Browsing by Author "Chauhan, Gajendra Singh"

Now showing 1 - 9 of 9

Applying Transfer Learning using BERT-Based Models for Hate Speech Detection
(CEUR-WS, 2021) Sharma, Yashvardhan; Chauhan, Gajendra Singh
Hateful 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.
Comparative Analysis of Various Text Summarization Techniques via Leveraging Transformer Model for the Fake News Detection
(CRC Press, 2022) Sharma, Yashvardhan; Chauhan, Gajendra Singh
Today, 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 genuine
Enhancing College Students' Critical Thinking Through Classroom News Literacy Intervention
(IEEE, 2024) Chauhan, Gajendra Singh
In our interconnected world, people are witnessing a dramatic increase in access to information and communication. Nevertheless, discerning trustworthy sources, validating information, distinguishing between fact and opinion, determining what content to share, and navigating other related challenges have become increasingly complex. Therefore, people should acquire the knowledge, skill, belief, and behavior to consume and create news informedly and ethically. As young individuals transition into adulthood, they begin to take charge of their life decisions. At this juncture, they must acquire news literacy skills. Thus, the au-thors developed an intervention to enhance news literacy among this age group in a College of Science and Technology employing the student's media competence (SuMeC) framework. Following a three-month training period, the participants' literacy proficiency was assessed through assignments using the Structure of the Observed Learning Outcomes (SOLO) taxonomy. The study adds new dimensions to the existing research pool by focusing on how and to what extent college students apply the competencies in everyday life. The findings demonstrate that the intervention effectively integrates news literacy skills among them and the intervention sets an example of how to generate news literacy skills among students in Indian settings.
Ensembling of Various Transformer Based Models for the Fake News Detection Task in the Urdu Language
(CEUR-WS, 2021) Sharma, Yashvardhan; Chauhan, Gajendra Singh
The 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.
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.
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
Impact of Transformer-Based Models and User Clustering in Early Fake News Detection in Social Media
(Scitepress, 2023) Sharma, Yashvardhan; Chauhan, Gajendra Singh
People 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.
Multimodal Fake News Detection on Fakeddit Dataset Using Transformer-Based Architectures
(Springer, 2023-01) Sharma, Yashvardhan; Chauhan, Gajendra Singh
Real-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.
Sanctity of digital privacy and personal data during covid-19: are youths enough digitally literate to deal with it?
(Revistes, 2023) Chauhan, Gajendra Singh
The COVID-19 pandemic has fast-tracked the development of digital applications and inspired everyone to adapt to the technologies to curb the spread of outbreak. As this crisis intensifies, the rapid usage of digital devices and apps has echoed the serious concerns about civil liberties, privacy, and data protection. Considering the situation, this research aimed to explore the internet using habits of the youths of West Bengal, a state in eastern India, during COVID-19. Besides, the paper explored their experiences using various digital applications, and the fundamental digital literacy of them and the safeguards they often take to protect their data from breaches. Thus,the paper presents the results by conducting an online survey among the youths in West Bengal. The result, from 215 participants, highlighted that the increased use of these digital applications has not matched the demand for digital privacy literacy among the young generation of the state. While this pandemic has raised their concerns over digital privacy and data protection, yet they do not undertake any strong protection mechanisms to protect them digitally. Besides, this paper suggests suitable plans to raise awareness among this generation and form a healthy digital citizenship with a proper regulatory framework as it is the need of the hour.