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1: Distribution of 3 levels of annotation in OLID

1: Distribution of 3 levels of annotation in OLID

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Thesis
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Posting offensive or abusive content on social media have been a serious concern in recent years. This has created a lot of problems because of the huge popularity and usage of social media sites like Facebook and Twitter. The main motivation lies in the fact that our model will automate and accelerate the detection of the posted offensive content...

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There have been many efforts to detect rumors using various machine learning (ML) models, but there is still a lack of understanding of their performance against different rumor topics and available features, resulting in a significant performance degrade against completely new and unseen (unknown) rumors. To address this issue, we investigate the...

Citations

... Today's digital world generates an incredible amount of data. To carry out our tasks, all of these many aspects, including the web, sensors, software, gadgets, and several other variables, all give birth to vast amounts of organized, unstructured, and semi-structured data [8]. Data is a new type of oil that is essential but requires more processing before it can be used. ...
... In the current technological context, machine learning is one of the most significant fields of artificial intelligence. Machines are developed in such a manner that they can learn and comprehend from the enormous quantities of data that are accessible to them [8]. ML begins with a training phase, followed by a decision phase. ...
Preprint
Recent studies show that social media has become an integral part of everyone's daily routine. People often use it to convey their ideas, opinions, and critiques. Consequently, the increasing use of social media has motivated malicious users to misuse online social media anonymity. Thus, these users can exploit this advantage and engage in socially unacceptable behavior. The use of inappropriate language on social media is one of the greatest societal dangers that exist today. Therefore, there is a need to monitor and evaluate social media postings using automated methods and techniques. The majority of studies that deal with offensive language classification in texts have used English datasets. However, the enhancement of offensive language detection in Arabic has gotten less consideration. The Arabic language has different rules and structures. This article provides a thorough review of research studies that have made use of artificial intelligence (AI) for the identification of Arabic offensive language in various contexts.