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Social Media and Information Cascades

In the age of technology and social media, social networking sites such as Facebook, Twitter, and many others have gained popularity. Many people have accounts on different sites, each with a different network. Users can post on their feeds and timelines and can reach a large audience. One of the defining features of social media is the way photos and videos are shared among the many users. Sometimes users share something, such as a picture, video, or article, and it can go viral, which means that it spreads quickly and widely on the Internet. One reason behind this is known as information cascades. This is an idea that a group of people are making choices in sequence. These choices can be based on observations they make about the actions of the people before them, like whether they shared or reshared a post. This set of choices can then lead to a cascade effect where the larger something grows, the more popularity it gains. On these social media sites, resharing or reposting allows users to share the content of others with their own network. When a person posts or shares something they introduce it to their friends in their network who then introduce it to their own network by resharing the post. This causes an expansion of the network and increases the number of people who see the post. The popularity of something depends on the number of viewers it reaches. Some things become more popular than others. So, there is interest in finding out how to predict something that is likely to be popular compared to something that is not.

The decision of resharing or not can depend on whether someone they know has reshared the post. First someone sees the post that was shared or that showed up on their feed or timeline. Then they decide whether they want to share it with their network or not. People make the decision sequentially, and each person can observe the choices made by those who acted earlier. Each person has some information besides their own that can influence their decision and behavior. Sometimes the person sharing the post might not even find it funny or interesting, but they don’t take that into consideration. They just share the post because many before them shared it. It is a cascading effect.

But that does not mean that every post will become popular and go viral. It is difficult to predict which of the many millions posts on Facebook or any other social media will go viral. In the context of Facebook, the term “cascades” is used to describe photos or videos that have been shared multiple times. But this happens very rarely. According to data provided by Facebook scientists and university scientists, only 1 in 20 photos posted on the social network gets shared even once and just 1 in 4,000 gets more than 500 shares. But that is not enough to make something go viral. To predict which photos would go viral on Facebook a preliminary analysis was done, and it was determined that at any given point in a cascade, there was a 50-50 chance that the number of shares would double. The variables that affected these chances and helped to get an accurate prediction were the rate and speed at which photos were shared, and the structure of sharing which means the number of networks the photo was shared in and how it was shared. Photos reposted in multiple networks proved to create stronger cascades. Scientist factored several criteria into their analysis and were able to accurately predict doubling events almost 80 percent of the time. The algorithm became more accurate the more times a photo was shared. For photos shared hundreds of times, their accuracy rate approached 88 percent, especially if the cascades quickly unfolded. But none of this guarantees that a post will spread widely throughout a network into other networks. The most important thing is understanding the network in which the post is being viewed and potentially shared. Also, the type of information can be important in determining whether something becomes popular or not. Something that is popular in a different network may or may not be popular in your network. Analyzing the dynamic behaviors of social networks is important in understanding the structure of large networks.

https://news.stanford.edu/news/2014/april/viral-photo-cascade-040314.html

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