Data Mining in Social Network

2018 ◽  
pp. 53-63 ◽  
Author(s):  
Flora Amato ◽  
Giovanni Cozzolino ◽  
Francesco Moscato ◽  
Vincenzo Moscato ◽  
Antonio Picariello ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Network

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

Collecting Qualitative Data for Social Network Analysis and Data Mining

2014 ◽  
pp. 124-133
Author(s):  
Nancy L. Leech ◽  
Kathleen M. T. Collins ◽  
Anthony J. Onwuegbuzie
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Qualitative Data

scholarly journals Digital sources: a case study of the analysis of the Recovery of Historical Memory in Spain on the social network Twitter

2019 ◽  
Vol 7 (2) ◽  
pp. 015 ◽  
Author(s):  
Mariluz Congosto
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Historical Memory ◽  
Historical Research ◽  
Huge Amount ◽  
Online Social Media ◽  
The Social ◽  
Special Relevance

The incorporation of digital sources from online social media into historical research brings great opportunities, although it is not without technological challenges. The huge amount of information that can be obtained from these platforms obliges us to resort to the use of quantitative methodologies in which algorithms have special relevance, especially regarding network analysis and data mining. The Recovery of Historical Memory in Spain on the social network Twitter will be analysed in this article. An open-code tool called T-Hoarder was used; it is based on objectivity, transparency and knowledge-sharing. It has been in use since 2012.

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