TY - GEN
T1 - Investigating rumor news using agreement-aware search
AU - Shang, Jingbo
AU - Shen, Jiaming
AU - Sun, Tianhang
AU - Liu, Xingbang
AU - Gruenheid, Anja
AU - Korn, Flip
AU - Lelkes, D.
AU - Yu, Cong
AU - Han, Jiawei
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/17
Y1 - 2018/10/17
N2 - Recent years have witnessed a widespread increase of rumor news generated by humans and machines. Therefore, tools for investigating rumor news have become an urgent necessity. One useful function of such tools is to see ways a specific topic or event is represented by presenting different points of view from multiple sources. In this paper, we propose Maester, a novel agreement-aware search framework for investigating rumor news. Given an investigative question, Maester will retrieve related articles to that question, assign and display top articles from agree, disagree, and discuss categories to users. Splitting the results into these three categories provides the user a holistic view towards the investigative question. We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences. Accordingly, we use gradient boosting tree models with keyword/entity matching features for relatedness detection, and leverage recurrent neural network to infer the level of agreement. Our experiments on the Fake News Challenge (FNC) dataset demonstrate up to an order of magnitude improvement of Maester over the original FNC winning solution, for agreement-aware search.
AB - Recent years have witnessed a widespread increase of rumor news generated by humans and machines. Therefore, tools for investigating rumor news have become an urgent necessity. One useful function of such tools is to see ways a specific topic or event is represented by presenting different points of view from multiple sources. In this paper, we propose Maester, a novel agreement-aware search framework for investigating rumor news. Given an investigative question, Maester will retrieve related articles to that question, assign and display top articles from agree, disagree, and discuss categories to users. Splitting the results into these three categories provides the user a holistic view towards the investigative question. We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences. Accordingly, we use gradient boosting tree models with keyword/entity matching features for relatedness detection, and leverage recurrent neural network to infer the level of agreement. Our experiments on the Fake News Challenge (FNC) dataset demonstrate up to an order of magnitude improvement of Maester over the original FNC winning solution, for agreement-aware search.
KW - Agreement Detection
KW - Relatedness Classification
KW - Rumor News
UR - http://www.scopus.com/inward/record.url?scp=85058037607&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058037607&partnerID=8YFLogxK
U2 - 10.1145/3269206.3272020
DO - 10.1145/3269206.3272020
M3 - Conference contribution
AN - SCOPUS:85058037607
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2117
EP - 2126
BT - CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
A2 - Paton, Norman
A2 - Candan, Selcuk
A2 - Wang, Haixun
A2 - Allan, James
A2 - Agrawal, Rakesh
A2 - Labrinidis, Alexandros
A2 - Cuzzocrea, Alfredo
A2 - Zaki, Mohammed
A2 - Srivastava, Divesh
A2 - Broder, Andrei
A2 - Schuster, Assaf
PB - Association for Computing Machinery
T2 - 27th ACM International Conference on Information and Knowledge Management, CIKM 2018
Y2 - 22 October 2018 through 26 October 2018
ER -