Help Political Rumor Modeling

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In summary, the two candidates running for mayor in the small town of Ecolopolis are Aaron Aardvark and Paula Platypus. Paula currently has a significant lead with 80% of the population supporting her. However, Aardvark's campaigners spread a rumor about Platypus having a drinking problem, causing people to switch their allegiance to Aardvark. Platypus' campaigners counteracted by refuting the rumor, leading people to switch back to supporting her. Ultimately, the outcome of the election will depend on the initial values and parameters chosen, but a SIR model, which treats rumors as an epidemic, could be used to predict the likely winner.
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xipe
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Homework Statement


The election for mayor is coming soon in the small town of Ecolopolis (population 5000).The two candidates are Aaron Aardvark and Paula Platypus. Paula Platypus currently has a big lead, with 80% of the population ready to vote for her.
As usual, the politics turn ugly. Five days before the election, Aardvark’s campaigners (all
6 of them) decide that they will taint Platypus’ reputation by spreading the rumor that she has a
drinking problem. Naturally, everyone they tell believes this rumor and switches their allegiance over to Aardvark. What’s more, each person who hears the rumor then spreads the rumor to everyone they chat with.
The Platypus campaigners (all 6 of them) realize what is going on. Two days before the election, they go out and start telling the townspeople that the rumor is a lie. The people they talk with believe them and switch their allegiance back to Platypus (if they supported her originally),and then help refute the lie with others they chat with.
Who is likely to win the election? And how likely is it?

Homework Equations


None given

The Attempt at a Solution


I have tried multiple things to model this situation. However, I am thinking it required a higher understanding of second order differential equations. This is for an engineering class, but diff eq isn't required. Every project we have done this semester has been easily done with diff eq but our coursework hasn't gotten us to second order DE's yet. Any guidance AT ALL is extremely helpful. As of now I have two really, really weird equations that don't really do a good job at deciding who has a better chance of winning.

Also, we recently worked on a sensitivity analysis, and I think this deals with that too (For testing sensitivity of word of mouth variables), if that helps at all.

Thank you so much for any help you can offer.
xipe
 
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  • #2
I would try a SIR model as rumors behave very much like an epidemic. And the rumors (infection, immunity, convalescence) seems to be the only parameter you can say something about.
 
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Thank you for the timely reply. I like what I have read from that link you posted, but I am unsure how to derive the solutions from a system of 3 DE's. I am just about half-way through my diff eq class and we haven't gotten so far as to attempt something like that. Any pointers would be greatly appreciated. The model is perfect though, three categories, all of which fit the bill.
 
  • #4
You can look on the internet for examples, solutions and theory. I assume there can be found plenty as it is one of the more famous differential equation systems. (Just type in "SIR model" on your preferred search page.) Or what can always be done, is to make it discrete. Consider small time intervals (half a day or so) and calculate the changes in steps. This way you can use the equations without having to solve the system. (Just calculate ##df(t)/dt ## as ##\Delta f(t) / \Delta t\,##.) What's also possible, is to draw some vector fields: take a point in the phase space (actually many) and draw a vector in the direction of change. This might not be an exact method, but it illustrates the general behavior. In any case there will be a great dependence on the initial values and parameters you chose.
 

What is political rumor modeling?

Political rumor modeling is a scientific approach to studying the spread and impact of political rumors. It involves using mathematical and statistical techniques to analyze data and predict the likelihood of a rumor being believed and shared among individuals or groups.

Why is political rumor modeling important?

Political rumor modeling is important because it helps us better understand how misinformation and false information can influence public opinion and political decision-making. By studying the patterns and factors that contribute to the spread of rumors, we can develop strategies to combat their influence and promote accurate information.

How do scientists collect data for political rumor modeling?

Scientists use a variety of methods to collect data for political rumor modeling, including surveys, social media analysis, and experiments. Surveys can provide information about individuals' beliefs and behaviors regarding rumors, while social media analysis can track the spread of rumors online. Experiments can also be conducted to test the effectiveness of different strategies for countering rumors.

What are some common factors that contribute to the spread of political rumors?

Some common factors that contribute to the spread of political rumors include confirmation bias, emotional appeals, and social influence. Confirmation bias is the tendency to believe information that aligns with our existing beliefs, while emotional appeals can make rumors more persuasive. Social influence, such as peer pressure or the influence of opinion leaders, can also play a role in the spread of rumors.

What are some potential applications of political rumor modeling?

Political rumor modeling has many potential applications, such as informing public relations and crisis management strategies, improving fact-checking and debunking efforts, and aiding in the development of policies to combat misinformation. It can also be used to study the effects of political rumors on elections and public opinion. Additionally, the insights gained from political rumor modeling can be applied to other areas where rumors and misinformation are prevalent, such as health and science communication.

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