Analyzing Social Network Data Using Deep Neural Networks: A Case Study Using Twitter Posts

2019 ◽  
Author(s):  
Wen-Hung Liao ◽  
Yen-Ting Huang ◽  
Tsu-Hsuan Yang ◽  
Yi-Chieh Wu
Keyword(s):  
Neural Networks ◽  
Social Network ◽  
Deep Neural Networks ◽  
Network Data ◽  
Social Network Data

scholarly journals Online Data Preprocessing: A Case Study Approach

2019 ◽  
Vol 9 (4) ◽  
pp. 2620
Author(s):  
Mohammed Zuhair Al-Taie ◽  
Seifedine Kadry ◽  
Joel Pinho Lucas
Keyword(s):  
Social Network ◽  
Data Preprocessing ◽  
Quality Data ◽  
The Internet ◽  
Network Data ◽  
Quality Of Data ◽  
Standard Format ◽  
Social Network Data

<span lang="EN-US">Besides the Internet search facility and e-mails, social networking is now one of the three best uses of the Internet. A tremendous number of volunteers every day write articles, share photos, videos and links at a scope and scale never imagined before. However, because social network data are huge and come from heterogeneous sources, the data are highly susceptible to inconsistency, redundancy, noise, and loss. For data scientists, preparing the data and getting it into a standard format is critical because the quality of data is going to directly affect the performance of mining algorithms that are going to be applied next. Low-quality data will certainly limit the analysis and lower the quality of mining results. To this end, the goal of this study is to provide an overview of the different phases involved in data preprocessing, with a focus on social network data. As a case study, we will show how we applied preprocessing to the data that we collected for the Malaysian Flight MH370 that disappeared in 2014.</span>

scholarly journals Deep Active Learning for Anchor User Prediction

2019 ◽  
Author(s):  
Anfeng Cheng ◽  
Chuan Zhou ◽  
Hong Yang ◽  
Jia Wu ◽  
Lei Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Social Network ◽  
Active Learning ◽  
Prediction Models ◽  
State Of The Art ◽  
Network Data ◽  
Multiple Networks ◽  
Social Network Data ◽  
Model Training ◽  
Vector Representations

Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction. To this end, we present a deep active learning model for anchor user prediction (DALAUP for short). However, active learning for anchor user sampling meets the challenges of non-i.i.d. user pair data caused by network structures and the correlation among anchor or non-anchor user pairs. To solve the challenges, DALAUP uses a couple of neural networks with shared-parameter to obtain the vector representations of user pairs, and ensembles three query strategies to select the most informative user pairs for labeling and model training. Experiments on real-world social network data demonstrate that DALAUP outperforms the state-of-the-art approaches.

Sign in / Sign up

Export Citation Format

Share Document