loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Mahdi Washha 1 ; Dania Shilleh 2 ; Yara Ghawadrah 2 ; Reem Jazi 2 and Florence Sedes 1

Affiliations: 1 University of Toulouse, France ; 2 Birzeit University

Keyword(s): Twitter, Social Networks, Spam.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Enterprise Information Systems ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Society, e-Business and e-Government ; Software Agents and Internet Computing ; Symbolic Systems ; User Profiling and Recommender Systems ; Web 2.0 and Social Networking Controls ; Web Information Systems and Technologies

Abstract: Online social networks (OSNs) provide data valuable for a tremendous range of applications such as search engines and recommendation systems. However, the easy-to-use interactive interfaces and low barriers of publications have exposed various information quality (IQ) problems, decreasing the quality of user-generated content (UGC) in such networks. The existence of a particular kind of ill-intentioned users, so-called social spammers, imposes challenges to maintain an acceptable level of information quality. Social spammers simply misuse all services provided by social networks to post spam contents in an automated way. As a natural reaction, various detection methods have been designed, which inspect individual posts or accounts for the existence of spam. The major limitations of these methods are supervised learning-based requiring ground truth data-sets. Moreover, the account-based detection methods are not practical for processing ”crawled” large collections of social posts, req uiring months to process such collections. Post-level detection methods also have another drawback in adapting robustly the dynamic behavior of spammers because of the weakness of features in discriminating among spam and non-spam, although of applicability of such methods in regards of time. Hence, in this paper, we introduce a design of an unsupervised learning approach dedicated for detecting spam accounts (or users) existing in large collections of trending topics, from a collective perspective point of view. More precisely, our method leverages the available simple meta-data about users and the published posts (tweets) related to a topic, as heuristic information, to find any correlation among spam users acting as a spam campaign. Compared to the supervised learning methods, our experimental evaluation demonstrates the efficiency of predicting spam accounts (users) in terms of accuracy, precision, recall, and F-measure performance metrics. (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 3.219.167.163

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:
Washha, M.; Shilleh, D.; Ghawadrah, Y.; Jazi, R. and Sedes, F. (2017). Information Quality in Online Social Networks: A Fast Unsupervised Social Spam Detection Method for Trending Topics. In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-248-6; ISSN 2184-4992, SciTePress, pages 663-675. DOI: 10.5220/0006372006630675

@conference{iceis17,
author={Mahdi Washha. and Dania Shilleh. and Yara Ghawadrah. and Reem Jazi. and Florence Sedes.},
title={Information Quality in Online Social Networks: A Fast Unsupervised Social Spam Detection Method for Trending Topics},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2017},
pages={663-675},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006372006630675},
isbn={978-989-758-248-6},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Information Quality in Online Social Networks: A Fast Unsupervised Social Spam Detection Method for Trending Topics
SN - 978-989-758-248-6
IS - 2184-4992
AU - Washha, M.
AU - Shilleh, D.
AU - Ghawadrah, Y.
AU - Jazi, R.
AU - Sedes, F.
PY - 2017
SP - 663
EP - 675
DO - 10.5220/0006372006630675
PB - SciTePress