Authors:
Athanasios Lyras
1
;
Sotiria Vernikou
1
;
Andreas Kanavos
2
;
Spyros Sioutas
1
and
Phivos Mylonas
3
Affiliations:
1
Computer Engineering and Informatics Department, University of Patras, Patras, Greece
;
2
Department of Digital Media and Communication, Ionian University, Kefalonia, Greece
;
3
Department of Informatics, Ionian University, Corfu, Greece
Keyword(s):
Big Data, Deep Learning, Deep Learning Neural Networks, LSTM, Natural Language Processing, Social Media, Text Mining, Trust Modeling, Twitter.
Abstract:
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 predict
s user credibility, is accessed for its worth in terms of model accuracy.
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