Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic

Narra, Manideep; Umer, Muhammad; Sadiq, Saima; Eshmawi, Ala’ Abdulmajid; Karamti, Hanen; Mohamed, Abdullah; Ashraf, Imran

doi:10.1109/access.2022.3206963

Cited by 18 publications

(8 citation statements)

References 59 publications

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“…We also compare the best-performing machine learning model (ie, Bernoulli NB) in our study with several deep learning models from reference, [62][63][64] and the comparative results are presented in Table 10. It can be observed that, due to the limitation of data availability, the performance of the deep learning models did not surpass that of the Bernoulli NB.…”

Section: Resultsmentioning

confidence: 99%

Monitoring the Relationship between Social Network Status and Influenza Based on Social Media Data

Yan,

Shan,

Zhang

et al. 2023

Disaster med. public health prep.

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Background: This article aims to analyze the relationship between user characteristics on social networks and influenza. Methods: Three specific research questions are investigated: (1) we classify Weibo updates to recognize influenza-related information based on machine learning algorithms and propose a quantitative model for influenza susceptibility in social networks; (2) we adopt in-degree indicator from complex networks theory as social media status to verify its coefficient correlation with influenza susceptibility; (3) we also apply the LDA topic model to explore users’ physical condition from Weibo to further calculate its coefficient correlation with influenza susceptibility. From the perspective of social networking status, we analyze and extract influenza-related information from social media, with many advantages including efficiency, low cost, and real time. Results: We find a moderate negative correlation between the susceptibility of users to influenza and social network status, while there is a significant positive correlation between physical condition and susceptibility to influenza. Conclusions: Our findings reveal the laws behind the phenomenon of online disease transmission, and providing important evidence for analyzing, predicting, and preventing disease transmission. Also, this study provides theoretical and methodological underpinnings for further exploration and measurement of more factors associated with infection control and public health from social networks.

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Section: Resultsmentioning

confidence: 99%

Monitoring the Relationship between Social Network Status and Influenza Based on Social Media Data

Yan,

Shan,

Zhang

et al. 2023

Disaster med. public health prep.

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“…Of course, for keywords like COVID-19, there is no deletion operation at the same time. Also to avoid the effect due to case, it is also necessary to convert all the obtained information to lower case at the beginning [35].…”

Section: Solutionsmentioning

confidence: 99%

Infodemic: Challenges and solutions in topic discovery and data process

Zhang

Yang

Lin

et al. 2023

Preprint

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Background The COVID-19 pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the infodemic, i.e. the importance of the spread of misinformation and rumors related to the epidemic, has been highlighted. However, while there is a growing interest in this phenomenon, studies on the subject discovery, data collection, and data preparation phases of the information analysis process have been lacking. Objective Since the epidemic is unprecedented and has not ended to this day, we aimed to examine the existing infodemic-related literature from the beginning of the epidemic to December 2022. Methods We have systematically searched ScienceDirect and IEEE Xplore databases with some search limitations. From the searched literature we selected titles, abstracts and keywords, and limitations sections. We conducted an extensive structured literature search and analysis by filtering the literature and sorting out the available information. Results A total of 47 papers ended up meeting the requirements of this review. Researchers in all of these literatures encountered different challenges, most of which were focused on the data collection step, with few challenges encountered in the data preparation phase and almost none in the topic discovery section. The challenges were mainly divided into the points of how to collect data quickly, how to get the required data samples, how to filter the data, what to do if the data set is too small, and how to pick the right classifier. In addition, researchers have proposed partial solutions to the challenges, and we have also proposed possible solutions. Conclusions This review found that information epidemics is a rapidly growing research area that attracts the interest of researchers from different disciplines. The number of studies in this field has increased significantly in recent years, with researchers from different countries, including the United States, India, and China. Infodemic topic discovery, data collection, and data preparation are not easy, and each step faces different challenges. While there is some research in this emerging field, there are still many challenges that need to be addressed. These findings highlight the need for more articles to address these issues and fill these gaps.

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“…( 5) Find the location of particular fish chosen by the osprey bird Update the modified location of the osprey bird by Eq. (6) Step 2 Find the upgraded location of the osprey bird Verify the condition using Eq. ( 8) Update the location of the osprey by Eq.…”

Section: Implemented Eooamentioning

confidence: 99%

“…It is difficult to discover false information from a social network [6]. False information has a detrimental effect on millions of people and their real environment due to its rapid propagation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Weighted Feature Fusion for Multiscale Atrous Convolution based 1DCNN with Dilated LSTM-aided Fake News Detection using Regional

Vasu,

Kalaivani

2023

Preprint

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The people present in the world rely on social media for gathering news, and it is mainly because of the development of technologies. The approaches employed in natural language processing are still deficient in judgment factors, and these techniques frequently rely upon political or social circumstances. Numerous low-level communities in the area are curious after experiencing the negative effects caused by the spread of false information in different sectors. Low-resource languages are still distracted, considering the fact that these techniques are extensively employed in the English language. This work aims to provide an analysis of regional language fake news and develop a referral system with advanced techniques to identify fake news in Hindi and Tamil. This proposed model includes (a) Regional Language Text Collection, (b) Text pre-processing, (c) Feature Extraction, (d) Weighted Stacked Feature Fusion, and (e) Fake News Detection. The text data is collected from the standard datasets. The collected text data is pre-processed and given into the feature extraction using Bidirectional Encoder Representations from Transformers (BERT), Transformer networks, and seq2seq network for extracting the three sets of language text features. These extracted feature sets are inserted into the weighted stacked feature fusion model, where the three sets of extracted features are integrated with the optimized weights that are acquired through the Enhanced Osprey Optimization Algorithm (EOOA). Here, weighted fused features are accomplished and passed toward the fake news detection phase. Fake news detection is performed with Multi-scale Atrous Convolution-based One-Dimensional Convolutional Neural Network with Dilated Long Short Term Memory (MACNN-DLSTM). The fake news in the regional language is detected finally. The experimental analysis is carried out by comparing with the conventional algorithms and detection techniques to showcase the efficiency of the developed regional language-based fake news detection model.

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Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic

Cited by 18 publications

References 59 publications

Monitoring the Relationship between Social Network Status and Influenza Based on Social Media Data

Monitoring the Relationship between Social Network Status and Influenza Based on Social Media Data

Infodemic: Challenges and solutions in topic discovery and data process

Adaptive Weighted Feature Fusion for Multiscale Atrous Convolution based 1DCNN with Dilated LSTM-aided Fake News Detection using Regional

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