Browsing by Author "Alzahrani, Amani"

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Exploring Changes in Translation Practices among Natural and Trainee Translators in Bilingual Society
(Cardiff University, 2024-01-24) Alzahrani, Amani; Federici, Theresa
This MA dissertation explores a comparative study of translation processes practised by "natural" bilingual translators and translation learners, who will henceforth be referred to as trainee translators. The term "natural" translators is derived from Harris and Sherwood's explanation, which describes them as those performing translation in daily scenarios by individuals without any specialised training (1978). This research project investigates bilingualism within the setting of Arab countries and its influence on the act of translation. The research explores the primary working units that participants use and the dominant strategies they adopt when translating culturally specific elements from English to Arabic. Additionally, the research delves into the perspectives of the groups regarding translation and a translator's role, inspired by Tymoczko's urge to broaden their comprehension of translation to keep up with how the world is changing (2009). As the field should expand beyond its current limitations. To fully comprehend the translation process, the research project includes translation tasks. The various translation working units used by the groups serve as an illustration of the value of formal translation training. The trainee translators often merge multiple translation unit levels as they tackle longer units. In contrast, natural translators usually focus on smaller units, though some handle larger ones. The research examines the potential effects of time constraints and direct observation on the strategies employed in translation. The research project underscores the translators' proactive roles and their profound respect for the text's authority.
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MISINFORMATION DETECTION IN THE SOCIAL MEDIA ERA
(Howard University, 2024-04-22) Alzahrani, Amani; Rawat, Danda B.
As social media becomes the main way of getting information, the spread of misinformation is a serious and widespread problem. Misinformation can take many forms, such as text, video, and audio, and it can travel quickly through different platforms, affecting the quality and trustworthiness of the information that users access around the world. Misinformation can have negative effects on how people think, act, and interact, and it can even endanger social peace. This study aims to tackle the complex problem of misinformation by presenting a comprehensive approach that addresses various forms of deceptive content on social media with a focus on Twitter ( currently X). Twitter stands out as a dynamic and influential microblogging service that enables users to share real-time updates, news, and opinions in concise 280-character messages known as tweets. We introduce a hybrid deep learning model that incorporates Feature-based models at both tweet and user levels, complemented by pre-trained text embedding models such as Global Vectors (GloVe) and Universal Sentence Encoders (USE). Through careful evaluation on a real-world dataset, our approach proves effective in detecting textual misinformation. Recognizing the vital need to verify the reliability of information on social media, we propose a method to assess user credibility. Our solution involves evaluating the credibility of users based on their profiles to enhance the rumors detection model. This study proposes a novel mechanism for assessing a user’s credibility. Additionally, we extended our study capabilities to address the challenges posed by deceptive video content spread on social media using DeepFake technology. As the rapid advancement of deepfake technology threatens the integrity of audio and video content, we present a novel approach combining Optical Flow (OF) algorithms with a Convolutional Neural Network (CNN) to enhance deepfake video detection. This comprehensive strategy addresses the diverse challenges posed by misinformation, credibility assessment, and deepfake detection in the dynamic landscape of social media.
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