scholarly journals Emergent use of Social Media on Elections: the use of Data Mining and Social Network Analysis for Political Purposes

2021 ◽  
Vol 10 (6) ◽  
pp. 118-122
Author(s):  
Acharoui Zakia ◽  
Ettaki Badia ◽  
Zerouaoui Jamal
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Support Vector ◽  
Use Of Data ◽  
Election Results ◽  
Use Of Social Media ◽  
The Impact

People spend more time on social media either for personal or social interest which generates an expanding amount of Data. This paper is written for researchers seeking to have an overview of the different technical methods used for political purposes principally Data Mining and Social Network Analysis. Hence, the first part introduces the impact of Social Media on politics for different aims such as communicating with voters, promoting participation, and predicting election results, then the two main methods to achieve political purposes were presented. Data mining approaches is likely to be used on political context to classify citizen’s opinion or predicting results thus by using methods such as term occurrence, mentions, Support Vector Machine, Machine Learning, and Artificial Neural Networks. The Social Network Analysis approaches are used to retrieve data about influencers, their role during a period, and the nature of the information shared.

Measuring the Impact of Social Media

2011 ◽  
pp. 24-36 ◽  
Author(s):  
Kimiz Dalkir
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Evaluation Method ◽  
Real Life ◽  
Work Processes ◽  
The Social ◽  
Post Test ◽  
The Impact

This chapter focuses on a method, social network analysis (SNA) that can be used to assess the quantity and quality of connection, communication and collaboration mediated by social tools in an organization. An organization, in the Canadian public sector, is used as a real-life case study to illustrate how SNA can be used in a pre-test/post-test evaluation design to conduct a comparative assessment of methods that can be used before, during and after the implementation of organizational change in work processes. The same evaluation method can be used to assess the impact of introducing new social media such as wikis, expertise locator systems, blogs, Twitter and so on. In other words, while traditional pre-test/post-test designs can be easily applied to social media, the social media tools themselves can be added to the assessment toolkit. Social network analysis in particular is a good candidate to analyze the connections between people and content as well as people with other people.

The Rise of Fashion Informatics: A Case of Data-Mining-Based Social Network Analysis in Fashion

2018 ◽  
Vol 37 (2) ◽  
pp. 87-102 ◽  
Author(s):  
Li Zhao ◽  
Chao Min
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Dynamic Network ◽  
Fashion Industry ◽  
Cognitive Computing ◽  
Data Set

With the advent of modern cognitive computing technologies, fashion informatics researchers contribute to the academic and professional discussion about how a large-scale data set is able to reshape the fashion industry. Data-mining-based social network analysis is a promising area of fashion informatics to investigate relations and information flow among fashion units. By adopting this pragmatic approach, we provide dynamic network visualizations of the case of Paris Fashion Week. Three time periods were researched to monitor the formulation and mobilization of social media users’ discussions of the event. Initial textual data on social media were crawled, converted, calculated, and visualized by Python and Gephi. The most influential nodes (hashtags) that function as junctions and the distinct hashtag communities were identified and represented visually as graphs. The relations between the contextual clusters and the role of junctions in linking these clusters were investigated and interpreted.

Social Network Analysis

2018 ◽  
pp. 1-18 ◽  
Author(s):  
Somya Jain ◽  
Adwitiya Sinha
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Analysis Techniques ◽  
Text Images ◽  
Application Specific

Over the last decade, technology has thrived to provide better, quicker, and more effective platforms to help individuals connect and disseminate information to other individuals. The increasing popularity of these networks and its huge content in the form of text, images, and videos provides new opportunities for data analytics in the context of social networks. This motivates data mining experts and researchers to deploy various mining apparatus and application-specific tools for analysing the massive, intricate, and dynamic social media knowledge. The research detailed in this chapter would entail major social network concepts with data analysis techniques. Moreover, it gives insight to representation and modelling of social networks with research datasets and tools.

scholarly journals Detecting Influencers in Social Media Using Social Network Analysis (SNA)

2018 ◽  
Vol 7 (4.38) ◽  
pp. 950 ◽  
Author(s):  
Siti Nurulain Mohd Rum ◽  
Razali Yaakob ◽  
Lilly Suriani Affendey
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Work ◽  
Modern Society ◽  
Business World ◽  
Management And Development ◽  
Use Of Social Media ◽  
Commercial Organizations

Social media has now become a key part of life in modern society; it is a place where people share their ideas, view, emotions, and sentiments. The explosion in the popularity of social media has led to an immense increase in data over the past few years. Users engage with this platform to share their experiences, feelings, and opinions on a broad range of topics, such as politics, personalities, news, products or events. Social media has also become a phenomenal platform that provides a powerful way for businesses to enhance their prospects and reach customers. Extracting and interpreting information from user-generated content is a trending topic in the scientific community as well as in the business world, and has attracted the interest of many commercial organizations. With the wise use of social media, the marketing process for promoting products and brands can be accelerated to reach the target audience. The beauty and health industry is one of the industries that make use of this platform as their digital marketing solution to integrate communications. Recently, many leading companies and brands have used digital influencers as their strategy for marketing campaigns in management and development. Therefore, the analysis of information extracted from social media is of great importance offering valuable insights and where the importance of each actor or individual in social media can be identified.  This can be achieved through the use of Social Network Analysis (SNA).  This research work aims at probing the effectiveness of SNA in social media in detecting the influencers in the area of beauty and health.   

Influence of Social Media on Enterprise Knowledge Sharing Based on Social Network Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 99-118
Author(s):  
Yongtao Deng
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Knowledge Sharing ◽  
Structural Equation Models ◽  
Intermediary Role ◽  
The Common ◽  
The Impact ◽  
Innovation Behavior

Abstract Based on the social network analysis method, this paper studies the impact of corporate knowledge-sharing in social media, sorts out the relationship between corporate internal social capital, knowledge-sharing, and individual innovation behavior, and explores the reliability of the scale through exploratory factor analysis validity test to ensure the reliability and rationality of the questionnaire, using improved social networks to construct structural equation models and regression analysis to verify the research model and related assumptions, and found that social media tacit knowledge-sharing in structural capital, cognitive capital the intermediary effect in the influence of individual innovation behavior is remarkable. The sharing attitude has a broad intermediary role between the interactive relationship and the willingness to share. There is a partial intermediary role between the reciprocity and the willingness to share, the common language, and the willingness to share, the common vision, and the willingness to share.

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