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International Journal of Data Science and Analytics

Erschienen in:

14.06.2022 | Regular Paper

Data-driven analytics of COVID-19 ‘infodemic’

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 3/2023

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Abstract

The rampant of COVID-19 infodemic has almost been simultaneous with the outbreak of the pandemic. Many concerted efforts are made to mitigate its negative effect to information credibility and data legitimacy. Existing work mainly focuses on fact-checking algorithms or multi-class labeling models that are less aware of the intrinsic characteristics of the language. Nor is it discussed how such representations can account for the common psycho-socio-behavior of the information consumers. This work takes a data-driven analytical approach to (1) describe the prominent lexical and grammatical features of COVID-19 misinformation; (2) interpret the underlying (psycho-)linguistic triggers in terms of sentiment, power and activity based on the affective control theory; (3) study the feature indexing for anti-infodemic modeling. The results show distinct language generalization patterns of misinformation of favoring evaluative terms and multimedia devices in delivering a negative sentiment. Such appeals are effective to arouse people’s sympathy toward the vulnerable community and foment their spreading behavior.

Vorheriger Artikel Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing

Nächster Artikel COVID-19 and 5G conspiracy theories: long term observation of a digital wildfire

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More examples can be found in Mythbusters,Mayo Clinic,Avert, etc.

Donovan [3] defines ‘infodemic’ as ‘an overabundance of information some accurate and some not that makes it hard for people to find trustworthy sources and reliable guidance when they need it’.

Pathogenicty of misinformation is a metaphorical description of such claims in infecting people’ belief. It is based on the presupposed fact that misinformation is taken as a dangerous virus and poses great threats to the information credibility of the society. Thus, various organizations strive to combat and debunk misinformation like the COVID-19 virus.

We consider to further this part of work in future by adopting state-of-the-art neural networks and pre-trained models.

The dataset is available at https://github.com/ClaraWan629/CoMythFact.

https://www.nltk.org/.

The score ranges from − 5.00 to 5.00 indicating various scales of sentiment polarity, affective power, and active degree in the continuous space.

https://scikit-learn.org/stable/.

The word vectors are pre-trained using en_core_web_md. We use the mean vectors of words in 300 dimension as the sentence representations.

The code is available at https://github.com/ClaraWan629/CoMythFact/blob/main/main.py.

https://app.sketchengine.eu.

We use the 36 tags of the Penn Tree Bank Tagset: https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html.

LD = content*100%/(content + function)}.

https://github.com/ClaraWan629/CoMythFact.

As suggested by lexical density and the reverse relation of word and sentence length distribution.

As suggested by the dominance of verbal structures over nominal structures.

As suggested by the sentiment evaluation.

As suggested by the power evaluation.

As suggested by the activity evaluation.

A notion of persuasive fallacy arguments.

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Titel: Data-driven analytics of COVID-19 ‘infodemic’
Publikationsdatum: 14.06.2022
Erschienen in: International Journal of Data Science and Analytics / Ausgabe 3/2023
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI: https://doi.org/10.1007/s41060-022-00339-8

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