loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Authors: Georgios Drakopoulos 1 ; Andreas Kanavos 2 ; 3 ; Konstantinos Paximadis 3 ; Aristidis Ilias 2 ; Christos Makris 2 and Phivos Mylonas 1

Affiliations: 1 Department of Informatics, Ionian University, Corfu, Greece ; 2 Computer Engineering and Informatics Department, University of Patras, Patras, Greece ; 3 Hellenic Open University, Patras, Greece

ISBN: 978-989-758-478-7

ISSN: 2184-3252

Keyword(s): Higher Order Metrics, Web Trust, Distributed Classification, MLlib, Apache Spark, PySpark, Twitter.

Abstract: Although trust is predominantly a human trait, it has been carried over to the Web almost since its very inception. Given the rapid Web evolution to a true melting pot of human activity, trust plays a central role since there is a massive number of parties interested in interacting in a multitude of ways but have little or even no reason to trust a priori each other. This has led to schemes for evaluating Web trust in contexts such as e-commerce, social media, recommender systems, and e-banking. Of particular interest in social networks are classification methods relying on network-dependent attributes pertaining to the past online behavior of an account. Since the deployment of such methods takes place at Internet scale, it makes perfect sense to rely on distributed processing platforms like Apache Spark. An added benefit of distributed platforms is paving the way algorithmically and computationally for higher order Web trust metrics. Here a Web trust classifier in MLlib, the machine learning library for Apache Spark, is presented. It relies on both the account activity but also on that of similar accounts. Three datasets obtained from topic sampling regarding trending Twitter topics serve as benchmarks. Based on the experimental results best practice recommendations are given. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.206.177.17

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Drakopoulos, G.; Kanavos, A.; Kanavos, A.; Paximadis, K.; Ilias, A.; Makris, C. and Mylonas, P. (2020). Computing Massive Trust Analytics for Twitter using Apache Spark with Account Self-assessment. In Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS, ISBN 978-989-758-478-7 ISSN 2184-3252, pages 403-414. DOI: 10.5220/0010214104030414

@conference{dmmlacs20,
author={Georgios Drakopoulos. and Andreas Kanavos. and Andreas Kanavos. and Konstantinos Paximadis. and Aristidis Ilias. and Christos Makris. and Phivos Mylonas.},
title={Computing Massive Trust Analytics for Twitter using Apache Spark with Account Self-assessment},
booktitle={Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,},
year={2020},
pages={403-414},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010214104030414},
isbn={978-989-758-478-7},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,
TI - Computing Massive Trust Analytics for Twitter using Apache Spark with Account Self-assessment
SN - 978-989-758-478-7
IS - 2184-3252
AU - Drakopoulos, G.
AU - Kanavos, A.
AU - Kanavos, A.
AU - Paximadis, K.
AU - Ilias, A.
AU - Makris, C.
AU - Mylonas, P.
PY - 2020
SP - 403
EP - 414
DO - 10.5220/0010214104030414

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.