Modeling Credibility in Social Big Data using LSTM Neural Networks

Athanasios Lyras, Sotiria Vernikou, Andreas Kanavos, Spyros Sioutas, Phivos Mylonas

2021

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 predicts user credibility, is accessed for its worth in terms of model accuracy.

Paper Citation

in Harvard Style

Lyras A., Vernikou S., Kanavos A., Sioutas S. and Mylonas P. (2021). Modeling Credibility in Social Big Data using LSTM Neural Networks. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS, ISBN 978-989-758-536-4, pages 599-606. DOI: 10.5220/0010726600003058

in Bibtex Style

@conference{dmmlacs21,
author={Athanasios Lyras and Sotiria Vernikou and Andreas Kanavos and Spyros Sioutas and Phivos Mylonas},
title={Modeling Credibility in Social Big Data using LSTM Neural Networks},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,},
year={2021},
pages={599-606},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010726600003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,
TI - Modeling Credibility in Social Big Data using LSTM Neural Networks
SN - 978-989-758-536-4
AU - Lyras A.
AU - Vernikou S.
AU - Kanavos A.
AU - Sioutas S.
AU - Mylonas P.
PY - 2021
SP - 599
EP - 606
DO - 10.5220/0010726600003058