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)

BibTeX

@inproceedings{a4d2337b519842a3b44726e0de63a385,
title = "Tweeted Fact vs Fiction: Identifying Vaccine Misinformation and Analyzing Dissent",
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.",
author = "Shreya Ghosh and Prasenjit Mitra",
year = "2024",
month = mar,
day = "15",
doi = "10.1145/3625007.3627307",
language = "English",
isbn = "9798400704093",
pages = "136--143",
editor = "{Aditya Prakash}, B. and Dong Wang and Tim Weninger",
booktitle = "ASONAM '23",
publisher = "Association for Computing Machinery, Inc",
note = "15th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2023 ; Conference date: 06-11-2023 Through 09-11-2023",
}