Health-related rumour detection on Twitter

Sicilia, Rosa; Giudice, Stella Lo; Pei, Yulong; Pechenizkiy, Mykola; Soda, Paolo

doi:10.1109/bibm.2017.8217899

Cited by 12 publications

(12 citation statements)

References 18 publications

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“…The included articles adopted disparate theoretical approaches in conceptualizing the phenomenon, with the dominant frameworks from the fields of psychology and network science. Theories employed in psychology aimed to explain individual-level cognitive response of misinformation and rumour online (Bode and Vraga, 2018;Bora et al, 2018;Chua and Banerjee, 2018;Li and Sakamoto, 2015;Ozturk et al, 2015), whereas network theories focus on the social mechanism and patterns of misinformation spread (Bessi et al, 2015;Radzikowski et al, 2016;Schmidt et al, 2018;Sicilia et al, 2017;Wood, 2018). Further co-citation analysis on all articles that investigated the phenomenon revealed that the disciplinary landscape concentrates around general science and vaccines/infectious disease, while psychology and communication studies have less cross-citation with the science and medicine literature.…”

Section: Resultsmentioning

confidence: 99%

“…Others have referred to psychological studies around conspiracist ideation, inoculation theory and social conformity in understanding the mechanism behind health misperception on social media (Bode and Vraga, 2018;Bora et al, 2018;Li and Sakamoto, 2015). Contrastingly, the use of system or network theories are aimed at explaining the patterns of social influence, social learning, social contagion and homophily and polarization processes (Bessi et al, 2015;Radzikowski et al, 2016;Schmidt et al, 2018;Sicilia et al, 2017;Wood, 2018). The framework typically assists the subsequent social network analysis.…”

Section: Theoretical Framework and Disciplines (Co-citation Analysis)mentioning

confidence: 99%

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Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Wang

McKee

Torbica

et al. 2019

Social Science & Medicine

1,069

806

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A B S T R A C TContemporary commentators describe the current period as "an era of fake news" in which misinformation, generated intentionally or unintentionally, spreads rapidly. Although affecting all areas of life, it poses particular problems in the health arena, where it can delay or prevent effective care, in some cases threatening the lives of individuals. While examples of the rapid spread of misinformation date back to the earliest days of scientific medicine, the internet, by allowing instantaneous communication and powerful amplification has brought about a quantum change. In democracies where ideas compete in the marketplace for attention, accurate scientific information, which may be difficult to comprehend and even dull, is easily crowded out by sensationalized news. In order to uncover the current evidence and better understand the mechanism of misinformation spread, we report a systematic review of the nature and potential drivers of health-related misinformation. We searched PubMed, Cochrane, Web of Science, Scopus and Google databases to identify relevant methodological and empirical articles published between 2012 and 2018. A total of 57 articles were included for full-text analysis. Overall, we observe an increasing trend in published articles on health-related misinformation and the role of social media in its propagation. The most extensively studied topics involving misinformation relate to vaccination, Ebola and Zika Virus, although others, such as nutrition, cancer, fluoridation of water and smoking also featured. Studies adopted theoretical frameworks from psychology and network science, while co-citation analysis revealed potential for greater collaboration across fields. Most studies employed content analysis, social network analysis or experiments, drawing on disparate disciplinary paradigms. Future research should examine susceptibility of different sociodemographic groups to misinformation and understand the role of belief systems on the intention to spread misinformation. Further interdisciplinary research is also warranted to identify effective and tailored interventions to counter the spread of health-related misinformation online.

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Section: Resultsmentioning

confidence: 99%

Section: Theoretical Framework and Disciplines (Co-citation Analysis)mentioning

confidence: 99%

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Wang

McKee

Torbica

et al. 2019

Social Science & Medicine

1,069

806

View full text Add to dashboard Cite

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“…Unsurprisingly, studies that relied on expert opinion used relatively small data sets (ranging from 109 to 625 tweets) compared with studies that used other labeling methods ( Table 1 ). Even those that used nonexperts but used manual coding (performed by nonexpert annotators) tended to work on a small sample of the data set [ 9 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Sicilia et al [ 9 ], Al-Rakhami and Al-Amri [ 18 ], and Chew and Eysenbach [ 16 ] defined credible tweets as tweets that have information from a confirmed, reliable source, such as the WHO, Centers for Disease Control, or another official health agency. This method differs from the method used by the second group mentioned previously as it first identified a tweet and then examined its source.…”

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

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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.

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“…It has practical value for those who care more about the credibility of single posts. Pioneer works have been conducted on the Twitter rumor detection task ( Sicilia et al, 2017 ; Sicilia et al, 2018a )). Most existing rumor detection models assume that each event has plenty of training instances and regard the task of rumor detection as the classification problem based on supervised learning.…”

Section: Introductionmentioning

confidence: 99%

A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media

Cheng

Song

et al. 2021

PeerJ Computer Science

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Background Rumor detection is a popular research topic in natural language processing and data mining. Since the outbreak of COVID-19, related rumors have been widely posted and spread on online social media, which have seriously affected people’s daily lives, national economy, social stability, etc. It is both theoretically and practically essential to detect and refute COVID-19 rumors fast and effectively. As COVID-19 was an emergent event that was outbreaking drastically, the related rumor instances were very scarce and distinct at its early stage. This makes the detection task a typical few-shot learning problem. However, traditional rumor detection techniques focused on detecting existed events with enough training instances, so that they fail to detect emergent events such as COVID-19. Therefore, developing a new few-shot rumor detection framework has become critical and emergent to prevent outbreaking rumors at early stages. Methods This article focuses on few-shot rumor detection, especially for detecting COVID-19 rumors from Sina Weibo with only a minimal number of labeled instances. We contribute a Sina Weibo COVID-19 rumor dataset for few-shot rumor detection and propose a few-shot learning-based multi-modality fusion model for few-shot rumor detection. A full microblog consists of the source post and corresponding comments, which are considered as two modalities and fused with the meta-learning methods. Results Experiments of few-shot rumor detection on the collected Weibo dataset and the PHEME public dataset have shown significant improvement and generality of the proposed model.

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Health-related rumour detection on Twitter

Cited by 12 publications

References 18 publications

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

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

A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media

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