Generalized Local Prominence for Source Detection in Real-World Rumor Networks

Syed Shafat Ali; Ajay Rastogi; Tarique Anwar; Syed Afzal M. Rizvi; Jian Yang; Jia Wu; Quan Z. Sheng

doi:10.1109/TKDE.2025.3567282

Generalized Local Prominence for Source Detection in Real-World Rumor Networks

Syed Shafat Ali^*, Ajay Rastogi, Tarique Anwar, Syed Afzal M. Rizvi, Jian Yang, Jia Wu, Quan Z. Sheng

^*Corresponding author for this work

Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

The problem of infection source detection deals with localizing the infection source in a given network. While the problem has been extensively studied in the past, researchers have mainly focused on simulated infection networks which may not be the correct reflection of the dynamics of real-world infections. More significantly, the existing methods assume that a rumor source lies at the center of an infection network (source-centrality), which is not always true in sparse real-world rumor networks. Due to the randomness of infection flow in such networks, the source may lie away from the center (source-skewness). There is also a lack of real-world infection network datasets to provide a true real-world perspective. Therefore, we revisit the source detection problem and contemplate a shift from mainstream simulations to a real-world paradigm. To this end, we generate two novel rumor network datasets, Cov19-RN and Use20-RN, based on COVID-19 and US Elections 2020 misinformation trends on Twitter (currently X). Besides, inspired by the technicalities inherent to real-world rumor networks, we propose a real-world oriented algorithm called Generalized Exoneration and Prominence based Age, GEPA, for rumor source detection. GEPA addresses the problem of source-skewness to detect rumor sources using the concept of generalized local prominence, which we introduce in this study. Our experiments show that GEPA significantly outperforms the state-of-the-art methods, producing detection rates of 73.6% against 61.5% of the closest competing method on Cov19-RN, and 61.5% against 52.6% of the closest competing method on Use20-RN. To the best of our knowledge, this study is the first such work to deal with source detection in real-world rumor networks and address the problem of source-skewness.

Original language	English
Article number	0b00006493e82e43
Journal	IEEE Transactions on Knowledge and Data Engineering
DOIs	https://doi.org/10.1109/TKDE.2025.3567282
Publication status	Accepted/In press - 2025

Bibliographical note

Publisher Copyright:
© 1989-2012 IEEE.

Keywords

COVID-19
Infection source detection
Misinformation
Real-world rumor networks
Source-skew
US Elections 2020

Access to Document

10.1109/TKDE.2025.3567282

Cite this

@article{9cf52dbdca6345d19a9ab15ef8bf036a,

title = "Generalized Local Prominence for Source Detection in Real-World Rumor Networks",

abstract = "The problem of infection source detection deals with localizing the infection source in a given network. While the problem has been extensively studied in the past, researchers have mainly focused on simulated infection networks which may not be the correct reflection of the dynamics of real-world infections. More significantly, the existing methods assume that a rumor source lies at the center of an infection network (source-centrality), which is not always true in sparse real-world rumor networks. Due to the randomness of infection flow in such networks, the source may lie away from the center (source-skewness). There is also a lack of real-world infection network datasets to provide a true real-world perspective. Therefore, we revisit the source detection problem and contemplate a shift from mainstream simulations to a real-world paradigm. To this end, we generate two novel rumor network datasets, Cov19-RN and Use20-RN, based on COVID-19 and US Elections 2020 misinformation trends on Twitter (currently X). Besides, inspired by the technicalities inherent to real-world rumor networks, we propose a real-world oriented algorithm called Generalized Exoneration and Prominence based Age, GEPA, for rumor source detection. GEPA addresses the problem of source-skewness to detect rumor sources using the concept of generalized local prominence, which we introduce in this study. Our experiments show that GEPA significantly outperforms the state-of-the-art methods, producing detection rates of 73.6% against 61.5% of the closest competing method on Cov19-RN, and 61.5% against 52.6% of the closest competing method on Use20-RN. To the best of our knowledge, this study is the first such work to deal with source detection in real-world rumor networks and address the problem of source-skewness.",

keywords = "COVID-19, Infection source detection, Misinformation, Real-world rumor networks, Source-skew, US Elections 2020",

author = "Ali, {Syed Shafat} and Ajay Rastogi and Tarique Anwar and Rizvi, {Syed Afzal M.} and Jian Yang and Jia Wu and Sheng, {Quan Z.}",

note = "Publisher Copyright: {\textcopyright} 1989-2012 IEEE.",

year = "2025",

doi = "10.1109/TKDE.2025.3567282",

language = "English",

journal = "IEEE Transactions on Knowledge and Data Engineering",

issn = "1041-4347",

publisher = "IEEE Computer Society",

}

TY - JOUR

T1 - Generalized Local Prominence for Source Detection in Real-World Rumor Networks

AU - Ali, Syed Shafat

AU - Rastogi, Ajay

AU - Anwar, Tarique

AU - Rizvi, Syed Afzal M.

AU - Yang, Jian

AU - Wu, Jia

AU - Sheng, Quan Z.

PY - 2025

Y1 - 2025

N2 - The problem of infection source detection deals with localizing the infection source in a given network. While the problem has been extensively studied in the past, researchers have mainly focused on simulated infection networks which may not be the correct reflection of the dynamics of real-world infections. More significantly, the existing methods assume that a rumor source lies at the center of an infection network (source-centrality), which is not always true in sparse real-world rumor networks. Due to the randomness of infection flow in such networks, the source may lie away from the center (source-skewness). There is also a lack of real-world infection network datasets to provide a true real-world perspective. Therefore, we revisit the source detection problem and contemplate a shift from mainstream simulations to a real-world paradigm. To this end, we generate two novel rumor network datasets, Cov19-RN and Use20-RN, based on COVID-19 and US Elections 2020 misinformation trends on Twitter (currently X). Besides, inspired by the technicalities inherent to real-world rumor networks, we propose a real-world oriented algorithm called Generalized Exoneration and Prominence based Age, GEPA, for rumor source detection. GEPA addresses the problem of source-skewness to detect rumor sources using the concept of generalized local prominence, which we introduce in this study. Our experiments show that GEPA significantly outperforms the state-of-the-art methods, producing detection rates of 73.6% against 61.5% of the closest competing method on Cov19-RN, and 61.5% against 52.6% of the closest competing method on Use20-RN. To the best of our knowledge, this study is the first such work to deal with source detection in real-world rumor networks and address the problem of source-skewness.

AB - The problem of infection source detection deals with localizing the infection source in a given network. While the problem has been extensively studied in the past, researchers have mainly focused on simulated infection networks which may not be the correct reflection of the dynamics of real-world infections. More significantly, the existing methods assume that a rumor source lies at the center of an infection network (source-centrality), which is not always true in sparse real-world rumor networks. Due to the randomness of infection flow in such networks, the source may lie away from the center (source-skewness). There is also a lack of real-world infection network datasets to provide a true real-world perspective. Therefore, we revisit the source detection problem and contemplate a shift from mainstream simulations to a real-world paradigm. To this end, we generate two novel rumor network datasets, Cov19-RN and Use20-RN, based on COVID-19 and US Elections 2020 misinformation trends on Twitter (currently X). Besides, inspired by the technicalities inherent to real-world rumor networks, we propose a real-world oriented algorithm called Generalized Exoneration and Prominence based Age, GEPA, for rumor source detection. GEPA addresses the problem of source-skewness to detect rumor sources using the concept of generalized local prominence, which we introduce in this study. Our experiments show that GEPA significantly outperforms the state-of-the-art methods, producing detection rates of 73.6% against 61.5% of the closest competing method on Cov19-RN, and 61.5% against 52.6% of the closest competing method on Use20-RN. To the best of our knowledge, this study is the first such work to deal with source detection in real-world rumor networks and address the problem of source-skewness.

KW - COVID-19

KW - Infection source detection

KW - Misinformation

KW - Real-world rumor networks

KW - Source-skew

KW - US Elections 2020

UR - http://www.scopus.com/inward/record.url?scp=105004811904&partnerID=8YFLogxK

U2 - 10.1109/TKDE.2025.3567282

DO - 10.1109/TKDE.2025.3567282

M3 - Article

AN - SCOPUS:105004811904

SN - 1041-4347

JO - IEEE Transactions on Knowledge and Data Engineering

JF - IEEE Transactions on Knowledge and Data Engineering

M1 - 0b00006493e82e43

ER -

Generalized Local Prominence for Source Detection in Real-World Rumor Networks

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Cite this