ARCNN framework for multimodal infodemic detection

Raj, Chahat; Meel, Priyanka

doi:10.1016/j.neunet.2021.11.006

Cited by 21 publications

(7 citation statements)

References 34 publications

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“…Other studies have also explored the use of the ReCOVery data set for false information classification. One such study is by Raj and Meel [ 66 ], where a novel deep learning model, the Allied Recurrent and Convolutional Neural Network (ARCNN), was created using both image and textual features within news articles to detect misinformation. The performance of the ARCNN was tested using 6 COVID-19 fake news data sets, with ReCOVery as 1 of the data sets, achieving an accuracy, precision, recall, and F 1 score of 80.98%, 53.85%, 58.33%, and 56.00%, respectively [ 66 ].…”

Section: Discussionmentioning

confidence: 99%

“…One such study is by Raj and Meel [ 66 ], where a novel deep learning model, the Allied Recurrent and Convolutional Neural Network (ARCNN), was created using both image and textual features within news articles to detect misinformation. The performance of the ARCNN was tested using 6 COVID-19 fake news data sets, with ReCOVery as 1 of the data sets, achieving an accuracy, precision, recall, and F 1 score of 80.98%, 53.85%, 58.33%, and 56.00%, respectively [ 66 ]. Another study using the ReCOVery data set for model development explored the use of multiple languages for fake news detection to improve model performance [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

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Data Exploration and Classification of News Article Reliability: Deep Learning Study

Zhan¹,

Li²,

Osmani³

et al. 2022

JMIR Infodemiology

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Background During the ongoing COVID-19 pandemic, we are being exposed to large amounts of information each day. This “infodemic” is defined by the World Health Organization as the mass spread of misleading or false information during a pandemic. This spread of misinformation during the infodemic ultimately leads to misunderstandings of public health orders or direct opposition against public policies. Although there have been efforts to combat misinformation spread, current manual fact-checking methods are insufficient to combat the infodemic. Objective We propose the use of natural language processing (NLP) and machine learning (ML) techniques to build a model that can be used to identify unreliable news articles online. Methods First, we preprocessed the ReCOVery data set to obtain 2029 English news articles tagged with COVID-19 keywords from January to May 2020, which are labeled as reliable or unreliable. Data exploration was conducted to determine major differences between reliable and unreliable articles. We built an ensemble deep learning model using the body text, as well as features, such as sentiment, Empath-derived lexical categories, and readability, to classify the reliability. Results We found that reliable news articles have a higher proportion of neutral sentiment, while unreliable articles have a higher proportion of negative sentiment. Additionally, our analysis demonstrated that reliable articles are easier to read than unreliable articles, in addition to having different lexical categories and keywords. Our new model was evaluated to achieve the following performance metrics: 0.906 area under the curve (AUC), 0.835 specificity, and 0.945 sensitivity. These values are above the baseline performance of the original ReCOVery model. Conclusions This paper identified novel differences between reliable and unreliable news articles; moreover, the model was trained using state-of-the-art deep learning techniques. We aim to be able to use our findings to help researchers and the public audience more easily identify false information and unreliable media in their everyday lives.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Data Exploration and Classification of News Article Reliability: Deep Learning Study

Zhan¹,

Li²,

Osmani³

et al. 2022

JMIR Infodemiology

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“…behavior prediction [3], recommendation systems [6,7], influence maximization [8], public opinion guiding [9], communities [10], and graph anomaly detection [11]. We cannot deny that social influence has become ubiquitous and complex in shaping our social decisions.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learning

Liu¹,

Cao²,

Pi³

2022

Preprint

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Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise, they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to demonstrate the efficiency and effectiveness of the proposed algorithm. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.

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“…The term ’social influence’ is usually understood as the process whereby a user’s emotions, opinions, or behaviors are shaped by their environment, i.e., the process by which people alter their behavior under the influence of others [ 3 ]. With the globalization of online social networks, social influence analysis has spread to many domains, including marketing [ 4 ], behavior prediction [ 5 ], recommendation systems [ 6 , 7 ], influence maximization [ 8 ], public opinion guiding [ 9 ], communities [ 10 ], and graph anomaly detection [ 11 ]. We cannot deny that social influence has become ubiquitous and complex in shaping our social decisions.…”

Section: Introductionmentioning

confidence: 99%

Modeling the social influence of COVID-19 via personalized propagation with deep learning

Liu

Cao

et al. 2022

World Wide Web

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Social influence prediction has permeated many domains, including marketing, behavior prediction, recommendation systems, and more. However, traditional methods of predicting social influence not only require domain expertise, they also rely on extracting user features, which can be very tedious. Additionally, graph convolutional networks (GCNs), which deals with graph data in non-Euclidean space, are not directly applicable to Euclidean space. To overcome these problems, we extended DeepInf such that it can predict the social influence of COVID-19 via the transition probability of the page rank domain. Furthermore, our implementation gives rise to a deep learning-based personalized propagation algorithm, called DeepPP. The resulting algorithm combines the personalized propagation of a neural prediction model with the approximate personalized propagation of a neural prediction model from page rank analysis. Four social networks from different domains as well as two COVID-19 datasets were used to analyze the proposed algorithm’s efficiency and effectiveness. Compared to other baseline methods, DeepPP provides more accurate social influence predictions. Further, experiments demonstrate that DeepPP can be applied to real-world prediction data for COVID-19.

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ARCNN framework for multimodal infodemic detection

Cited by 21 publications

References 34 publications

Data Exploration and Classification of News Article Reliability: Deep Learning Study

Data Exploration and Classification of News Article Reliability: Deep Learning Study

Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learning

Modeling the social influence of COVID-19 via personalized propagation with deep learning

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