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6: Categorization of Deep Neural Networks

6: Categorization of Deep Neural Networks

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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...


... As such, it is important to keep social media and other communication platforms free from offensive content. Considerable research has been conducted on deep-learning techniques for detecting offensive language (Pitsilis et al., 2018;Mehra and Hasanuzzaman, 2020). One of the growing challenges in the field of content detection is code-switching (Aguilar et al., 2020; 1 2 ...
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Conference Paper
The prevalent use of offensive content in social media has become an important reason for concern for online platforms (customer service chat-boxes, social media platforms, etc). Classifying offensive and hate-speech content in on-line settings is an essential task in many applications that needs to be addressed accordingly. However, online text from online platforms can contain code-switching, a combination of more than one language. The non-availability of labeled code-switched data for low-resourced code-switching combinations adds difficulty to this problem. To overcome this, we release a human-generated dataset containing around 10k samples for testing for three language combinations en-fr, en-es, and en-de 1 and a synthetic code-switched dataset containing 30k samples for training 2. In this paper, we describe the process for gathering the human-generated data and our algorithm for creating synthetic code-switched offensive content data. We also introduce the results of a keyword classification baseline and a multilingual transformer-based classification model.
... 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. ...
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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.