Title: Algerian Arabizi rumour detection based on morphosyntactic analysis

Authors: Chahnez Zakaria; Kamel Smaïli; Besma Sahnoun; Assia Chala; Radjaa Agagna; Célia Amirat

Addresses: LMCS Laboratory, National School of Computer Science, BP 68M, 16270, Oued-Smar, Algeria ' LORIA Scientific Campus BP 239, Vandoeuvre Les Nancy 54506, France ' National School of Computer Science, BP 68M, 16270, Oued-Smar, Algeria ' National School of Computer Science, BP 68M, 16270, Oued-Smar, Algeria ' National School of Computer Science, BP 68M, 16270, Oued-Smar, Algeria ' National School of Computer Science, BP 68M, 16270, Oued-Smar, Algeria

Abstract: Social networks have become a customary news media source in recent times. However, the openness and unrestricted way of sharing information on social networks fosters spreading rumours which may cause severe damages economically, socially, etc. Motivated by this, our paper focuses on the rumour detection problem in Algerian Arabizi. Studying linguistic rules of Algerian Arabizi, we propose a lemmatiser and a parser for analysing and standardising the text to produce better rumour detection models. An approach for classifying rumours and news in social networks based on emotions' expression and users' positions is proposed. The experiments were done on many ngram representations where the best one has reached more than 94% of F-score. In addition to that this research deals with resources creation for Algerian Arabizi which is an under-resourced dialect. A corpus and several lexicons have been built, which can be the subject of other works dealing with this dialect.

Keywords: social medias; rumour detection; lemmatiser; parser; Arabizi; machine learning; Arabizi corpus; resource building; social networks; morphosyntactic analyser; emotion lexicon and position lexicon.

DOI: 10.1504/IJKEDM.2023.135716

International Journal of Knowledge Engineering and Data Mining, 2023 Vol.8 No.1, pp.43 - 66

Received: 09 Oct 2022
Accepted: 16 Jan 2023
Published online: 03 Jan 2024 *

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