Search

Spotting fake news is a critical problem nowadays. Social media are responsible for propagating fake news. Fake news propagated over digital platforms generates confusion as well as induce biased perspectives in people. Detection of misinformation over the digital platform is essential to mitigate its adverse impact. Many approaches have been implemented in recent years. Despite the productive work, fake news identification poses many challenges due to the lack of a comprehensive publicly available benchmark dataset. There is no large-scale dataset that consists of Indian news only. So, this paper presents IFND (Indian fake news dataset) dataset. The dataset consists of both text and images. The majority of the content in the dataset is about events from the year 2013 to the year 2021. Dataset content is scrapped using the Parsehub tool. To increase the size of the fake news in the dataset, an intelligent augmentation algorithm is used. An intelligent augmentation algorithm generates meaningful fake news statements. The latent Dirichlet allocation (LDA) technique is employed for topic modelling to assign the categories to news statements. Various machine learning and deep-learning classifiers are implemented on text and image modality to observe the proposed IFND dataset's performance. A multi-modal approach is also proposed, which considers both textual and visual features for fake news detection. The proposed IFND dataset achieved satisfactory results. This study affirms that the accessibility of such a huge dataset can actuate research in this laborious exploration issue and lead to better prediction models., Competing Interests: Conflict of interestThe authors do not have any conflict of interest., (© The Author(s) 2021.)

In recent years, rumours and fake news are spreading widely and very rapidly all over the world. Such circumstances lead to the propagation and production of an inaccurate news article. Also, misinformation and fake news are increased by the user without proper verification. Hence, it is necessary to restrict the spreading of fake information on mass media and to promote confidence all over the world. For this purpose, this paper recognizes the detection of fake news in an effective manner. The proposed methodology in detecting fake news consists of four different phases namely the data pre-processing phase, feature reduction phase, feature extraction phase as well as the classification phase. During data pre-processing, the input data are pre-processed by employing tokenization, stop-words deletion as well as stemming. In the second phase, the features are reduced by employing PPCA to enhance accuracy. Then the extracted feature is provided to the classification phase where LSTM-LF algorithm is utilized to classify the news as fake or real optimally. Furthermore, this paper utilizes four different datasets namely the Buzzfeed dataset, GossipCop dataset, ISOT dataset as well as Politifact dataset for evaluation. The performance evaluation and the comparative analysis are conducted and the analysis reveals that the proposed approach provides better performances when compared to other fake detection-based approaches., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.)

Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks., Competing Interests: Conflict of interestThe authors declare that there is no actual or potential conflict of interest in relation to this paper. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.)

Objective: To study the role of autologous bone marrow aspirate therapy (ABMAT) in wound healing. Approach: This is a retrospective analysis of 9 patients (11 chronic nonhealing wounds) in whom ABMAT was used. Patients (wounds) were grouped into two groups. Group 1 included 4 patients (5 wounds) refusing/unfit for reconstruction and managed only with ABMAT. Group 2 included 5 patients (6 wounds) who agreed/fit for reconstruction after wound bed preparation with ABMAT. End point of the study was complete wound healing. Results: ABMAT helped in complete healing of chronic nonhealing wounds by secondary intention in group 1 patients and enhanced process of wound bed preparation for reconstruction in group 2 patients. Innovation: This study highlights the importance of ABMAT in the management of chronic nonhealing wounds. Conclusion: ABMAT helps in wound bed preparation to allow the wound to heal completely or cover by skin graft/flap.