Search Results for author: Bilal Ghanem

Found 14 papers, 3 papers with code

DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach

no code implementations10 Apr 2023 Bilal Ghanem, Alona Fyshe

At the same time, DISTO ranks the performance of state-of-the-art DG models very differently from MT-based metrics, showing that MT metrics should not be used for distractor evaluation.

Distractor Generation Machine Translation +3

What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts

no code implementations ACL 2021 Sanja Stajner, Seren Yenikent, Bilal Ghanem, Marc Franco-Salvador

According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people{'}s everyday life and career.

Benchmarking Binary Classification +1

Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language

1 code implementation EACL (WANLP) 2021 Hala Mulki, Bilal Ghanem

Moreover, Let-Mi was used as an evaluation dataset through binary/multi-/target classification tasks conducted by several state-of-the-art machine learning systems along with Multi-Task Learning (MTL) configuration.

Multi-Task Learning

FakeFlow: Fake News Detection by Modeling the Flow of Affective Information

1 code implementation EACL 2021 Bilal Ghanem, Simone Paolo Ponzetto, Paolo Rosso, Francisco Rangel

To capture this, we propose in this paper to model the flow of affective information in fake news articles using a neural architecture.

Fake News Detection

Irony Detection in a Multilingual Context

1 code implementation6 Feb 2020 Bilal Ghanem, Jihen Karoui, Farah Benamara, Paolo Rosso, Véronique Moriceau

This paper proposes the first multilingual (French, English and Arabic) and multicultural (Indo-European languages vs. less culturally close languages) irony detection system.

FacTweet: Profiling Fake News Twitter Accounts

no code implementations15 Oct 2019 Bilal Ghanem, Simone Paolo Ponzetto, Paolo Rosso

We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features.

TexTrolls: Identifying Russian Trolls on Twitter from a Textual Perspective

no code implementations3 Oct 2019 Bilal Ghanem, Davide Buscaldi, Paolo Rosso

Our approach is mainly based on textual features which utilize thematic information, and profiling features to identify the accounts from their way of writing tweets.

An Emotional Analysis of False Information in Social Media and News Articles

no code implementations26 Aug 2019 Bilal Ghanem, Paolo Rosso, Francisco Rangel

Fake news is risky since it has been created to manipulate the readers' opinions and beliefs.

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