Predicting Audience Tweet Engagement
Name
Wu-juliajwu-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
Size
1.16 MB
Format
Adobe PDF
Checksum (MD5)
2fe19ca922e8279713721bbd32a0a87f
Author(s)
Wu, Julia
Advisor(s)
Roy, Deb
Date Issued
February 2022
Publisher
Massachusetts Institute of Technology
Abstract
Social media has become the ubiquitous infrastructure through which the world is connected. It allows people to interact not only with family members and friends but also with prominent figures like movie stars, presidential candidates, and even royalty. These celebrities have immense presences on social media, and each post they share has the potential to reach millions of people. As the sphere of social media influence grows increasingly large, it also becomes increasingly important to be able to understand how influencers on social media affect their audience. However, it is difficult for individuals with large social media platforms to gain insight into how their posts influence their followers. While social media platforms do provide influencers with some audience breakdowns and statistics, they are often not granular enough to be useful. In this thesis, we present methods to analyze an influencer’s tweets and audience. We then use these results to predict which segments of an influencers audience will interact with different types of posts. These insights can help determine which areas an influencer has the greatest potential to make an impact in and thus guide the direction and content of influencer campaigns.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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