Finding Spoiler Bias in Tweets by Zero-shot Learning and Knowledge Distilling from Neural Text Simplification Avi Bleiweiss BShalem Research Sunnyvale, CA, USA
[email protected] Abstract evoke far less interest to users who consult online reviews first, and later engage with the media itself. Automatic detection of critical plot informa- Social media platforms have placed elaborate tion in reviews of media items poses unique challenges to both social computing and com- policies to better guard viewers from spoilers. On putational linguistics. In this paper we propose the producer side, some sites adopted a strict con- to cast the problem of discovering spoiler bias vention of issuing a spoiler alert to be announced in in online discourse as a text simplification task. the subject of the post (Boyd-Graber et al., 2013). We conjecture that for an item-user pair, the Recently, Twitter started to offer provisions for the simpler the user review we learn from an item tweet consumer to manually mute references to summary the higher its likelihood to present a specific keywords and hashtags and stop display- spoiler. Our neural model incorporates the ad- vanced transformer network to rank the sever- ing tweets containing them (Golbeck, 2012). But ity of a spoiler in user tweets. We constructed a despite these intricate mechanisms that aim to pre- sustainable high-quality movie dataset scraped emptively ward off unwanted spoilers, the solutions from unsolicited review tweets and paired with proposed lack timely attraction of consumers and a title summary and meta-data extracted from may not scale, as spoilers remain a first-class prob- a movie specific domain.