Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents
Authors: 
Sayar Ghosh Roy, Anshul Padhi, Risubh Jain, Manish Gupta, 
Vasudeva VarmaAuthors Info & Claims
Sayar Ghosh Roy, Anshul Padhi, Risubh Jain, Manish Gupta, Vasudeva VarmaAuthors Info & ClaimsPages 11 - 20
Abstract
Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge, this is the first dataset automatically created using streams of incoming search engine queries to generate sentence-level popularity annotations. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task. Our proposed technique coupled with a BERT-based neural model exceeds nDCG values of 0.8 for proactive sentence-specific popularity forecasting. Notably, our study presents a non-trivial takeaway: though popularity and salience are different concepts, transfer learning from salience prediction enhances popularity forecasting. We release InfoPop and make our code publicly available1.
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Presentation Video for 'Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents' at ACM Conference on Hypertext and Social Media (HT '22) [Main Track]
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June 2022
272 pages
ISBN:9781450392334
DOI:10.1145/3511095
- General Chairs:
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- Ludovico Boratto,
- Program Chair:
- Federica Cena
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Published: 28 June 2022
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