People Who Have Smaller Network are More Likely to Spreads Fake News on Twitter
Scientific analysis proofs that Twitter is excellent platform for spreading actual news and it is even better at spreading fake news unfortunately. Moreover, false claims about politics spread further than other category of news included in the analysis. In fact, numerically, it took the truth about six times as long as falsehood to reach 1500 people. Researchers have found that false news was more likely to be “viral”. So not only were the retweet chains longer, but they were more likely to branch off into new chains. Additionally, compared with people who spread true news, those who spread false news were newer to Twitter, had fewer followers, followed fewer people and were less active with social media platform. False tweets prompted greater feelings of surprise and disgust while true tweets generated replies expressing sadness and trust.
What is interested in this research is that people who were less active, had fewer followers, and followed fewer people are more likely to spread false news. In lecture, we’ve discussed how the diffusion of a behavior depends on the information spread through a group of people. This is a situation where it cannot be modeled well at a level of homogeneous populations. Many of our interactions with the rest of the world happen locally, instead of globally. We often do not care as much about the full population’s decisions as about the decisions made by friends and colleagues. In this case, people care and believe in the decisions or news tweet by follower and who that followed, even if they are nationally in the minority. The cluster of density p is a set of nodes such that each node in the set has at least a p fraction of its network neighbors in the set. If the remaining network contains a cluster of density greater than 1-q, then the set of initial adopters will not cause a complete cascade. Moreover, whenever a set of initial adopters does not cause a complete cascade with threshold q, the remaining network must contain a cluster of density greater than 1-q. We argued that clusters are obstacles to cascades and clusters is the only obstacles to cascades. If the cluster density is large, complete cascade is less likely to occur. In contrast, if the cluster density is small, which infers people on Tweeter who have small network, complete cascade is more likely to occur. This implies that people who have less followers and follow less people are more likely to change their behavior and believes in the behavior carried by his neighbors. The above reasons cause the phenomenon illustrated in the news article that compared with people who spread true news, those who spread false news were newer to Twitter, had fewer followers, followed fewer people and were less active with social media platform.
https://www.latimes.com/science/sciencenow/la-sci-sn-fake-news-twitter-20180308-story.html