Modeling Credibility in Social Big Data using LSTM Neural Networks

被引:4
作者
Lyras, Athanasios [1 ]
Vernikou, Sotiria [1 ]
Kanavos, Andreas [2 ]
Sioutas, Spyros [1 ]
Mylonas, Phivos [3 ]
机构
[1] Univ Patras, Comp Engn & Informat Dept, Patras, Greece
[2] Ionian Univ, Dept Digital Media & Commun, Kefalonia, Greece
[3] Ionian Univ, Dept Informat, Corfu, Greece
来源
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES (WEBIST) | 2021年
关键词
Big Data; Deep Learning; Deep Learning Neural Networks; LSTM; Natural Language Processing; Social Media; Text Mining; Trust Modeling; Twitter;
D O I
10.5220/0010726600003058
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Communication accounts for a vital need among people in order to express and exchange ideas, emotions, messages, etc. Social media fulfill this necessity as users can make use of a variety of platforms like Twitter, to leave their digital fingerprint by uploading personal data. The ever humongous volume of users claims for evaluation and that is why the subject of user credibility or trust in a social network is equally vital and meticulously discussed in this paper. Specifically, a trust method, as we measure user credibility and trust in a social environment using user metrics, is proposed. Our dataset is derived from Twitter and consists of tweets from a popular television series. Initially, our text data are analyzed and preprocessed using NLP tools and in following, a balanced dataset that serves in model evaluation and parameter tuning, is constructed. A deep learning forecasting model, which uses LSTM/BiLSTM layers along with classic Artificial Neural Network (ANN) and predicts user credibility, is accessed for its worth in terms of model accuracy.
引用
收藏
页码:599 / 606
页数:8
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