To browse Academia. The social communities created by social media, where people have shown great interest, have led to the analysis of social network. In this sense, many techniques have been developed and these techniques have been practised in various fields. By means of the softwares, developed for investigation of complex network analysis, detailed surveys and research can be made about social media. In this study, a social network analysis has conducted on the social media. In addition, although the hashtags are addressing different topics, the value of the network characteristics such as clustering and centralization were found to be similar to each other. Social media is a well-known platform for user to create, share and check the new information which is being updated on a daily basis. The world has become a global village because of the utilization of the internet and social media like Facebook, Twitter and various other social media sites. Sentiment-Analysis is a combination of two words Sentiment and Analysis. Any opinion of an individual through which the feelings, attitude, and thoughts can be expressed is known as sentiment. Twitter, being the most popular microblogging This article explores how nationalism is expressed on Twitter by Poker Chip Distribution For Texas Holdem Malaysian and Indonesian young generation. Twitter has a hashtag feature, which is one of the trends popular among youths. This research investigates the Poker Chip Distribution For Texas Holdem network behavior in Twitter among the Malaysians and Indonesians on cyber-nationalism through quantitative measurement of the connection patterns among users, information deployment, and coverage-network distance. This analysis was performed utilizing the social network analysis method, employing the NodeXL Pro software as a data collecting engine. The data were collected a day after the badminton tournament was held in Malaysia to optimize the data quality. The three network metrics levels were used to quantify their behavior, i. To discover and identify the influential nodes in any complex network has been an important issue. It is a significant factor in order to control over the network. Through control on a network, any information can be spread and stopped in a short span of time. Both targets can be achieved, since network of information can be extended and as well destroyed. So, information spread and community formation have become one of the most crucial issues in the world of SNA Social Network Analysis. In this work, the complex network of twitter social network has been formalized and results are analyzed. For this purpose, different network metrics have been utilized. Visualization of the network is provided in its original form and then filter out different percentages from the network to eliminate the less impacting nodes and edges for better analysis. This network is analyzed according to different centrality measures, like edge-betweenness, betweenness centrality, closeness centrality and eigenvector centrality. Influential nodes are detected and their impact is observed on the network. The communities are analyzed in terms of network coverage considering the Minimum Spanning Tree, shortest path distribution and network diameter. It is found that these are the very effective ways to find influential and central nodes from such big social networks like Facebook, Instagram, Twitter, LinkedIn, etc. Now a day everything around the globe is connected via networks like information, places and events which make a tangle of connections. The outgrowth and favoritism of online social network made available a large amount of data all of a sudden from social organization, human behavior and their interaction. Analyzing social network is to make sense of these complex connections. Specific to this asymmetric matrix, the authors use UCINET social network analysis software and visual software NETDRAW, analyze Microblog network structure, network centrality based on network group incidents, and comprehensively describe the laws to release and acquire group incidents under the mixed network environment of Microblog, combined with the massive Microblog exchange data, to analyze the non-rational behavior of the netizens, so as to provide a scientific basis for setting out the appropriate guiding strategy and interventions. Iranian Journal of Information Processing and Management, This paper aims at evaluating the question of whether features of the public sphere are present in the activities of Iranian Twitter users. To achieve this objective, the paper provides a synthesis of computational social sciences, network analysis, and Habermas's approach to the public sphere. The data comes from all Iranian users, including those who are actively engaged in political issues on Twitter. This method of data collection focuses on the computational aspects of the users. Added information on how they conducted networking and facilitated public opinion, which resulted in increased user interaction, would complete the data-gathering process to align with the theoretical claims. It also indicates that Twitter was a platform on which Iranian users fulfilled the requirements of the public sphere. Some of the findings indicate that there were 48, users connected with 25, edges, and together, they formed three communities. While one of the communities focused solely on social issues, the other two engaged in a rival political challenge.
Comparison of Two Different Social Groups on Twitter with Network Analysis
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