Introduction
Australia’s parliamentary elections are coming under increasing pressure due to the increasing amount of misinformation and disinformation being shared on social media platforms.
People share content that is intentionally misleading or not based in fact. Others share this content without checking sources or its validity.
The challenge is becoming more acute with the rise of AI-generated audio and video clips. These fake clips can appear to show a politician saying or doing things that are simply untrue. The potential for such material to sway public opinion – and election outcomes – is increasingly disturbing.
The issue is even more concerning when you consider how major social platforms, such as Facebook and X are reducing their content checking procedures, leaving it up to users to flag posts they deem to be misinformation.
The Role of Graph Databases
One way of avoiding further escalation of the problem involves tackling the issue at the source, and one way of doing this is through the use of graph database technology.
This form of database structures and analyses information as entities and relationships and can help to untangle patterns and covert threads within them, ones which may seem legitimate but could be hiding scams or misinformation.
By revealing these connections and delivering otherwise hidden insights, graphs can unleash the power of contextual data to fight all kinds of deception.
There are three key qualities of graph technology that make it a powerful tool for investigating misinformation, fraud, and scams. They are:
- Spotting hidden relationships in large volumes of data:
Graph databases store data as a network of interconnected facts. This type of data model is useful for researchers to easily and quickly map and analyse complex connections. In the context of election misinformation in online advertising, for example, it’s the relationships between social media ads, funders, and candidates that would hold insights.By organising the data as “nodes” and “relationships,” graph databases can enable researchers to surface hidden patterns and relationships between the ads and account credentials and then analyse those patterns and anomalies within the weakly connected components to discern malicious accounts. - Traversing relationships at scale and speed:
Graph databases enable investigators to store detailed patterns of problematic actors and their online content. They can then query the data to uncover intricate connections between the suspicious actor and other entities. By easily extending across data at scale and quickly identifying shared credentials between multiple accounts, analysts are able to spot areas for further investigation.Graphs easily encompass historical data, so users can quickly uncover associations between different entities, like flagged and deleted social media accounts, for example, to build a more comprehensive analysis of how such networks can operate undetected on these social media platforms. - Uncovering massive financial fraud:
It isn’t just misinformation that can be untangled by graph technology. It can also uncover previously hidden financial scams.There are increasing numbers of cases where misleading political advertising has been placed on social media platforms with the objective of swaying public opinion. In some cases, bad actors had disguised themselves as election campaigns to scam engaged voters out of money by promising merchandise like hats, flags, or coins in exchange for their credit card information.
Keeping On Top of An Evolving Problem
Every year, organisations and consumers alike lose billions of dollars to online scams. Busting fraud and protecting users is all about finding and investigating the connections between various online entities, and modern technologies are helping to do so.
The challenge of misleading information in election cycles is also constantly rising. To ensure confidence in the democratic process is upheld, new ways to counter this must be found and used.
Operating like a master detective, graph technology is capable of mapping patterns and relationships across huge amounts of data, enabling users to expose digitally savvy bad actors and helping them keep one step ahead of a complex challenge facing society.
As a result, the technology has much to offer when it comes both to elections and financial scams.
