Automatic detection of COVID-19 vaccine misinformation with graph link prediction

Weinzierl, Maxwell A.; Harabagiu, Sanda M.

doi:10.1016/j.jbi.2021.103955

Cited by 22 publications

(14 citation statements)

References 31 publications

(8 reference statements)

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“…The results of Step 2 of the method, aiming to scale up the discovery of framings on the entire Tweet collections discussing the vaccines, were evaluated using the following metrics: Precision ( ), Recall ( ) and -measure. computes the number of tweets correctly identified to evoke any framing, out of all tweets that the system reported in in (Weinzierl & Harabagiu, 2021 ) automatically pinpointed. measures how many tweets were identified by the system reported in in (Weinzierl & Harabagiu, 2021 ) to evoke a framing out all of the tweets that were judged to do so, and .…”

Section: Resultsmentioning

confidence: 99%

“… computes the number of tweets correctly identified to evoke any framing, out of all tweets that the system reported in in (Weinzierl & Harabagiu, 2021 ) automatically pinpointed. measures how many tweets were identified by the system reported in in (Weinzierl & Harabagiu, 2021 ) to evoke a framing out all of the tweets that were judged to do so, and . We measured 80.1%; 83.2%, and 81.6% when evaluating on the test collection for the HPV vaccine while 71.5%, 75.8%, and 73.6% when evaluated in the test collection for COVID-19 vaccines.…”

Section: Resultsmentioning

confidence: 99%

“…Then we used a Natural Language Processing (NLP) method taking advantage of Deep Learning to recognize all tweets evoking any vaccine hesitancy framing. The NLP method, detailed in (Weinzierl & Harabagiu, 2021 ), uses a supervised learning framework. This entails that we have divided the judged tweets into a training set, a validation set, and a testing set.…”

Section: Methodsmentioning

confidence: 99%

“…When knowledge embedding models learn to represent each node and each relation from a graph, they rely on a link scoring function, which we have used to predict if a tweet evokes a framing , and thus should be included in the graph FCG- . In addition, as we detail in (Weinzierl & Harabagiu, 2021 ), we designed a neural architecture that also considered the language from each tweet, not only its neural representation to make the prediction of a tweet that should be linked in each FCG- . This method allowed us to discover that there were 282,651 tweets in that evoked some and 1,256,369 tweets in that evoked some .…”

Section: Methodsmentioning

confidence: 99%

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Scaling up the discovery of hesitancy profiles by identifying the framing of beliefs towards vaccine confidence in Twitter discourse

2022

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Our study focused on the discovery of how vaccine hesitancy is framed in Twitter discourse, allowing us to recognize at-scale all tweets that evoke any of the hesitancy framings as well as the stance of the tweet authors towards the frame. By categorizing the hesitancy framings that propagate misinformation, address issues of trust in vaccines, or highlight moral issues or civil rights, we were able to empirically recognize their ontological commitments. Ontological commitments of vaccine hesitancy framings couples with the stance of tweet authors allowed us to identify hesitancy profiles for two most controversial yet effective and underutilized vaccines for which there remains substantial reluctance among the public: the Human Papillomavirus and the COVID-19 vaccines. The discovered hesitancy profiles inform public health messaging approaches to effectively reach Twitter users with promise to shift or bolster vaccine attitudes. Supplementary Information The online version contains supplementary material available at 10.1007/s10865-022-00328-z.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Scaling up the discovery of hesitancy profiles by identifying the framing of beliefs towards vaccine confidence in Twitter discourse

2022

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“…This work focused on vaccine stance in general, but since the start of the COVID-19 pandemic vaccine stance classification has become almost inextricably linked to COVID-19 due to its societal relevance. An example is [22] who present CoV-axLies, a COVID-19 vaccine misinformation dataset, and demonstrate that their model, based on knowledge graphs, outperforms widely used classification methods for the detection of vaccine misinformation, an important cause of vaccine hesitancy.…”

Section: Related Researchmentioning

confidence: 99%

CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection

Lemmens¹,

Nooten²,

Kreutz³

et al. 2022

Preprint

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We present CoNTACT 1 : a Dutch language model adapted to the domain of COVID-19 tweets. The model was developed by continuing the pre-training phase of RobBERT [3] by using 2.8M Dutch COVID-19 related tweets posted in 2021. In order to test the performance of the model and compare it to RobBERT, the two models were tested on two tasks: (1) binary vaccine hesitancy detection and (2) detection of arguments for vaccine hesitancy. For both tasks, not only Twitter but also Facebook data was used to show cross-genre performance. In our experiments, CoNTACT showed statistically significant gains over RobBERT in all experiments for task 1. For task 2, we observed substantial improvements in virtually all classes in all experiments. An error analysis indicated that the domain adaptation yielded better representations of domain-specific terminology, causing CoNTACT to make more accurate classification decisions.

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Exploring the scope of explainable artificial intelligence in link prediction problem-an experimental study

Dwivedi,

Pandey,

Saxena

2024

Multimed Tools Appl

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Automatic detection of COVID-19 vaccine misinformation with graph link prediction

Cited by 22 publications

References 31 publications

Scaling up the discovery of hesitancy profiles by identifying the framing of beliefs towards vaccine confidence in Twitter discourse

Scaling up the discovery of hesitancy profiles by identifying the framing of beliefs towards vaccine confidence in Twitter discourse

CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection

Exploring the scope of explainable artificial intelligence in link prediction problem-an experimental study

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