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Author: search.filters.author.Chauhan, Gajendra SinghSubject: search.filters.subject.Deep Learning (DL)

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    FakeExpose: Uncovering the falsity of news by targeting the multimodality via transfer learning
    (Taru Publications, 2023-08) Chauhan, Gajendra Singh; Sharma, Yashvardhan
    Social media for news utilization has its own pros and cons. There are several reasons why people look for and read news through internet media. On the one hand, it is easier to access, and on the other, social media’s dynamic content and misinformation pose serious problems for both government and public institutions. Several studies have been conducted in the past to classify online reviews and their textual content. The current paper suggests a multimodal strategy for the (FND) task that covers both text and image. The suggested model (FakeExpose) is created to automatically learn a variety of discriminative features, instead of relying on manually created features. Several pre-trained words and image embedding models, such as DistilRoBERTa and Vision Transformers (ViTs) are used and fine-tined for the best feature extraction and the various word dependencies. Data augmentation is used to address the issue of pre-trained textual feature extractors not processing a maximum of 512 tokens at a time. The accuracy of the presented model on PolitiFact and GossipCop is 91.35 percent and 98.59 percent, respectively, based on current standards. According to our knowledge, this is the first attempt to use the FakeNewsNet repository to reach the maximum multimodal accuracy. The results show that combining text and image data improves accuracy when compared to utilizing only text or images (Unimodal). Moreover, the outcomes imply that adding more data has improved the model’s accuracy rather than degraded it.
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    FakeRevealer: A Multimodal Framework for Revealing the Falsity of Online Tweets Using Transformer-Based Architectures
    (Scitepress, 2023) Sharma, Yashvardhan; Chauhan, Gajendra Singh
    As the Internet has evolved, the exposure and widespread adoption of social media concepts have altered the way news is formed and published. With the help of social media, getting news is cheaper, faster, and easier. However, this has also led to an increase in the number of fake news articles, either by manipulating the text or morphing the images. The spread of fake news has become a serious issue all over the world. In one case, at least 20 people were killed just because of false information that was circulated over a social media platform. This makes it clear that social media sites need a system that uses more than one method to spot fake news stories. To solve this problem, we’ve come up with FakeRevealer, a single-configuration fake news detection system that works on transfer learning based techniques. Our multi-modal archutecture understands the textual features using a language transformer model called DistilRoBERTa and image features are extracted using the Vision Transf ormer (ViTs) that is pre-trained on ImageNet 21K. After feature extraction, a cosine similarity measure is used to fuse both the features. The evaluation of our proposed framework is done over publicly available twitter dataset and results shows that it outperforms current state-of-art on twitter dataset with an accuracy of 80.00% which is 2.23%more, that than the current state-of-art on twitter dataset