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Searching for Evidence of Scientific News in Scholarly Big Data

Hoque, Md Reshad Ul; Bradley, Dash; Kwan, Chiman; Chiatti, Agnese; Li, Jiang and Wu, Jian (2019). Searching for Evidence of Scientific News in Scholarly Big Data. Proceedings of the 10th International Conference on Knowledge Capture - K-CAP '19 pp. 251–254.

DOI (Digital Object Identifier) Link: https://doi.org/10.1145/3360901.3364438
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Abstract

Public digital media can often mix factual information with fake scientific news, which is typically difficult to pinpoint, especially for non-professionals. These scientific news articles create illusions and misconceptions, thus ultimately influence the public opinion, with serious consequences at a broader social scale. Yet, existing solutions aiming at automatically verifying the credibility of news articles are still unsatisfactory. We propose to verify scientific news by retrieving and analyzing its most relevant source papers from an academic digital library (DL), e.g., arXiv. Instead of querying keywords or regular named entities extracted from news articles, we query domain knowledge entities (DKEs) extracted from the text. By querying each DKE, we retrieve a list of candidate scholarly papers. We then design a function to rank them and select the most relevant scholarly paper. After exploring various representations, experiments indicate that the term frequency-inverse document frequency (TF-IDF) representation with cosine similarity outperforms baseline models based on word embedding. This result demonstrates the efficacy of using DKEs to retrieve scientific papers which are relevant to a specific news article. It also indicates that word embedding may not be the best document representation for domain specific document retrieval tasks. Our method is fully automated and can be effectively applied to facilitating fake and misinformed news detection across many scientific domains.

Item Type: Journal Item
Copyright Holders: 2019 Association for Computing Machinery
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 68248
SWORD Depositor: Jisc Publications-Router
Depositing User: Jisc Publications-Router
Date Deposited: 25 Nov 2019 09:30
Last Modified: 27 Nov 2019 08:35
URI: http://oro.open.ac.uk/id/eprint/68248
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