Tweeted Fact vs Fiction

Identifying Vaccine Misinformation and Analyzing Dissent

verfasst von

Shreya Ghosh, Prasenjit Mitra

Abstract

In this paper, we develop an end-to-end knowledge extraction and management framework for COVID-19 vaccination misinformation. This framework automatically extracts information consistent and inconsistent with scientific evidence regarding vaccination. Additionally, using novel natural language processing methods (including triple-attention based sarcasm detection and utilizing topic-based similarity scoring, agglomerative clustering, and word embedding vectors for misinformation category identification and counter-fact summarization in a semi-supervised way from web-based sources), we explore public opinion towards vaccination resistance. Our knowledge extraction pipeline constructs knowledge-bases automatically, categorizes vaccine dissenting tweets into 15 misinformation categories automatically, and effectively analyzes discourses in those tweets. Our contributions are as follows: (i) the proposed knowledge extraction framework does not require huge amounts of labelled tweets of different categories (our method uses only 50-labelled tweets for each of 15 misinformation categories, in stark contrast to existing approaches that typically rely on 10,000 or more labelled tweets), and (ii) our module outperformed baselines by a significant margin of ≈ 8% to ≈ 14% (F1 score) in the classification tasks using Twitter dataset.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Pennsylvania State University

Typ

Aufsatz in Konferenzband

Seiten

136-143

Anzahl der Seiten

8

Publikationsdatum

15.03.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Artificial intelligence, Computernetzwerke und -kommunikation, Information systems, Informationssysteme und -management, Sicherheit, Risiko, Zuverlässigkeit und Qualität, Sozialpsychologie, Kommunikation

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1145/3625007.3627307 (Zugang: Geschlossen)