MSc Thesis Defense Announcement of Kamonashish Saha:"DeBERTNeXT: A Multimodal Fake News Detection Framework"

 

Date: Wednesday April 12, 2023

Time:  11:00 am – 12:30pm

Location: Odette Building (OB), Room 108

 

Reminders: 1. Two-part attendance mandatory (sign-in sheet, QR Code) 2. Arrive 5-10 minutes prior to event starting - LATECOMERS WILL NOT BE ADMITTED. Note that due to demand, if the room has reached capacity, even if you are "early" admission is not guaranteed. 3. Please be respectful of the presenter by NOT knocking on the door for admittance once the door has been closed whether the presentation has begun or not (If the room is at capacity, overflow is not permitted (ie. sitting on floors) as this is a violation of the Fire Safety code). 4. Be respectful of the decision of the advisor/host of the event if you are not given admittance. The School of Computer Science has numerous events occurring soon.

 

 

 

There is a rapid influx of fake news nowadays, which poses an immense threat to our society. Fake news has been impacting us in several ways which include changing our thoughts, manipulating opinions, and also causing chaos due to misinformation. With the ease of access and sharing information on social media platforms, such fake news or misinformation has been spreading in different modalities which include text, image, audio, and video. Although there have been a lot of approaches to detecting fake news in textual format only, however, multimodal approaches are less frequent as it is difficult to fully use the information derived from different modalities to achieve high accuracy in a combined format. To tackle these issues, we introduce DeBertNeXT which is a multimodal fake news detection model that utilizes both textual and visual information from an article for fake news classification. We perform experiments on the immense Fakeddit dataset and two other smaller benchmark datasets named Politifact and Gossipcop. Our model outperforms the existing models on the Fakeddit dataset by about 3.80 %, Politifact by 2.10 % and Gossipcop by 1.00 %.

 

Keywords: Multi-modal, Fake News, Transfer Learning, DeBERTa, ConvNext

 

Internal Reader: Dr. Pooya Moradian Zadeh

External Reader: Dr. Ning Zhang     

Advisor: Dr. Ziad Kobti

Chair: Dr. Kalyani Selvarajah