An Ensemble Deep Learning Technique to Detect COVID-19 Misleading Information

Elhadad, Mohamed K.; Li, Kin Fun; Gebali, Fayez

doi:10.1007/978-3-030-57811-4_16

Cited by 12 publications

(30 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each word is represented in a meaningful vector space where the cosine distance between two words depicts their similarity. In the studies [14] [15], the authors applied an embedding layer using 300-dimensional pre-trained glove vectors. This layer could convert the tweet texts into a meaningful vector space.…”

Section: B Feature Extractionmentioning

confidence: 99%

A Novel Stance based Sampling for Imbalanced Data

Agarwal¹,

Rana²,

Jariwala³

et al. 2022

IJACSA

View full text Add to dashboard Cite

While the world is suffering from coronavirus pandemic (COVID-19), a parallel battle with Infodemic, the proliferation of fake news online is also taking place. The spread of fake news during this global pandemic COVID-19 has dangerous consequences. This is the driving force behind this study. Relying on incorrect information obtained from the internet or social media can be fatal. According to a World Health Organization survey, at least 800 people have lost their lives because of COVID-19 misinformation during this time, highlighting the accurate automated classification of fake news. However, the data at disposal for classification is imbalanced. The Internet has a vast repository of authentic healthcare news, whereas Fake News on COVID-19 healthcare is not abundant. This imbalance leads to incorrect classification. The paper studies alternative approaches to text sampling. In this paper, we propose a stance based sampling method for balancing news data. The disparity between the title and content of news items is utilized to sample data points selectively and rectify the imbalance. The key findings are that the proposed stance-based sampling strategies enhance categorisation task performance consistently for varying degrees of imbalance. The proposed techniques can better detect misleading news in the health care sector.

show abstract

Section: B Feature Extractionmentioning

confidence: 99%

A Novel Stance based Sampling for Imbalanced Data

Agarwal¹,

Rana²,

Jariwala³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Misinformation will typically have a high perplexity because it uses vocabulary and phrases that differ significantly from the reliable text used to train the model. Other work trained a deep learning model to directly differentiate between reliable and unreliable assertions about COVID-19 (125). Another system identified YouTube videos containing conspiracy theories about the origin of SARS-CoV-2 (such as being caused by 5G cellular networks) by analyzing the transcript of the video using a supervised ML approach (126).…”

Section: Applicationsmentioning

confidence: 99%

Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing

Chen

Leaman

Allot

et al. 2021

Annu. Rev. Biomed. Data Sci.

View full text Add to dashboard Cite

The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language processing (NLP)—the branch of artificial intelligence that interprets human language—can be applied to address many of the information needs made urgent by the COVID-19 pandemic. This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic. We detail work on four core NLP tasks: information retrieval, named entity recognition, literature-based discovery, and question answering. We also describe work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection. We conclude by discussing observable trends and remaining challenges. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

show abstract

“…Previous studies have focused on specific health issues and sometimes on specific types of rumors [ 8 , 18 , 19 , 21 , 22 ]. This suggests the need for a more general framework that can detect the accuracy of health information across known and previously unknown health conditions, such as during the outbreak of a previously unknown infectious disease.…”

Section: Introductionmentioning

confidence: 99%

“…The second group comprised studies that relied on an external website, such as a fact-checking website, to label the tweets. One such example is the study by Elhaddad et al [ 19 ], which relied on a fact-checking website to identify misleading information. A similar method was used by Ghenai et al [ 21 ], who relied on the website of the World Health Organization (WHO) to identify 6 rumors.…”

Section: Introductionmentioning

confidence: 99%

Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study

Albalawi¹,

Nikolov²,

Buckley³

2022

JMIR Form Res

View full text Add to dashboard Cite

Background In recent years, social media has become a major channel for health-related information in Saudi Arabia. Prior health informatics studies have suggested that a large proportion of health-related posts on social media are inaccurate. Given the subject matter and the scale of dissemination of such information, it is important to be able to automatically discriminate between accurate and inaccurate health-related posts in Arabic. Objective The first aim of this study is to generate a data set of generic health-related tweets in Arabic, labeled as either accurate or inaccurate health information. The second aim is to leverage this data set to train a state-of-the-art deep learning model for detecting the accuracy of health-related tweets in Arabic. In particular, this study aims to train and compare the performance of multiple deep learning models that use pretrained word embeddings and transformer language models. Methods We used 900 health-related tweets from a previously published data set extracted between July 15, 2019, and August 31, 2019. Furthermore, we applied a pretrained model to extract an additional 900 health-related tweets from a second data set collected specifically for this study between March 1, 2019, and April 15, 2019. The 1800 tweets were labeled by 2 physicians as accurate, inaccurate, or unsure. The physicians agreed on 43.3% (779/1800) of tweets, which were thus labeled as accurate or inaccurate. A total of 9 variations of the pretrained transformer language models were then trained and validated on 79.9% (623/779 tweets) of the data set and tested on 20% (156/779 tweets) of the data set. For comparison, we also trained a bidirectional long short-term memory model with 7 different pretrained word embeddings as the input layer on the same data set. The models were compared in terms of their accuracy, precision, recall, F1 score, and macroaverage of the F1 score. Results We constructed a data set of labeled tweets, 38% (296/779) of which were labeled as inaccurate health information, and 62% (483/779) of which were labeled as accurate health information. We suggest that this was highly efficacious as we did not include any tweets in which the physician annotators were unsure or in disagreement. Among the investigated deep learning models, the Transformer-based Model for Arabic Language Understanding version 0.2 (AraBERTv0.2)-large model was the most accurate, with an F1 score of 87%, followed by AraBERT version 2–large and AraBERTv0.2-base. Conclusions Our results indicate that the pretrained language model AraBERTv0.2 is the best model for classifying tweets as carrying either inaccurate or accurate health information. Future studies should consider applying ensemble learning to combine the best models as it may produce better results.

show abstract

An Ensemble Deep Learning Technique to Detect COVID-19 Misleading Information

Cited by 12 publications

References 12 publications

A Novel Stance based Sampling for Imbalanced Data

A Novel Stance based Sampling for Imbalanced Data

Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing

Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study

Contact Info

Product

Resources

About