Predicting Audience Tweet Engagement
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 s...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/143330 |
| _version_ | 1811068288720961536 |
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| author | Wu, Julia |
| author2 | Roy, Deb |
| author_facet | Roy, Deb Wu, Julia |
| author_sort | Wu, Julia |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T07:54:03Z |
| format | Thesis |
| id | mit-1721.1/143330 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T07:54:03Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1433302022-06-16T04:00:57Z Predicting Audience Tweet Engagement Wu, Julia Roy, Deb Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. M.Eng. 2022-06-15T13:13:00Z 2022-06-15T13:13:00Z 2022-02 2022-02-22T18:32:23.342Z Thesis https://hdl.handle.net/1721.1/143330 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Wu, Julia Predicting Audience Tweet Engagement |
| title | Predicting Audience Tweet Engagement |
| title_full | Predicting Audience Tweet Engagement |
| title_fullStr | Predicting Audience Tweet Engagement |
| title_full_unstemmed | Predicting Audience Tweet Engagement |
| title_short | Predicting Audience Tweet Engagement |
| title_sort | predicting audience tweet engagement |
| url | https://hdl.handle.net/1721.1/143330 |
| work_keys_str_mv | AT wujulia predictingaudiencetweetengagement |