar #RatnaMilikSiapa yang merupakan isu politik dari kasus <em>hoax</em> kebohongan penganiayaan RS di kota Bandung dijadikan sebagai tunggangan para pengguna <em>twitter</em> untuk membangun opini di masyarakat sehinga menjadikan hal tersebut sebagai <em>hoax</em> menjelang pemilihan Presiden 2019. Opini-opini dari setiap pengguna <em>twitter</em> tersebut telah menciptakan jaringan-jaringan komunikasi yang membahas tentang kasus politik penganiyayaan RS dengan tagar #RatnaMilikSiapa . Penelitian ini bertujuan untuk mengidentifikasi aktivitas penyebaran tagar #RatnaMilikSiapa dengan menggunakan metode <em>Social Network Analyis</em> (SNA) pada jejaring sosial <em>twitter</em>. Dalam penelitian ini, dilakukan identifikasi terhadap aktor utama dengan melakukan perhitungan sentralitas tingkatan atau <em>degree Centrality </em>(BC) sehingga dapat ditemukan aktor yang berpengaruh dalam terbentuknya kelompok-kelompok jaringan <em>tweet</em> #RatnaMilikSiapa pada jejaring sosial <em>twitter</em>. Hasil dari penelitan ini adalah ditemukannya 3 aktor  kunci (<em>creator &amp; influencers</em>) yang berasal dari 5 aktor utama penyebaran <em>tweet </em> #RatnaMilikSiapa dengan mengidentifikasi adanya pertukaran berita yang dilakukan oleh para aktor utama dan didukung dengan perhitungan nilai sentralitas keperantaraan atau <em>betweenness Centrality </em>(BC). Kemudian juga ditemukannya 32 aktor <em>boundary spanner</em> yang merupakan dampak dari aktivitas pertukaran berita atau <em>information exchange</em>  yang dilakukan oleh aktor kunci pada jaringan komunikasi dalam jejaring sosial <em>twitter</em>.</p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Entering the political year of 2018-2019, Indonesia is facing a hoax crisis where political issues spread rapidly, especially on social media as a place for connecting people all over the world. Twitter as one of the popular social media, which is frequently used by the society of Indonesian, leads the political issues spread widely through the hashtag (#). </em><em> </em><em>#RatnaMilikSiapa which was a hoax case about RS persecution in Bandung turned as a way for Twitter users creating a judgment in the society so that that issue became a hoax approaching the Presidential Election of 2019. The opinions of Twitter users had created a communication network discussed RS persecution as a political issue with #RatnaMilikSiapa. </em><em> </em><em>This research intends to identify the #RatnaMilikSiapa deployment activity with the using of Social Network Analyis (SNA) method on Twitter. This research conducts the identification toward the main actors with degree Centrality (BC) calculation until the actor who influenced the establishment of #RatnaMilikSiapa tweet network groups on Twitter can be found. The results of this research are the researcher had found the three key actors (creator and influencers) which originated from 5 main actors who spread #RatnaMilikSiapa tweet. The researcher identifies the information exchange which had been done by the main actors and the results supports by the value of betweenness Centrality (BC) calculation. Later, the researcher had found 32 actors of boundary spanner which was the impact of information exchange done by the key actors on the communication network of Twitter.</em><em></em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals A network analysis of character relations in Etienne van Heerden’s Toorberg

Literator ◽  
2013 ◽  
Vol 34 (2) ◽  
Author(s):  
Burgert A. Senekal
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Betweenness Centrality ◽  
Theoretical Framework ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Traditional Concept ◽  
Family Ties

Etienne van Heerden’s Toorberg can be approached as a modern, postcolonial farm novel, partly because it challenges the concept of lineage of inheritance, which is characteristic of the traditional farm novel. Lineage of inheritance implies a strong family bond, and it is therefore instructive to investigate how family ties function within this novel. The article views family ties within Toorberg using Social Network Analysis (SNA), a largely unknown theoretical framework that can also be applied within the study of literature. It is shown how characters’ positions in this network can be calculated in terms of degree centrality, closeness centrality, Eigenvector centrality and betweenness centrality, and how these measures expose the way in which this novel undermines the traditional concept of inheritance.

Exploring the “Radicalization Pipeline” on YouTube

2021 ◽  
Author(s):  
Amanda Champion ◽  
Richard Frank
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Betweenness Centrality ◽  
Video Content ◽  
Youtube Videos

In this study we used social network analysis of incel-related videos on YouTube to understand the recommendations, patterns, and dissemination of incel ideology on a popular multimedia platform, i.e., YouTube. Results revealed 12 distinct groups in the network (e.g., Female Hypergamy, Gynocentric Bias). Central videos in each group revealed the spread of ideological material on YouTube. Videos with the highest betweenness centrality scores were evaluated to map the pathways from groups with more innocuous video content to groups with more extremist incel-ideological content.

scholarly journals Novel Approach for Optimizing Information Propagation in Dynamic Social Network

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Graph Theory ◽  
Social Network ◽  
Network Analysis ◽  
Dynamic Networks ◽  
Degree Centrality ◽  
Information Propagation ◽  
Precise Information ◽  
Dynamic Social Network ◽  
Novel Approach ◽  
Selection Of

Identification of potential node is one of the essential operations to be carried out in social network analysis as it is necessary to undertake various important decisions associated with the information propagation. Review of existing literature towards social network highlights that there is very less work carried out towards emphasizing potential node. Therefore, the proposed study offers a novel and unique solution that is capable of optimizing the level of information propagation when it is exposed to dynamic networks. The proposed study has been modeled using graph theory and it uses degree centrality distribution in order to offer more insight towards analyzing the selection of potential nodes in a social network. The study significantly contributes towards precise information propagation and its sustainability in the presence of dynamic social network in every aspect.

ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER

2019 ◽  
Vol 24 (2) ◽  
pp. 88-104
Author(s):  
Ilham Aminudin ◽  
Dyah Anggraini
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Clustering Coefficient ◽  
R Programming

Banyak bisnis mulai muncul dengan melibatkan pengembangan teknologi internet. Salah satunya adalah bisnis di aplikasi berbasis penyedia layanan di bidang moda transportasi berbasis online yang ternyata dapat memberikan solusi dan menjawab berbagai kekhawatiran publik tentang layanan transportasi umum. Kemacetan lalu lintas di kota-kota besar dan ketegangan publik dengan keamanan transportasi umum diselesaikan dengan adanya aplikasi transportasi online seperti Grab dan Gojek yang memberikan kemudahan dan kenyamanan bagi penggunanya Penelitian ini dilakukan untuk menganalisa keaktifan percakapan brand jasa transportasi online di jejaring sosial Twitter berdasarkan properti jaringan. Penelitian dilakukan dengan dengan mengambil data dari percakapan pengguna di social media Twitter dengan cara crawling menggunakan Bahasa pemrograman R programming dan software R Studio dan pembuatan model jaringan dengan software Gephy. Setelah itu data dianalisis menggunakan metode social network analysis yang terdiri berdasarkan properti jaringan yaitu size, density, modularity, diameter, average degree, average path length, dan clustering coefficient dan nantinya hasil analisis akan dibandingkan dari setiap properti jaringan kedua brand jasa transportasi Online dan ditentukan strategi dalam meningkatkan dan mempertahankan keaktifan serta tingkat kehadiran brand jasa transportasi online, Grab dan Gojek.

scholarly journals A study on the influence of Korean Middle School students' relationship through Science class applying STAD cooperative learning

2018 ◽  
Vol 8 (4) ◽  
pp. 291 ◽  
Author(s):  
Dongryeul Kim
Keyword(s):  
Middle School ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Middle School Students ◽  
Collaborative Learning ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Science Class ◽  
School Students

  In order to find out the influence of Korean Middle School Students' relationship by science class applying STAD collaborative learning, this study conducted a social network analysis and sought to analyze the communication networks within the group and identified the change process of the type. The subject of this study was 30 students of the second grade at the girls' middle school located in Korea's Metropolitan City. For five weeks, science class applying STAD Collaborative Learning was implemented in the ‘reproduction and generation’ chapter. First, the class social network analysis showed that all the prices of density, degree centrality, closeness centrality, and betweenness centrality have risen after science class applying STAD Collaborative Learning. Also, the classroom's relationship index has improved. In other words, STAD Collaborative Learning encouraged interaction among students. Second, in order to research popularity, students' centrality analysis through the class social network analysis showed that top-ranked students' values of density, degree centrality, closeness centrality, and betweenness centrality appeared commonly high after science class applying STAD Collaborative Learning. Third, the analysis of the communication network change within six groups showed that all channel type appeared most often and circle type also appeared anew after science class applying STAD Collaborative Learning. In other words, it was possible to exchange information freely and communicate with all members of the group through STAD Collaborative Learning.

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