Tweeted Fact vs Fiction

Identifying Vaccine Misinformation and Analyzing Dissent

authored by: 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.
Organisation(s): L3S Research Centre
External Organisation(s): Pennsylvania State University
Type: Conference contribution
Pages: 136-143
No. of pages: 8
Publication date: 15.03.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Artificial Intelligence, Computer Networks and Communications, Information Systems, Information Systems and Management, Safety, Risk, Reliability and Quality, Social Psychology, Communication
Sustainable Development Goals: SDG 3 - Good Health and Well-being
Electronic version(s): https://doi.org/10.1145/3625007.3627307 (Access: Closed)

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",
}