Visualization and Analysis of Frames in Collections of Messages

2012 ◽  
pp. 321-339 ◽  
Author(s):  
Esther Vlieger ◽  
Loet Leydesdorff
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Vector Space ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Social Systems ◽  
Semantic Map ◽  
Network Space ◽  
Communication Of Meaning

A step-by-step introduction is provided on how to generate a semantic map from a collection of messages (full texts, paragraphs, or statements) using freely available software and/or SPSS for the relevant statistics and the visualization. The techniques are discussed in the various theoretical contexts of (i) linguistics (e.g., Latent Semantic Analysis), (ii) sociocybernetics and social systems theory (e.g., the communication of meaning), and (iii) communication studies (e.g., framing and agenda-setting). The authors distinguish between the communication of information in the network space (social network analysis) and the communication of meaning in the vector space. The vector space can be considered a generated as an architecture by the network of relations in the network space; words are then not only related, but also positioned. These positions are expected rather than observed, and therefore one can communicate meaning. Knowledge can be generated when these meanings can recursively be communicated and therefore also further codified.

scholarly journals Content Analysis and the Measurement of Meaning: The Visualization of Frames in Collections of Messages

2011 ◽  
Vol 3 (1) ◽  
pp. 28-50 ◽  
Author(s):  
Esther Vlieger ◽  
Loet Leydesdorff
Keyword(s):  
Content Analysis ◽  
Social Network ◽  
Network Analysis ◽  
Vector Space ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Social Systems ◽  
Communication Studies ◽  
Semantic Map ◽  
Communication Of Meaning

A step-to-step introduction is provided on how to generate a semantic map from a collection of messages (full texts, paragraphs or statements) using freely available software and/or SPSS for the relevant statistics and the visualization. The techniques are discussed in the various theoretical contexts of (i) linguistics (e.g., Latent Semantic Analysis), (ii) sociocybernetics and social systems theory (e.g., the communication of meaning), and (iii) communication studies (e.g., framing and agenda-setting). We distinguish between the communication of information in the network space (social network analysis) and the communication of meaning in the vector space. The vector space can be considered as the space in which the network of relations spans an architecture; words then are not only related, but also positioned. These positions are expected rather than observed and therefore one can communicate meaning.

Questions to the Article of Telehealth During the COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
CHIEN WEI
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Citation Analysis ◽  
Recent Article ◽  
Semantic Analysis ◽  
The World ◽  
Choropleth Map ◽  
Citation Metrics ◽  
Number Of Citations

UNSTRUCTURED The recent article published on December 1 in 2020 is well-written but remains several questions that are required to clarifications further, including (1) how many out of those 543 articles were published in Pubmed, (2) whether visualizations can be applied to the study, particularly, with citation analysis, and (3)the article lacks the method to category the overall sentiment about the usefulness of telehealth and analysis to quantify the research contributions in countries/regions to the world. We replicated a study using the similar collected articles in Pubmed to (1) visualize the research contributions in countries/regions and journals using citation metrics, and (2) demonstrate the semantic analysis applied to category article topics related to journals and citations. A total of 514 similar articles extracted from the previous study were collected to match the number of citations in Pubmed. The x-index was used to evaluate research contributions to the COVID-19 epidemic for countries/regions and journals shown on a choropleth map and Kano diagram, respectively. The semantic analysis was performed using abstracts to category article topics related to journals using social network analysis.

scholarly journals Usage of Social Network Analysis (Sna) in Crisis Management

2016 ◽  
Vol 2 (1) ◽  
pp. 72-88
Author(s):  
Hakan Güreşci ◽  
Recep Sait Arpat
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Crisis Management ◽  
Social Systems ◽  
Holistic Approach ◽  
Current Crisis ◽  
Critical Data ◽  
Political Crises ◽  
Management Concepts

Abstract Social systems are complex structures that consist of different sub-systems. Therefore, understanding social systems is more difficult than comprehending electronic or mechanical systems. What makes social systems more complex than other systems is that society is not simply the sum of each individual in the society. In the current global system, the countries, which have become small villages, try to meet national security needs by converting the unknown to known and identifying the correlation among political, military, social and economic events. The current crisis management concepts are conducted through systematic approaches. Besides, the management of social, economic and political crises need to be conducted in a holistic approach covering all sub-systems. At this point, the function of Social Network Analysis (SNA) emerges. SNA, which forms the main subject of this paper, is a tool for examining the structure of a crisis through correlating the sub-elements. The aim of this study is to show how SNA can be used in crisis management. First, SNA is performed on a generic crisis situation and the results are presented. Then, the additional critical data requirements are put forward to manage the crisis effectively.

Social Network Analysis

2018 ◽  
pp. 388-415
Author(s):  
Soufiana Mekouar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Communication Systems ◽  
Information Diffusion ◽  
Social Systems ◽  
Critical Role ◽  
Network Applications ◽  
Graph Measure ◽  
Statistical Graph

The study of social network analysis has grown in popularity in the past decades and has been used in many areas. It is an interesting and useful field that gained an increasing popularity due to the explosion of social media that has emerged with advances in communication systems, which play a critical role in forming human activities and interactions in social systems. The authors present some techniques from a data mining perspective and statistical graph measure that can be used in various applications such as to perform community detection, clustering in a social network, identify spurious and anomalous users, predict links between vertices in a social network, model and improve the information diffusion, design trust models, and improve other applications. Then, the authors provide a recent literature review of such applications and thus outline challenges of social network applications.

scholarly journals Predicting Politician’s Supporters’ Network on Twitter Using Social Network Analysis and Semantic Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Asif Khan ◽  
Huaping Zhang ◽  
Jianyun Shang ◽  
Nada Boudjellal ◽  
Arshad Ahmad ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Diffusion ◽  
Semantic Analysis ◽  
Machine Learning Techniques ◽  
Political Leader ◽  
Political Leaders ◽  
Social Media Networks ◽  
Learning Techniques

Politics is one of the hottest and most commonly mentioned and viewed topics on social media networks nowadays. Microblogging platforms like Twitter and Weibo are widely used by many politicians who have a huge number of followers and supporters on those platforms. It is essential to study the supporters’ network of political leaders because it can help in decision making when predicting their political futures. This study focuses on the supporters’ network of three famous political leaders of Pakistan, namely, Imran Khan (IK), Maryam Nawaz Sharif (MNS), and Bilawal Bhutto Zardari (BBZ). This is done using social network analysis and semantic analysis. The proposed method (1) detects and removes fake supporter(s), (2) mines communities in the politicians’ social network(s), (3) investigates the supporters’ reply network for conversations between supporters about each leader, and, finally, (4) analyses the retweet network for information diffusion of each political leader. Furthermore, sentiment analysis of the supporters of politicians is done using machine learning techniques, which ultimately predicted and revealed the strongest supporter network(s) among the three political leaders. Analysis of this data reveals that as of October 2017 (1) IK was the most renowned of the three politicians and had the strongest supporter’s community while using Twitter in a very controlled manner, (2) BBZ had the weakest supporters’ network on Twitter, and (3) the supporters of the political leaders in Pakistan are flexible on Twitter, communicating with each other, and that any group of supporters has a low level of isolation.

scholarly journals Keyword extraction for film reviews based on social network analysis and natural language technology

2020 ◽  
Vol 189 ◽  
pp. 03019
Author(s):  
Quan Yanan ◽  
Tan Fuqiang
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Analysis ◽  
Semantic Network ◽  
Keyword Extraction ◽  
Main Stream ◽  
Language Technology

At present, there are many movie reviews appear on main stream websites, and these evaluations are quite different to the same movie. As a customer, how to choose your favorite movie and television program? To solve this problem, this study attempts to use the semantic analysis of word vectors (Word2vec) semantic analysis in machine learning as a research tool to mine a large number of movie reviews. The research shows that most movie reviews have a certain theme cohesion and their semantic network has quite connected. Through the use of social network analysis and the use of Word2vec word vector technology in natural language processing, it is possible to present a streamlined movie review based on movie review network semantics and keyword extraction, thus helping to select the favorite movie review.

Sign in / Sign up

Export Citation Format

Share Document