Predicting and Understanding News Social Popularity with Emotional Salience Features
Author:
Affiliation:
1. Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
Funder
Science and Engineering Research Council (SERC) A*STAR
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3343031.3351048
Reference36 articles.
1. To Post or Not to Post
2. Carl Ambroselli Julian Risch Ralf Krestel and Andreas Loos. 2018. Prediction for the Newsroom: Which Articles Will Get the Most Comments? North American Chapter of the Association for Computational Linguistics: Human Language Technologies 193--199. Carl Ambroselli Julian Risch Ralf Krestel and Andreas Loos. 2018. Prediction for the Newsroom: Which Articles Will Get the Most Comments? North American Chapter of the Association for Computational Linguistics: Human Language Technologies 193--199.
3. Monika Bednarek and Helen Caple. 2017. The Discourse of News Values: How News Organizations Create Newsworthiness. In New York: Oxford University Press. Monika Bednarek and Helen Caple. 2017. The Discourse of News Values: How News Organizations Create Newsworthiness. In New York: Oxford University Press.
4. What Makes Online Content Viral?
Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Incorporating Word Embedding and Hybrid Model Random Forest Softmax Regression for Predicting News Categories;Multimedia Tools and Applications;2023-09-15
2. Dual emotion based fake news detection: A deep attention-weight update approach;Information Processing & Management;2023-07
3. Content still matters. A machine learning model for predicting news longevity from textual and context features;Information Processing & Management;2023-07
4. Predicting public mental health needs in a crisis using situational indicators and social media emotions: A Singapore big data study;2023-04-25
5. The popularity of contradictory information about COVID-19 vaccine on social media in China;Computers in Human Behavior;2022-09
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3