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 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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Liu, Renbin

Advisors dc:contributor.advisor

• Palacios, Tomás
• Peng, Feifei

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144583

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144583