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|Title:||Analysing confirmation bias||Authors:||Teo, Kai Jie||Keywords:||Engineering::Computer science and engineering::Computer applications::Social and behavioral sciences||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Teo, K. J. (2022). Analysing confirmation bias. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158259||Project:||SCSE21-0161||Abstract:||In recent years, there has been a rise in usage of social media by the global population, partially due to the rise in capabilities of portable devices such as the smartphones. Along with the increase usage in social media, more people are also consuming news through social media platforms, as news media corporations also use social media platforms as another means of reporting news coverage, in addition to their traditional methods such as through print and mass media. The availability of news on social media platforms inevitably lead to numerous interactions by users of social media, as many users comment and react to these posts posted by the news providers. These interactions can sometimes lead to polarisation as more biased views are expressed. The behaviour of users making biased views due to their initial beliefs can be associated with a psychological phenomenon known as confirmation bias. This project is thus conducted to study the amount of confirmation bias that exist in some social media posts on Twitter, as well as to analyse the sentiments of the comments expressed by users on these posts. The study is conducted through a proposed solution, where the solution uses a mix of machine learning and non-machine learning methods. The result from the study suggests that confirmation bias exists in the Twitter posts included in the study, as some users display signs of confirmation bias based on the comments they have written and their behaviour outside of the conversation, such as in their other comment replies, retweets and likes. These users also tend to be vocal on various issues as they interact heavily with other contents posted by news providers on Twitter.||URI:||https://hdl.handle.net/10356/158259||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
Updated on Dec 1, 2022
Updated on Dec 1, 2022
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