research-article

Using Arabic Microblogs Features in Determining Credibility

Authors:

Amal Abdullah AlMansour,

Costas S. IliopoulosAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 1212 - 1219

https://doi.org/10.1145/2808797.2808819

Published: 25 August 2015 Publication History

Abstract

The increased usage of Twitter as a medium for reporting news and sharing information between people has caught the attention of researchers from different disciplines. One of the research directions is the analysis of online information from the perspective of its credibility. This paper aims to assess and analyze the credibility of tweets in Arabic language. In order to achieve the stated goal, first we employ the idea of crowdsourcing where users can explicitly express their opinions about credibility of a set of tweets. This information coupled with the data about tweets' features enable us to investigate which features may indicate the credibility level of a tweet, e.g. tweet with attached image and was authored by a person who posts a lot of tweets will be, with high probability, a credible tweet. We distinguish three main groups of features: authority and topical expertise (of the source), data quality (of the content), and popularity (of the content and the source). We argue that content data quality factor based on content linguistic features in addition to source authority is more important than content popularity in identifying credible messages. In addition to this, we identified three experts who also rated the credibility of tweets and based on that we investigate the level of agreement between experts and the crowd, and we identify which expert represents the crowd in the best way. This can allow us to select the most representative expert when it is needed. This study is a pilot of a large study that aims at predicting credibility of Arabic Twitter messages using machine learning approaches.

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Cited By

Qureshi KMalick RSabih M(2021)Social Media and Microblogs Credibility: Identification, Theory Driven Framework, and RecommendationIEEE Access10.1109/ACCESS.2021.31144179(137744-137781)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3114417
AlMansour A(2017)Twitter features distributions across similar labelers2017 13th International Computer Engineering Conference (ICENCO)10.1109/ICENCO.2017.8289823(405-410)Online publication date: Dec-2017
https://doi.org/10.1109/ICENCO.2017.8289823
Mansour A(2016)Labeling Agreement Level and Classification Accuracy2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2016.51(271-274)Online publication date: 2016
https://doi.org/10.1109/SITIS.2016.51

Using Arabic Microblogs Features in Determining Credibility
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information retrieval

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cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 August 2015

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Cited By

Qureshi KMalick RSabih M(2021)Social Media and Microblogs Credibility: Identification, Theory Driven Framework, and RecommendationIEEE Access10.1109/ACCESS.2021.31144179(137744-137781)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3114417
AlMansour A(2017)Twitter features distributions across similar labelers2017 13th International Computer Engineering Conference (ICENCO)10.1109/ICENCO.2017.8289823(405-410)Online publication date: Dec-2017
https://doi.org/10.1109/ICENCO.2017.8289823
Mansour A(2016)Labeling Agreement Level and Classification Accuracy2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2016.51(271-274)Online publication date: 2016
https://doi.org/10.1109/SITIS.2016.51

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