Over recent years the term “fake news” is getting used more frequently. Fake news defined by the Cambridge dictionary is “false stories that appear to be news, spread on the internet or using other media, usually created to influence political views or as a joke”. The recent increase in fake news has negative effects, one being that people believe the lies told as truth and another effect is that it changes the way people view legitimate news. It can be very harmful in certain scenarios such as spreading fake cures for coronavirus that is actually dangerous or spreading lies that can have a big impact on an election.
The paper, “Studying Fake News via Network Analysis: Detection and Mitigation” by Kai Shu, H. Russell Bernard, and Huan Liu examines the spread of fake news through network analysis. They talk about various network properties that helped them do analysis on fake news; Echo Chambers, Individual Users, Filter Bubbles, and Malicious Accounts.
Echo chambers refer to the idea that beliefs are amplified because the person is in a closed system. It is based on 2 factors; social credibility and frequency heuristic. Social credibility is a factor because people usually surround themselves with like-minded people, hence most people in a person X’s circle will have similar beliefs as them. Because everyone around X thinks that news, which could be fake, is correct, X will also believe it cause of social credibility. The other factor frequency heuristic points out that people are likely to believe something they hear more often. Therefore if a person is in a closed system and everyone in it believes the same info then they will not hear any other opinions. So if the fake news is what they are hearing the most then they will likely believe it.
Filter bubbles are similar to echo chambers but it’s isolation on social media. Almost all social media sites use algorithms to figure out what type of content you like. They then proceed to continue feed you content on that topic and things similar to it. Due to this users would get to see one perspective on their social media like they are in a closed system. Therefore if they are only fed fake news then they will believe it.
Both echo chambers and filter bubbles relate to strong triadic closure, a topic we learned in class. Looking at the figure above, Y is close to and agrees with what both X and Z say. So even if initially X and Z aren’t friends, because Y has a strong connection with both of them, X and Z will become friends and trust each other’s opinions. Therefore if Z is talking about some fake news to X, because Y believes and trusts Z, X will also believe that fake news.
It’s interesting to see that rise of fake news has been at the same time as the rise of social media. The echo chambers and flitters bubble help understand why this is the case through network analysis. We usually surround over selves if with people who have similar views and believes and only view content from one side perspective. Therefore it is important to educate ourselves with different perspectives, rather than believing something from the first and maybe only perspective you see.
Works Cited
One reply on “Why fake news is so common”
This is quite interesting. I did my blog post about using graph theory to find fake news which involves tracing back the sources and the ways that these fakes news spread which is often from bots and phishing sites. Taking into account your research, it may be hard to identify fake news from trustable connections alike the example you have given, which also explains why not all fake news can be filtered out! Thanks for the information!