Detecting Inspiring Content on Social Media

被引:1
|
作者
Ignat, Oana [1 ]
Boureau, Y-Lan [2 ]
Yu, Jane A. [3 ]
Halevy, Alon [3 ]
机构
[1] Univ Michigan, Ann Arbor, MI 48109 USA
[2] Facebook AI, New York, NY USA
[3] Facebook AI, Menlo Pk, CA USA
关键词
inspiration; social media data; natural language processing; emotions; sentiment; INSPIRATION; COMMUNICATION;
D O I
10.1109/ACII52823.2021.9597431
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Inspiration moves a person to see new possibilities and transforms the way they perceive their own potential. Inspiration has received little attention in psychology, and has not been researched before in the NLP community. To the best of our knowledge, this work is the first to study inspiration through machine learning methods. We aim to automatically detect inspiring content from social media data. To this end, we analyze social media posts to tease out what makes a post inspiring and what topics are inspiring. We release a dataset of 5,800 inspiring and 5,800 non-inspiring English-language public post unique ids collected from a dump of Reddit public posts made available by a third party and use linguistic heuristics to automatically detect which social media English-language posts are inspiring.
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页数:8
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