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

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, ${\mathsf {Cov19-RN}}$ and ${\mathsf {Use20-RN}}$, based on COVID-19 and US Elections 2020 misinformation trends on Twitter (currently $\mathbb {X}$). 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, ${\mathsf {GEPA}}$, for rumor source detection. ${\mathsf {GEPA}}$ 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 ${\mathsf {GEPA}}$ significantly outperforms the state-of-the-art methods, producing detection rates of 73.6% against 61.5% of the closest competing method on ${\mathsf {Cov19-RN}}$, and 61.5% against 52.6% of the closest competing method on ${\mathsf {Use20-RN}}$. 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.