Rajiv Ratn Shah

Bio: Rajiv Ratn Shah is an academic researcher from Indraprastha Institute of Information Technology. The author has contributed to research in topics: Computer science & Social media. The author has an hindex of 25, co-authored 172 publications receiving 1801 citations. Previous affiliations of Rajiv Ratn Shah include Delhi Technological University & Indian Institute of Technology Delhi.

SpotFake: A Multi-modal Framework for Fake News Detection

Abstract: A rapid growth in the amount of fake news on social media is a very serious concern in our society. It is usually created by manipulating images, text, audio, and videos. This indicates that there is a need of multimodal system for fake news detection. Though, there are multimodal fake news detection systems but they tend to solve the problem of fake news by considering an additional sub-task like event discriminator and finding correlations across the modalities. The results of fake news detection are heavily dependent on the subtask and in absence of subtask training, the performance of fake news detection degrade by 10% on an average. To solve this issue, we introduce SpotFake-a multi-modal framework for fake news detection. Our proposed solution detects fake news without taking into account any other subtasks. It exploits both the textual and visual features of an article. Specifically, we made use of language models (like BERT) to learn text features, and image features are learned from VGG-19 pre-trained on ImageNet dataset. All the experiments are performed on two publicly available datasets, i.e., Twitter and Weibo. The proposed model performs better than the current state-of-the-art on Twitter and Weibo datasets by 3.27% and 6.83%, respectively.

Bagged support vector machines for emotion recognition from speech

Abstract: Speech emotion recognition, a highly promising and exciting problem in the field of Human Computer Interaction, has been studied and analyzed over several decades. It concerns the task of recognizing a speaker’s emotions from their speech recordings. Recognizing emotions from speech can go a long way in determining a person’s physical and psychological state of well-being. In this work we performed emotion classification on three corpora — the Berlin EmoDB, the Indian Institute of Technology Kharagpur Simulated Emotion Hindi Speech Corpus (IITKGP-SEHSC), and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). A combination of spectral features was extracted from them which was further processed and reduced to the required feature set. Ensemble learning has been proven to give superior performance compared to single estimators. We propose a bagged ensemble comprising of support vector machines with a Gaussian kernel as a viable algorithm for the problem at hand. We report the results obtained on the three datasets mentioned above.

Detecting Offensive Tweets in Hindi-English Code-Switched Language.

Abstract: The exponential rise of social media websites like Twitter, Facebook and Reddit in linguistically diverse geographical regions has led to hybridization of popular native languages with English in an effort to ease communication. The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script. The paper introduces a novel tweet dataset, titled Hindi-English Offensive Tweet (HEOT) dataset, consisting of tweets in Hindi-English code switched language split into three classes: non-offensive, abusive and hate-speech. Further, we approach the problem of classification of the tweets in HEOT dataset using transfer learning wherein the proposed model employing Convolutional Neural Networks is pre-trained on tweets in English followed by retraining on Hinglish tweets.

Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings

Abstract: Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.

ADVISOR: Personalized Video Soundtrack Recommendation by Late Fusion with Heuristic Rankings

Abstract: Capturing videos anytime and anywhere, and then instantly sharing them online, has become a very popular activity. However, many outdoor user-generated videos (UGVs) lack a certain appeal because their soundtracks consist mostly of ambient background noise. Aimed at making UGVs more attractive, we introduce ADVISOR, a personalized video soundtrack recommendation system. We propose a fast and effective heuristic ranking approach based on heterogeneous late fusion by jointly considering three aspects: venue categories, visual scene, and user listening history. Specifically, we combine confidence scores, produced by SVMhmm models constructed from geographic, visual, and audio features, to obtain different types of video characteristics. Our contributions are threefold. First, we predict scene moods from a real-world video dataset that was collected from users' daily outdoor activities. Second, we perform heuristic rankings to fuse the predicted confidence scores of multiple models, and third we customize the video soundtrack recommendation functionality to make it compatible with mobile devices. A series of extensive experiments confirm that our approach performs well and recommends appealing soundtracks for UGVs to enhance the viewing experience.

Social Media and Fake News in the 2016 Election

Abstract: Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

The assessment of aphasia and related disorders

Abstract: tical perspective. For instance, there are only three passing references to kuru in a book of 650 pages. This edition reflects the renewed interest in the immunological theories of multiple sclerosis. More than half the text is devoted to Professor Lumsden's analysis of the pathoIogy and, in particular, the chemical pathology of the immune response. There is a great deal of original work devoted to the chemistry and behaviour of the immunoglobulins. Much of this appears in specialist journals and one must be grateful for the critical summary provided here. Professor Lumsden unequivocally sees the key to the problem of multiple sclerosis in the study of its immunochemistry, relegating infection by a virus or a slow virus to a quite subsidiary role. The clinical studies drawing on wide practical experience help to get one's prejudices about the illness onto a more reasoned footing. The section on treatment is still sadly limited. Dr. McAlpine found little to add to the regime which he described in 1955. McAlpine, Lumsden, and Acheson's reappraisal is an essential reference for the practising neurologist and the new edition makes important modification of and changes in emphasis from the edition of 1965.