Details
| Original language | English |
|---|---|
| Title of host publication | ASONAM '23 |
| Subtitle of host publication | Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining |
| Editors | B. Aditya Prakash, Dong Wang, Tim Weninger |
| Pages | 136-143 |
| Number of pages | 8 |
| Publication status | Published - 15 Mar 2024 |
| Event | 15th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2023 - Kusadasi, Turkey Duration: 6 Nov 2023 → 9 Nov 2023 |
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.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Information Systems
- Decision Sciences(all)
- Information Systems and Management
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Psychology(all)
- Social Psychology
- Social Sciences(all)
- Communication
Sustainable Development Goals
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ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ed. / B. Aditya Prakash; Dong Wang; Tim Weninger. 2024. p. 136-143.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Tweeted Fact vs Fiction
T2 - 15th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2023
AU - Ghosh, Shreya
AU - Mitra, Prasenjit
PY - 2024/3/15
Y1 - 2024/3/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85190627629&partnerID=8YFLogxK
U2 - 10.1145/3625007.3627307
DO - 10.1145/3625007.3627307
M3 - Conference contribution
AN - SCOPUS:85190627629
SN - 9798400704093
SP - 136
EP - 143
BT - ASONAM '23
A2 - Aditya Prakash, B.
A2 - Wang, Dong
A2 - Weninger, Tim
Y2 - 6 November 2023 through 9 November 2023
ER -