Search Results for author: Tamer Elsayed

Found 18 papers, 5 papers with code

AraFacts: The First Large Arabic Dataset of Naturally Occurring Claims

1 code implementation EACL (WANLP) 2021 Zien Sheikh Ali, Watheq Mansour, Tamer Elsayed, Abdulaziz Al‐Ali

We introduce AraFacts, the first large Arabic dataset of naturally occurring claims collected from 5 Arabic fact-checking websites, e. g., Fatabyyano and Misbar, and covering claims since 2016.

Fact Checking

Automated Fact-Checking for Assisting Human Fact-Checkers

no code implementations13 Mar 2021 Preslav Nakov, David Corney, Maram Hasanain, Firoj Alam, Tamer Elsayed, Alberto Barrón-Cedeño, Paolo Papotti, Shaden Shaar, Giovanni Da San Martino

The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism.

Fact Checking

Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets

no code implementations COLING 2020 Reem Suwaileh, Muhammad Imran, Tamer Elsayed, Hassan Sajjad

For example, results show that, for training a location mention recognition model, Twitter-based data is preferred over general-purpose data; and crisis-related data is preferred over general-purpose Twitter data.

ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection

no code implementations EACL (WANLP) 2021 Fatima Haouari, Maram Hasanain, Reem Suwaileh, Tamer Elsayed

In this paper we introduce ArCOV19-Rumors, an Arabic COVID-19 Twitter dataset for misinformation detection composed of tweets containing claims from 27th January till the end of April 2020.

Fact Checking Misinformation

Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media

3 code implementations15 Jul 2020 Alberto Barron-Cedeno, Tamer Elsayed, Preslav Nakov, Giovanni Da San Martino, Maram Hasanain, Reem Suwaileh, Fatima Haouari, Nikolay Babulkov, Bayan Hamdan, Alex Nikolov, Shaden Shaar, Zien Sheikh Ali

The first four tasks compose the full pipeline of claim verification in social media: Task 1 on check-worthiness estimation, Task 2 on retrieving previously fact-checked claims, Task 3 on evidence retrieval, and Task 4 on claim verification.

Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey

1 code implementation19 May 2020 Ammar Rashed, Mucahid Kutlu, Kareem Darwish, Tamer Elsayed, Cansin Bayrak

On June 24, 2018, Turkey conducted a highly consequential election in which the Turkish people elected their president and parliament in the first election under a new presidential system.

Stance Classification Stance Detection

Quick and Simple Approach for Detecting Hate Speech in Arabic Tweets

no code implementations LREC 2020 Abeer Abuzayed, Tamer Elsayed

To tackle the problem efficiently, we adopt a {``}quick and simple{''} approach by which we investigate the effectiveness of 15 classical (e. g., SVM) and neural (e. g., CNN) learning models, while exploring two different term representations.

Overview of OSACT4 Arabic Offensive Language Detection Shared Task

no code implementations LREC 2020 Hamdy Mubarak, Kareem Darwish, Walid Magdy, Tamer Elsayed, Hend Al-Khalifa

This paper provides an overview of the offensive language detection shared task at the 4th workshop on Open-Source Arabic Corpora and Processing Tools (OSACT4).

ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks

1 code implementation EACL (WANLP) 2021 Fatima Haouari, Maram Hasanain, Reem Suwaileh, Tamer Elsayed

In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that spans one year, covering the period from 27th of January 2020 till 31st of January 2021.

Information Retrieval Natural Language Processing

Embedding-based Qualitative Analysis of Polarization in Turkey

no code implementations23 Sep 2019 Mucahid Kutlu, Kareem Darwish, Cansin Bayrak, Ammar Rashed, Tamer Elsayed

During the election period, the Turkish people extensively shared their political opinions on Twitter.

Social and Information Networks

Simple But Not Na\"\ive: Fine-Grained Arabic Dialect Identification Using Only N-Grams

no code implementations WS 2019 Sohaila Eltanbouly, May Bashendy, Tamer Elsayed

This paper presents the participation of Qatar University team in MADAR shared task, which addresses the problem of sentence-level fine-grained Arabic Dialect Identification over 25 different Arabic dialects in addition to the Modern Standard Arabic.

Dialect Identification

Efficient Test Collection Construction via Active Learning

no code implementations17 Jan 2018 Md Mustafizur Rahman, Mucahid Kutlu, Tamer Elsayed, Matthew Lease

To create a new IR test collection at low cost, it is valuable to carefully select which documents merit human relevance judgments.

Active Learning

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