Department of Computer Science and Information Systems
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Item Applying Transfer Learning using BERT-Based Models for Hate Speech Detection(CEUR-WS, 2021) Sharma, Yashvardhan; Chauhan, Gajendra SinghHateful and Offensive speech is rising along with social media. This issue has motivated researchers to devise novel approaches which perform better than the traditional algorithms. This paper presents the methods adopted by the BITS Pilani team for Subtask 1A of the Hate Speech and Offensive Content Identification in English and Indo-Aryan Language task proposed by the Forum of Information Retrieval Evaluation in 2021. We have used data augmentation to make the models generalize better. We have experimented with different feature extraction techniques along with machine learning algorithms. But, fine-tuning the pre-trained BERT-based models using transfer learning gave us the best results for all the given languages on the test set. We got the highest Macro-F1 of 0.7993 for the English Language, 0.7612 for the Hindi Language, and 0.8306 for the Marathi Language using the pre-trained BERT-based models.Item Ensembling of Various Transformer Based Models for the Fake News Detection Task in the Urdu Language(CEUR-WS, 2021) Sharma, Yashvardhan; Chauhan, Gajendra SinghThe spread of misinformation has become a severe issue affecting society. Inaccurate information has enormous potential to cause real-world impacts. Developing algorithms to detect fake news automatically will be very useful in preventing unnecessary panic and damage caused by rumors. This fake news problem is present for all languages, and it becomes crucial to solve it for languages other than English, with scarce datasets. This paper aims to tackle the problem of automatic fake news detection in Urdu, a low-resource language. FIRE-2021 has provided the Urdu dataset used in this paper. We fine-tuned monolingual and multilingual transformers. After searching for hyperparameters, we tried ensembling our models. We submitted our model for the UrduFake task, and it achieved an accuracy of 0.596 and an F1- macro score of 0.449.Item Comparative Analysis of Various Text Summarization Techniques via Leveraging Transformer Model for the Fake News Detection(CRC Press, 2022) Sharma, Yashvardhan; Chauhan, Gajendra SinghToday, the internet has come to be a necessary part of our lifestyle. The role of traditional information channels consisting of newspapers and televisions on how we acquire and consume news has to become much less prominent than within the past. Indeed, the boom of social media structures has performed a critical function in this variation. Oppositely, it empowers the widespread of ”fake news,” i.e., low-quality news with purposefully false data. The broad spread of fake news has the potential for incredibly adverse effects on people and society. The proposed research work aims to design a robust model for an automatic fake news detection system to help journalists and everyday users from misleading content. In this paper, we have studied and performed a deep comparison of leading Transformer-based models for the task of text classification and explored and compared various text summarizing techniques for dealing with more prominent long-length articles before classifying them through existing models. BERTSUM gives the most noticeable results out of all the three methods by enabling us to create a system to label an arbitrarily long article as fake or genuineItem Multimodal Fake News Detection on Fakeddit Dataset Using Transformer-Based Architectures(Springer, 2023-01) Sharma, Yashvardhan; Chauhan, Gajendra SinghReal-time information is transforming due to technological advancements and widespread internet access. In our increasingly digital culture, fake news and misinformation are more common in journalism, news reporting, social media, and other online information consumption platforms. The spread of misinformation can have harmful impacts or even control public events by using multimedia content to deceive readers and gain dissemination. The question here is how to spot fake news about recently occurring events and it is one of the special difficulties in Fake News Detection (FND) on social networking sites. Recent study has considerably increased our ability to identify fake news, because of less emphasis on utilizing the relationship between the textual and visual information in news samples. It is possible to spot fake news by giving importance to similarity among textual and visual features. In this paper, we study the task of identifying fake news using the Fakeddit dataset, which is a collection of full-length articles and related images. We propose a multimodal approach that makes use of transfer learning to gather semantic and contextual data, develop stronger hidden representations between the words in news samples and the images, and tries to improve the accuracy of FND task. We carefully evaluate the performance of our model on the Fakeddit dataset. The results demonstrate that the proposed model learns more accurate textual features and outperforms the most current textual results on that dataset.Item FakeRevealer: A Multimodal Framework for Revealing the Falsity of Online Tweets Using Transformer-Based Architectures(Scitepress, 2023) Sharma, Yashvardhan; Chauhan, Gajendra SinghAs 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 datasetItem Impact of Transformer-Based Models and User Clustering in Early Fake News Detection in Social Media(Scitepress, 2023) Sharma, Yashvardhan; Chauhan, Gajendra SinghPeople are now consuming news on social media platforms rather than through traditional sources as a result of easy access to the internet. This has allowed for the recent rise in the online dissemination of false information. The spread of false information seriously damages people’s reputations and the public’s trust in them. The research community has recently given fake news identification a great deal of attention, and prior studies have mainly concentrated on finding hints in news content or diffusion graphs. The older models, on the other hand, didn’t have the key features needed to spot fake news quickly. We focus on finding fake news by using features that are available when it is just starting to spread. The current work suggests a new framework made up of content-based features taken from news articles and social-context features taken from user characteristics and responses at the sentence level. In addition, we extend our approach to Transformer-based models and leverage user clustering to demonstrate a considerable performance gain over the original model.