A Unified Graph-Based Approach to Disinformation Detection using Contextual and Semantic Relations
Date Issued
June 2022
Abstract
As recent events have demonstrated, disinformation spread through social
networks can have dire political, economic and social consequences. Detecting
disinformation must inevitably rely on the structure of the network, on users
particularities and on event occurrence patterns. We present a graph data
structure, which we denote as a meta-graph, that combines underlying users'
relational event information, as well as semantic and topical modeling. We
detail the construction of an example meta-graph using Twitter data covering
the 2016 US election campaign and then compare the detection of disinformation
at cascade level, using well-known graph neural network algorithms, to the same
algorithms applied on the meta-graph nodes. The comparison shows a consistent
3%-4% improvement in accuracy when using the meta-graph, over all considered
algorithms, compared to basic cascade classification, and a further 1% increase
when topic modeling and sentiment analysis are considered. We carry out the
same experiment on two other datasets, HealthRelease and HealthStory, part of
the FakeHealth dataset repository, with consistent results. Finally, we discuss
further advantages of our approach, such as the ability to augment the graph
structure using external data sources, the ease with which multiple meta-graphs
can be combined as well as a comparison of our method to other graph-based
disinformation detection frameworks.
networks can have dire political, economic and social consequences. Detecting
disinformation must inevitably rely on the structure of the network, on users
particularities and on event occurrence patterns. We present a graph data
structure, which we denote as a meta-graph, that combines underlying users'
relational event information, as well as semantic and topical modeling. We
detail the construction of an example meta-graph using Twitter data covering
the 2016 US election campaign and then compare the detection of disinformation
at cascade level, using well-known graph neural network algorithms, to the same
algorithms applied on the meta-graph nodes. The comparison shows a consistent
3%-4% improvement in accuracy when using the meta-graph, over all considered
algorithms, compared to basic cascade classification, and a further 1% increase
when topic modeling and sentiment analysis are considered. We carry out the
same experiment on two other datasets, HealthRelease and HealthStory, part of
the FakeHealth dataset repository, with consistent results. Finally, we discuss
further advantages of our approach, such as the ability to augment the graph
structure using external data sources, the ease with which multiple meta-graphs
can be combined as well as a comparison of our method to other graph-based
disinformation detection frameworks.
File(s)![Thumbnail Image]()
Name
2109.11781 2.pdf
Size
722.93 KB
Format
Adobe PDF
Checksum (MD5)
7187aab9e4e170adb9b1a88d9a0563ef