An Innovative Framework for Supporting Social Network Polluting-content Detection and Analysis

Alfredo Cuzzocrea, Fabio Martinelli, Francesco Mercaldo

Abstract

In last years we are witnessing a growing interest in tools for analyzing big data gathered from social networks in order to find common opinions. In this context, content polluters on social networks make the opinion mining process difficult to browse valuable contents. In this paper we propose a method aimed to discriminate between pollute and real information from a semantic point of view. We exploit a combination of word embedding and deep learning techniques to categorize semantic similarities between (pollute and real) linguistic sentences. We experiment the proposed method on a data set of real-world sentences obtaining interesting results in terms of precision and recall.

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Paper Citation


in Harvard Style

Cuzzocrea A., Martinelli F. and Mercaldo F. (2019). An Innovative Framework for Supporting Social Network Polluting-content Detection and Analysis.In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-372-8, pages 303-311. DOI: 10.5220/0007737403030311


in Bibtex Style

@conference{iceis19,
author={Alfredo Cuzzocrea and Fabio Martinelli and Francesco Mercaldo},
title={An Innovative Framework for Supporting Social Network Polluting-content Detection and Analysis},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2019},
pages={303-311},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007737403030311},
isbn={978-989-758-372-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - An Innovative Framework for Supporting Social Network Polluting-content Detection and Analysis
SN - 978-989-758-372-8
AU - Cuzzocrea A.
AU - Martinelli F.
AU - Mercaldo F.
PY - 2019
SP - 303
EP - 311
DO - 10.5220/0007737403030311