An NLP-Powered Human Rights Monitoring Platform

Alhelbawy, Ayman; Lattimer, Mark; Kruschwitz, Udo; Fox, Chris; Poesio, Massimo

doi:10.1016/j.eswa.2020.113365

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…In some works, the crowdworkers are evaluated, without notifying them, by incorporating texts from the pre-annotated sample into each crowdworker task. Less accurate crowdworkers are disqualified based on comparison with the expert labels (Albadi et al, 2018(Albadi et al, , 2022Alhelbawy et al, 2016;Chowdhury et al, 2020;Mubarak, Hassan, & Chowdhury, 2022;Shannag et al, 2022). Another approach is selecting crowdworkers with good reputation scores, which are provided on the crowdsourcing platform (Ousidhoum et al, 2019).…”

Section: Quality and Validationmentioning

confidence: 99%

“…Some researchers may opt to include in the dataset post IDs instead of the actual text due to various considerations, often centred around respecting social media platforms' policies (most notably Twitter). This is the case of 8 datasets in our collection Albadi et al, 2018Albadi et al, , 2019Alhelbawy et al, 2016;Alsafari et al, 2020;Alshalan & Al-Khalifa, 2020;Jamal et al, 2015;Nakov et al, 2021). When a dataset only contains post IDs, it becomes vulnerable to data loss due to post deletion.…”

Section: Textual Content Availabilitymentioning

confidence: 99%

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Toxic language detection: A systematic review of Arabic datasets

Bensalem,

Rosso,

Zitouni

2024

Expert Systems

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The detection of toxic language in the Arabic language has emerged as an active area of research in recent years, and reviewing the existing datasets employed for training the developed solutions has become a pressing need. This paper offers a comprehensive survey of Arabic datasets focused on online toxic language. We systematically gathered a total of 54 available datasets and their corresponding papers and conducted a thorough analysis, considering 18 criteria across four primary dimensions: availability details, content, annotation process, and reusability. This analysis enabled us to identify existing gaps and make recommendations for future research works. For the convenience of the research community, the list of the analysed datasets is maintained in a GitHub repository.

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Section: Quality and Validationmentioning

confidence: 99%

Section: Textual Content Availabilitymentioning

confidence: 99%

Toxic language detection: A systematic review of Arabic datasets

Bensalem,

Rosso,

Zitouni

2024

Expert Systems

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“…Various classifiers have been applied on social media articles to classify news as fake using NLP techniques such as N-gram and CNN [9] or bag-of-words [10]. It has been especially worthwhile to apply NLP on human rights related social media articles [11].…”

Section: A Nlp Approaches To Recent Problemsmentioning

confidence: 99%

“…Ref [13] incorporates a live mining phase, which extracts further information about an article from corresponding news articles. The model in [11] supports user involvement, and [6] uses unsupervised learning to capture exact representations of data from a user without any human bias. These methods can be incorporated in studies aiming to detect false information during the COVID-19 pandemic more for accurate and effective results.…”

Section: A Nlp Approaches To Recent Problemsmentioning

confidence: 99%

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Understanding the Applications of Natural Language Processing on COVID-19 Data

Verma

Paul

Kariyannavar

et al. 2020

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

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In the digital age, the Internet has enabled the circulation of ideas and information and, in turn, has increased awareness among people. However, this does not come with its drawbacks. With the proliferation of online platforms, hoaxers can easily lure people towards their propagandist views or false news. The need to root out such false information and hate speech during this COVID-19 pandemic has never been more essential. The following study presents a survey of various papers that attempt to tackle similar problem statements with fake news, sentiment classification, and topic extraction. The paper focuses on how existing quality research can help improve the current state of research on COVID-19 related datasets by guiding researchers towards valuable procedures to help governmental authorities combat the rise in the spread of false news and malicious and hate comments.

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