Author: Gabrian Mak

It has been about 10 days since the election day(Biden vs Trump) started, but only after several days were the results announced. The specific events that caused the latency of the results being published are rather political and not the focus of this blog post. I, however, wanted to focus on the actions that the news networks could take to report the events of the presidential election. According to game theory, we will reference the actions that these news networks could take as strategies. To elaborate more, the news networks always want to be able to report the most up to date news that is valid and true. Therefore, the ultimate dilemma for news networks is to wait and be delayed to announce the sure victor or immediately announce the victor with a chance that they will be wrong, resulting in hurting the news network’s reputation.

Let’s consider an example, Fox News and ABC news. The possible strategies can be stated as follows:

1. Fox News and ABC news delay reporting the sure victor
2. Fox News delay reporting the sure victor, but ABC news immediately report the false victor
3. Fox News immediately report the false victor, but ABC news delay reporting the sure victor
4. Fox News and ABC news immediately report the false victor

There are obviously more variations of the strategies, but this is a simple example that will give us a sufficient analysis of what the news networks did. With these strategies, let us consider the results of each one. (Number references to the strategy number above)

1. Readers read the article about delaying the report, they may be furious but what they read is true and readers still believe the news networks.
2. Readers read abc news and find out they reported fake news, readers will lose trust in abc news. Additionally, since fox news is reporting differently, readers that have read the news from abc news may doubt the truth about what fox news is reporting.
3. Readers read fox news and find out they reported fake news, readers will lose trust in fox news. Additionally, since ABC news is reporting differently, readers that have read the news from fox news may doubt the truth about what fox news is reporting.
4. Readers read the article and find out both news networks reported fake news, readers lose trust in both news networks.

With this analysis, we can assign values to Nash Equilibrium. An example is as such:

Even though figure 1 is a rough diagram, the values assigned are a reflection of the analysis above. With a decent knowledge of game theory, we can understand that the strategy that both news network delayed reporting is the pure strategy. In other words, that strategy is the best strategy such that both news networks have no incentive to change their strategy. This is the reason as to why there was a delay in news network reporting the victor as such information is crucial and can impact the trust of readers in the news networks.

Hopefully, this insight has given somewhat interesting insights into how game theory can be used to evaluate real-life strategies.

Sources:

Farrell, H. (2020, November 07). Want to know why the networks finally called it for Biden? Here’s the likely reason. Retrieved November 13, 2020, from https://www.washingtonpost.com/politics/2020/11/07/want-know-why-networks-finally-called-it-biden-heres-likely-reason/

Predicting an election’s aftermath with game theory: DW: 13.10.2020. (n.d.). Retrieved November 13, 2020, from https://www.dw.com/en/predicting-an-elections-aftermath-with-game-theory/av-55257954

As the election for the next president draws near, there is more attention in the media. The importance of media is very high as it is a means of communication for everyone around the world. It is supposed to report the truth for recent events, which is why it is important that the news is true. If the news isn’t true, then it is considered fake news and can affect real life events such as the election. This can lead to a devastating result such as the election of an unworthy president. Another recent event in which fake news has impacted real life events is the spreading of false information regarding COVID-19. There are countless fake news about the current virus such as “Dark skin people are protected from the infection” or “Hand sanitizer can’t kill viruses”. If people believed in the former and decided to go to the public without safety measures, it would result in a higher chance of getting the virus and may result in death. This is why it is important that fake news is ceased and deleted as soon as possible to save people around the world.

The challenges of identifying fake news can be broken down into three parts:

• Unverifiable Data
• Huge Volume of Data
• Evolving Behaviours

The use of algorithm detection often leads to false-positives and incorrect results near the edge cases(scenarios where the shared news is hard to determine as it is on the borderline). Instead, a better approach is to calculate a reliability score for each content. Automatically allow or disallow the content if the reliability score is high or low respectively. When the reliability score is around the middle, use graph theory to identify the likelihood of fake news.

Such a graph is created using three nodes with the relationship shown in figure 1:

• Account(age, history, connections)
• Post(Timestamp, flags/reactions, IP address)
• Article(Host URL, author)

These three nodes can help to find various connections that can be an indication of whether the content is fake or not. For example, accounts that are relatively new and share a content should be flagged as unreliable. Usually accounts with purpose to spread fake news don’t only share one content but multiple content. This would result in a graph like figure 2.

Another easily identifiable link that suggests fake news is one that is called “link farm” in which multiple accounts are used to increase the popularity of an account. Figure 3 shows this exact scenario where three accounts from the same IP address all share the same three articles which results in a diamond shape.

The above example is also a scenario which can be described as unusual behavior. It can be hard to detect using algorithm detection without graph theory.

In conclusion, it is important to stop the spreading of fake news as it can severely impact the outcome of various real life events. Thanks to Cambridge Intelligence, they have used the above to create such a program. This program is called KeyLines which attempts to filter fake news using the graph theory and a reliable data set from BS Detector and Buzzfeed Fact check.

Reference:

• Disney, A. (2017, February 28). Using graph technologies to weed out fake news. Retrieved October 09, 2020, from https://cambridge-intelligence.com/detecting-fake-news/

• Doughton, S. (2020, July 05). COVID-19 meets Election 2020: The perfect storm for misinformation. Retrieved October 09, 2020, from https://www.seattletimes.com/seattle-news/health/covid-19-meets-election-2020-the-perfect-storm-for-misinformation/