Department of Computer Science and Information Systems

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Now showing 1 - 10 of 12
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    Intelligent Task Assignment in a Crowdsourcing Platform
    (Springer, 2018-11) Anand, Vijayalakshmi
    Crowdsourcing is a process of judiciously selecting the right user (worker) from a large pool of online community who could solve the task. The undertaking of jobs by many online users (workers) simultaneously helps solving large-scale computational problems. After completion of the job, monetary reward would be offered to the user who has completed the job satisfactorily or efficiently. The main challenge in crowdsourcing platforms is to assign a task to a user as users are mainly available online and are unknown and unfamiliar to each other. To overcome this challenge, we have proposed a new algorithm for task assignment based on the trustworthiness of online users. Trustworthiness is calculated by using the belief and knowledge values (metrics) of individuals who have shown interest in taking up the task. We have used vector space model to find out that the person is knowledgeable to do the task at hand. The belief value for a user is calculated by using the reputation and the familiarity index of the user on the social media interactions. Tasks would be assigned to all the users whose trust value is above a specific threshold. We have evaluated the proposed algorithm on a social media application that we created for sharing expertise amongst the off-campus faculty colleagues of our university.
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    Reputation-Based Reinforcement Algorithm for Motivation in Crowdsourcing Platform
    (Springer, 2019-07) Anand, Vijayalakshmi
    Crowdsourcing is a well-known model for solving tasks in several organizations in the recent times. While building, the crowdsourcing platform is simple, and its success depends on the amount of individuals taking part in it. We propose a brand new gamification methodology to draw folks to participate within the crowdsourcing platform. Reinforcement algorithm is employed in this gamification method to motivate the people. This reinforcement algorithm can direct a user to participate in some actions that yield maximum reward in a crowdsourcing platform. This gamification technique motivates user to participate in various activities in the crowdsourcing platform. The proposed algorithm is applied on a social media application that has been implemented for faculties to share their research and tutorial experience. We proved that participation of faculties in crowdsourcing platform improved after applying this gamification method.
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    Motivation of participants on the crowdsourcing platform using intelligent agents
    (Research Institute for Intelligent Computer Systems, 2020-03) Anand, Vijayalakshmi
    Crowdsourcing is a model where individuals or organizations receive services from a large group of Internet users including ideas, finances, completing a complex task, etc. Several crowdsourcing websites have failed due to la ck of user participation; hence, the success of crowdsourcing platforms is manifested by the mass of user participation. However, an issue of motivating users to participate in crowdsourcing platform stays challenging. We have proposed a new approach, i.e. , reinforcement learning - based gamification method to motivate users. Gamification has been a practical approach to engaging users in many fields, but still, it needs an improvement in the Crowdsourcing platform. In this paper, the gamification approach is strengthened by a reinforcement learning algorithm. We have created an intelligent agent using the Reinforcement learning algorithm (Q - learning). This agent suggests an optimal action plan that yields maximum reward points to the users for their active pa rticipation in the Crowdsourcing application. Also, its performance is compared with the SARSA algorithm (On - policy learning), which is another Reinforcement learning algorithm.
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    A Hybrid Model for Effective Fake News Detection with a Novel COVID-19 Dataset
    (CITEPRESS, 2021) Narang, Pratik
    Due to the increasing number of users in social media, news articles can be quickly published or share among users without knowing its credibility and authenticity. Fast spreading of fake news articles using different social media platforms can create inestimable harm to society. These actions could seriously jeopardize the reliability of news media platforms. So it is imperative to prevent such fraudulent activities to foster the credibility of such social media platforms. An efficient automated tool is a primary necessity to detect such misleading articles. Considering the issues mentioned earlier, in this paper, we propose a hybrid model using multiple branches of the convolutional neural network (CNN) with Long Short Term Memory (LSTM) layers with different kernel sizes and filters. To make our model deep, which consists of three dense layers to extract more powerful features automatically. In this research, we have created a dataset (FN-COV) collecting 69976 fake and real news articles during the pandemic of COVID-19 with tags like social-distancing, covid19, and quarantine. We have validated the performance of our proposed model with one more real-time fake news dataset: PHEME. The capability of combined kernels and layers of our C-LSTM network is lucrative towards both the datasets. With our proposed model, we achieved an accuracy of 91.88% with PHEME, which is higher as compared to existing models and 98.62% with FN-COV dataset.
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    MCNNet: Generalizing Fake News Detection with a Multichannel Convolutional Neural Network using a Novel COVID-19 Dataset
    (ACM Digital Library, 2021-01) Narang, Pratik
    During the pandemic of COVID-19, the propagation of fake news is spreading like wildfire on social media. Such fake news articles have created confusion among people and serious social disruptions as well. To detect such news articles effectively, we propose a generalized classification model (MCNNet) having the power of learning across different kernel-sized convolutional layers in different parallel channel network. The capability of MCNNet is lucrative towards any real-world fake news dataset. Experimental results have demonstrated the performance of our model with different real-world fake news datasets.
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    FNDNet – A deep convolutional neural network for fake news detection
    (Elsevier, 2020-06) Narang, Pratik
    With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.
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    DeepFakE: improving fake news detection using tensor decomposition-based deep neural network
    (Springer, 2020-05) Narang, Pratik
    Social media platforms have simplified the sharing of information, which includes news as well, as compared to traditional ways. The ease of access and sharing the data with the revolution in mobile technology has led to the proliferation of fake news. Fake news has the potential to manipulate public opinions and hence, may harm society. Thus, it is necessary to examine the credibility and authenticity of the news articles being shared on social media. Nowadays, the problem of fake news has gained massive attention from research communities and needed an optimal solution with high efficiency and low efficacy. Existing detection methods are based on either news-content or social-context using user-based features as an individual. In this paper, the content of the news article and the existence of echo chambers (community of social media-based users sharing the same opinions) in the social network are taken into account for fake news detection. A tensor representing social context (correlation between user profiles on social media and news articles) is formed by combining the news, user and community information. The news content is fused with the tensor, and coupled matrix-tensor factorization is employed to get a representation of both news content and social context. The proposed method has been tested on a real-world dataset: BuzzFeed. The factors obtained after decomposition have been used as features for news classification. An ensemble machine learning classifier (XGBoost) and a deep neural network model (DeepFakE) are employed for the task of classification. Our proposed model (DeepFakE) outperforms with the existing fake news detection methods by applying deep learning on combined news content and social context-based features as an echo-chamber.
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    FakeBERT: Fake news detection in social media with a BERT-based deep learning approach
    (Springer, 2021-01) Narang, Pratik
    In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of fake news at an incredible pace and scale. In recent researches, many useful methods for fake news detection employ sequential neural networks to encode news content and social context-level information where the text sequence was analyzed in a unidirectional way. Therefore, a bidirectional training approach is a priority for modelling the relevant information of fake news that is capable of improving the classification performance with the ability to capture semantic and long-distance dependencies in sentences. In this paper, we propose a BERT-based (Bidirectional Encoder Representations from Transformers) deep learning approach (FakeBERT) by combining different parallel blocks of the single-layer deep Convolutional Neural Network (CNN) having different kernel sizes and filters with the BERT. Such a combination is useful to handle ambiguity, which is the greatest challenge to natural language understanding. Classification results demonstrate that our proposed model (FakeBERT) outperforms the existing models with an accuracy of 98.90%.
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    EchoFakeD: improving fake news detection in social media with an efficient deep neural network
    (Springer, 2021-01) Narang, Pratik
    The increasing popularity of social media platforms has simplified the sharing of news articles that have led to the explosion in fake news. With the emergence of fake news at a very rapid rate, a serious concern has produced in our society because of enormous fake content dissemination. The quality of the news content is questionable and there exists a necessity for an automated tool for the detection. Existing studies primarily focus on utilizing information extracted from the news content. We suggest that user-based engagements and the context related group of people (echo-chamber) sharing the same opinions can play a vital role in the fake news detection. Hence, in this paper, we have focused on both the content of the news article and the existence of echo chambers in the social network for fake news detection. Standard factorization methods for fake news detection have limited effectiveness due to their unsupervised nature and primarily employed with traditional machine learning models. To design an effective deep learning model with tensor factorization approach is the priority. In our approach, the news content is fused with the tensor following a coupled matrix–tensor factorization method to get a latent representation of both news content as well as social context. We have designed our model with a different number of filters across each dense layer along with dropout. To classify on news content and social context-based information individually as well as in combination, a deep neural network (our proposed model) was employed with optimal hyper-parameters. The performance of our proposed approach has been validated on a real-world fake news dataset: BuzzFeed and PolitiFact. Classification results have demonstrated that our proposed model (EchoFakeD) outperforms existing and appropriate baselines for fake news detection and achieved a validation accuracy of 92.30%. These results have shown significant improvements over the existing state-of-the-art models in the area of fake news detection and affirm the potential use of the technique for classifying fake news.
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    AENeT: an attention-enabled neural architecture for fake news detection using contextual features
    (Springer, 2021) Narang, Pratik; Sharma, Yashvardhan
    In the current era of social media, the popularity of smartphones and social media platforms has increased exponentially. Through these electronic media, fake news has been rising rapidly with the advent of new sources of information, which are highly unreliable. Checking off a particular news article is genuine or fake is not easy for any end user. Search engines like Google are also not capable of telling about the fakeness of any news article due to its restriction with limited query keywords. In this paper, our end goal is to design an efficient deep learning model to detect the degree of fakeness in a news statement. We propose a simple network architecture that combines the use of contextual embedding as word embedding and uses attention mechanisms with relevant metadata available. The efficacy and efficiency of our models are demonstrated on several real-world datasets. Our model achieved 46.36% accuracy on the LIAR dataset, which outperforms the current state of the art by 1.49%.