Rumor Detection of COVID-19 Pandemic on Online Social Networks

Shi, Anqi; Qu, Zheng; Jia, Qingyao; Lyu, Chen

doi:10.1109/sec50012.2020.00055

Cited by 14 publications

(10 citation statements)

References 10 publications

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“…In Shi et al (2020), the authors introduced a model using the XGBoost ensemble learning algorithm, where 16 basic features of four types such as text characteristic, user-related, interaction-based, and emotion-based features are used in their collected rumor data from microblog. They showed that the accuracy of the model is not satisfactory when these features are used individually.…”

Section: Traditional ML Methodsmentioning

confidence: 99%

“…The study Koirala (2020) released a dataset by scraping the data from various news and blog sites using Webhose.io API. In the studies Yang et al (2021); Shi et al (2020), the authors used a dataset containing microblogs related to COVID-19 ) posts and news articles posted on blog sites and traditional news agencies were considered to collect both fake and real news. The variation of data collection from various social platforms is shown in Fig.…”

Section: Data Sourcesmentioning

confidence: 99%

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Applications of machine learning for COVID-19 misinformation: a systematic review

Sanaullah

Das

et al. 2022

Soc. Netw. Anal. Min.

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The inflammable growth of misinformation on social media and other platforms during pandemic situations like COVID-19 can cause significant damage to the physical and mental stability of the people. To detect such misinformation, researchers have been applying various machine learning (ML) and deep learning (DL) techniques. The objective of this study is to systematically review, assess, and synthesize state-of-the-art research articles that have used different ML and DL techniques to detect COVID-19 misinformation. A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey was solely centered on reproducible and high-quality research. We reviewed 43 papers that fulfilled our inclusion criteria out of 260 articles found from our keyword search. We have surveyed a complete pipeline of COVID-19 misinformation detection. In particular, we have identified various COVID-19 misinformation datasets and reviewed different data processing, feature extraction, and classification techniques to detect COVID-19 misinformation. In the end, the challenges and limitations in detecting COVID-19 misinformation using ML techniques and the future research directions are discussed.

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Section: Traditional ML Methodsmentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

Applications of machine learning for COVID-19 misinformation: a systematic review

Sanaullah

Das

et al. 2022

Soc. Netw. Anal. Min.

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“…Machine learning and deep learning techniques have been employed to study COVID-19 posts on social media, with much of the focus on topic modeling, sentiment analysis, and misinformation detection [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Both sentiment analysis and misinformation detection are supervised classification problems.…”

Section: Introductionmentioning

confidence: 99%

“…Both sentiment analysis and misinformation detection are supervised classification problems. Many studies have employed the Valence Aware Dictionary and Sentiment Reasoner (VADER) model or long short-term memory (LSTM) for sentiment analysis and ensemble machine learning models, such as Extreme Gradient Boosting (XGBoost), for misinformation detection [13][14][15][17][18][19]23,25]. Topic modeling, on the other hand, is an unsupervised clustering method.…”

Section: Introductionmentioning

confidence: 99%

The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study

W¹,

Lan²,

Wang³

et al. 2021

JMIR Med Inform

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Background In 2020, the COVID-19 pandemic put the world in a crisis regarding both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it as an infodemic in February 2020. Objective The aim of this study was to examine the propagation patterns and textual transformation of COVID-19–related rumors on a closed social media platform. Methods We obtained a data set of suspicious text messages collected on Taiwan’s most popular instant messaging platform, LINE, between January and July 2020. We proposed a classification-based clustering algorithm that could efficiently cluster messages into groups, with each group representing a rumor. For ease of understanding, a group is referred to as a “rumor group.” Messages in a rumor group could be identical or could have limited textual differences between them. Therefore, each message in a rumor group is a form of the rumor. Results A total of 936 rumor groups with at least 10 messages each were discovered among 114,124 text messages collected from LINE. Among 936 rumors, 396 (42.3%) were related to COVID-19. Of the 396 COVID-19–related rumors, 134 (33.8%) had been fact-checked by the International Fact-Checking Network–certified agencies in Taiwan and determined to be false or misleading. By studying the prevalence of simplified Chinese characters or phrases in the messages that originated in China, we found that COVID-19–related messages, compared to non–COVID-19–related messages, were more likely to have been written by non-Taiwanese users. The association was statistically significant, with P<.001, as determined by the chi-square independence test. The qualitative investigations of the three most popular COVID-19 rumors revealed that key authoritative figures, mostly medical personnel, were often misquoted in the messages. In addition, these rumors resurfaced multiple times after being fact-checked, usually preceded by major societal events or textual transformations. Conclusions To fight the infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media has given rise to an unprecedented number of unverified rumors, it also provides a unique opportunity for us to study the propagation of rumors and their interactions with society. Therefore, we must put more effort into these areas.

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“…In light of the infodemic, several investigations have been carried out to look at the COVID-19 misinformation issue in various aspects. Topics included but not limited to, the types and contents of COVID-19 misinformation [27] [5], the spread and prevalence of rumors on social media platforms [7], [13], [10], [27], [19], [20], the consequences of misinformation [6], and the application of machine learning algorithms on rumor analyses [21] [11]. However, the majority of the studies focused on data collected from public social media platforms such as Twitter, Facebook, or Weibo.…”

mentioning

confidence: 99%

The Evolution of Rumors on a Closed Platform during COVID-19

Wang¹,

Lan²,

Yu³

et al. 2021

Preprint

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Rumor Detection of COVID-19 Pandemic on Online Social Networks

Cited by 14 publications

References 10 publications

Applications of machine learning for COVID-19 misinformation: a systematic review

Applications of machine learning for COVID-19 misinformation: a systematic review

The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study

The Evolution of Rumors on a Closed Platform during COVID-19

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