TY - GEN
T1 - A Privacy-aware Distributed Knowledge Graph Approach to QoIS-driven COVID-19 Misinformation Detection
AU - Shang, Lanyu
AU - Kou, Ziyi
AU - Zhang, Yang
AU - Chen, Jin
AU - Wang, Dong
N1 - This research is supported in part by the National Science Foundation under Grant No. CHE-2105032, IIS-2008228, CNS-1845639, CNS-1831669. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
PY - 2022
Y1 - 2022
N2 - In this paper, we focus on the quality of information service (QoIS) of COVID-19-related information on social media. Our goal is to provide reliable COVID-19 information service by accurately detecting the misleading COVID-19 posts on social media by exploring the community-contributed COVID-19 fact data (CCFD) from different social media platforms. In particular, CCFD refers to the fact-checking reports that are submitted to each social media platform by its users and fact-checking professionals. Our work is motivated by the observation that CCFD often contains useful COVID-19 knowledge facts (e.g., "COVID-19 is not a flu") that can effectively facilitate the identification of misleading COVID-19 social media posts. However, CCFD is often private to the individual social media platform that owns it due to the data privacy concerns such as data copyright of CCFD and user profile information of CCFD contributors. In this paper, we leverage the CCFD from different social media platforms to accurately detect COVID19 misinformation while effectively protecting the privacy of CCFD. Two critical challenges exist in solving our problem: 1) how to generate privacy-aware COVID-19 knowledge facts from the platform-specific CCFD? 2) How to effectively integrate the privacy-aware COVID-19 knowledge facts from different social media platforms to correctly assess the truthfulness of a COVID19 post? To address these challenges, we develop CoviDKG, a COVID-19 distributed knowledge graph framework that constructs a set of CCFD-based knowledge graphs on individual social media platform and exchanges the privacy-aware COVID19 knowledge facts across different platforms to effectively detect misleading COVID-19 posts. We evaluate CoviDKG on two real-world social media datasets and the results show that CoviDKG achieves significant performance gains compared to state-of-the-art baselines in accurately detecting misleading COVID-19 posts on social media.
AB - In this paper, we focus on the quality of information service (QoIS) of COVID-19-related information on social media. Our goal is to provide reliable COVID-19 information service by accurately detecting the misleading COVID-19 posts on social media by exploring the community-contributed COVID-19 fact data (CCFD) from different social media platforms. In particular, CCFD refers to the fact-checking reports that are submitted to each social media platform by its users and fact-checking professionals. Our work is motivated by the observation that CCFD often contains useful COVID-19 knowledge facts (e.g., "COVID-19 is not a flu") that can effectively facilitate the identification of misleading COVID-19 social media posts. However, CCFD is often private to the individual social media platform that owns it due to the data privacy concerns such as data copyright of CCFD and user profile information of CCFD contributors. In this paper, we leverage the CCFD from different social media platforms to accurately detect COVID19 misinformation while effectively protecting the privacy of CCFD. Two critical challenges exist in solving our problem: 1) how to generate privacy-aware COVID-19 knowledge facts from the platform-specific CCFD? 2) How to effectively integrate the privacy-aware COVID-19 knowledge facts from different social media platforms to correctly assess the truthfulness of a COVID19 post? To address these challenges, we develop CoviDKG, a COVID-19 distributed knowledge graph framework that constructs a set of CCFD-based knowledge graphs on individual social media platform and exchanges the privacy-aware COVID19 knowledge facts across different platforms to effectively detect misleading COVID-19 posts. We evaluate CoviDKG on two real-world social media datasets and the results show that CoviDKG achieves significant performance gains compared to state-of-the-art baselines in accurately detecting misleading COVID-19 posts on social media.
KW - COVID-19
KW - Misinformation
KW - Quality of Information Service (QoIS)
KW - Social Media
UR - http://www.scopus.com/inward/record.url?scp=85135379777&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135379777&partnerID=8YFLogxK
U2 - 10.1109/IWQoS54832.2022.9812879
DO - 10.1109/IWQoS54832.2022.9812879
M3 - Conference contribution
AN - SCOPUS:85135379777
T3 - 2022 IEEE/ACM 30th International Symposium on Quality of Service, IWQoS 2022
BT - 2022 IEEE/ACM 30th International Symposium on Quality of Service, IWQoS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE/ACM International Symposium on Quality of Service, IWQoS 2022
Y2 - 10 June 2022 through 12 June 2022
ER -