Real-time Social Media Content Recommendation for Live Sports Events
The presence of social media is getting greater in the sports arena. Many people who watch live sports games also follow social media platforms for live coverage and commentaries. Although these additional content can enrich the watching experiences of the audience, they may become distractions to t...
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| Other Authors: | |
| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/144583 |
| _version_ | 1826208288538099712 |
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| author | Liu, Renbin |
| author2 | Palacios, Tomás |
| author_facet | Palacios, Tomás Liu, Renbin |
| author_sort | Liu, Renbin |
| collection | MIT |
| description | The presence of social media is getting greater in the sports arena. Many people who watch live sports games also follow social media platforms for live coverage and commentaries. Although these additional content can enrich the watching experiences of the audience, they may become distractions to the audience from some key events in a live sports game. In this thesis, we propose a system that will automatically present relevant and engaging social media content for a live game. We will employ techniques in Natural Language Processing to filter social media posts to select the best ones for users to follow while watching the game. With an engagement prediction model augmented with other metadata of the post and of its author, the audience can enjoy the game without missing out on important game coverage and reactions on social media. |
| first_indexed | 2024-09-23T14:03:29Z |
| format | Thesis |
| id | mit-1721.1/144583 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:03:29Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1445832022-08-30T03:23:31Z Real-time Social Media Content Recommendation for Live Sports Events Liu, Renbin Palacios, Tomás Peng, Feifei Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science The presence of social media is getting greater in the sports arena. Many people who watch live sports games also follow social media platforms for live coverage and commentaries. Although these additional content can enrich the watching experiences of the audience, they may become distractions to the audience from some key events in a live sports game. In this thesis, we propose a system that will automatically present relevant and engaging social media content for a live game. We will employ techniques in Natural Language Processing to filter social media posts to select the best ones for users to follow while watching the game. With an engagement prediction model augmented with other metadata of the post and of its author, the audience can enjoy the game without missing out on important game coverage and reactions on social media. M.Eng. 2022-08-29T15:57:20Z 2022-08-29T15:57:20Z 2022-05 2022-05-27T16:19:56.322Z Thesis https://hdl.handle.net/1721.1/144583 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Liu, Renbin Real-time Social Media Content Recommendation for Live Sports Events |
| title | Real-time Social Media Content Recommendation for Live Sports Events |
| title_full | Real-time Social Media Content Recommendation for Live Sports Events |
| title_fullStr | Real-time Social Media Content Recommendation for Live Sports Events |
| title_full_unstemmed | Real-time Social Media Content Recommendation for Live Sports Events |
| title_short | Real-time Social Media Content Recommendation for Live Sports Events |
| title_sort | real time social media content recommendation for live sports events |
| url | https://hdl.handle.net/1721.1/144583 |
| work_keys_str_mv | AT liurenbin realtimesocialmediacontentrecommendationforlivesportsevents |