Author: Tao Qiu

We’ve talked a lot about information cascades and epidemics in class so I’m going to combine them and talk about people tweeting about the ebola outbreak from a few years back. Ebola is very deadly and tragic which got many people to fear it. However, it is extremely rare and unlikely for anyone in the Western world to get it. By examining the relationships between tweets and retweets, it becomes more evident how such news spread through people, and how the news has brought both positive and negative effects. Positive effects are that people are more aware of the situation and can thus help out by donating to the Red Cross charities to help the poor infected Africans. Negative effects are that people might live in unnecessary fear which is not good for their mental health. This report doesn’t have an actual diagram of the graph of the relationship structure of tweets and retweets since it’s studying thousands of tweets. It does give statistics of the graph though and I’ll use some of them to construct the graph myself but with descriptions.

To begin, it’s stated that 91% of the ebola retweets came from the initial message while 47.5% of those retweets is one direct retweet of the initial message (height 1 of the graph). Those 47.5% of retweets have a structural virality of 2. This shows that the spread of information is in a broadcast model instead of a viral model. This does make a lot of sense since the distribution of Twitter followers follows the power law with celebrities having lots of followers who would retweet from the celebrity. Celebrity tweets are often always trending so they’re easily seen. The broadcast model has its own advantages over the viral model. Because in a broadcast model, the average person would look at a tweet that’s been retweeted a lesser number of times. Having more and more retweets and adding commentary to the retweets is more likely to spread fake news since fake news spreads faster than real news, especially on a topic like ebola that is bound to shock people. If the original tweet was made by someone reliable like Barack Obama or UNICEF, then people in a broadcast model will get more reliable information since they’re closer to the reliable source.

In conclusion, no matter what topic especially if it’s serious, people should always see from whom they’re retweeting from because fake news can spread easily so it is better for everyone to retweet from a smaller group of reliable sources.

Retrieved from https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-019-6747-8

Throughout the last few decades, the United States has been working to stop the illegal cocaine trade. Most of the cocaine is produced in Colombia where traffickers send them through various routes in Central America and into the United States. The United States use interdiction to seize cocaine shipments at several checkpoints in Central America and to make arrests. However, the NarcoLogic model is proposed to showcase that interdiction may not be working as intended and instead, it may end up opening up new routes in Central America that are more well hidden. Currently, the United States is spending as much as $18 billion from its drug control budget towards interdiction. However, over the last 20 years or so, cocaine prices have dropped and deaths by cocaine overdoses have increased. This leads to people thinking that interdiction may not be effective.

As you can see from the graph, the red nodes indicate active checkpoints while the gray nodes indicate inactive checkpoints that may have been interdicted. Dashed edges indicate trafficking routes between checkpoints. Important red nodes are the one at the bottom right in Colombia which is the main source of cocaine as well as the ones near the Mexican border which is the main destination. There are a lot of inactive nodes in Costa Rica and Panama where there used to be a lot of checkpoints. But interdiction has rendered them inactive so in turn, Colombia gets a higher degree in the network by connecting edges all the way to Honduras and Nicaragua. This decreases the overall diameter of the network as well as average path length which means less checkpoints and harder for law enforcement to track cocaine shipments. Most edges in the graph are local bridges with relatively low betweenness which showcase the many paths that cocaine can get trafficked across Central America. So even if one local government decides to take a stand against cocaine trafficking, people can easily use other routes which shows how ineffective interdiction can be. There is barely any triadic closure which makes sense when you’re mapping routes because routes are all about taking shortcuts. This proves that cocaine trafficking can manage to be very efficient without needing extra routes since law enforcement in these countries often do not have to resources to stop it.

In conclusion, interdiction has not been very effective at stopping cocaine trafficking since it has instead formed shorter routes and those that are more spread out into either remote regions of areas of high poverty.