research-article

Detecting Factual and Non-Factual Content in News Articles

Authors:

Debapriyo MajumdarAuthors Info & Claims

CODS '17: Proceedings of the 4th ACM IKDD Conferences on Data Sciences

Article No.: 17, Pages 1 - 12

https://doi.org/10.1145/3041823.3041837

Published: 09 March 2017 Publication History

Abstract

News articles are a major source of facts about the current state and events of our surrounding world. However, not all news articles are equally rich in presenting the facts. In this paper, we consider the problem of detecting factual and non-factual parts in news articles. We present a comprehensive survey on the existing literature on fact classification on news articles as well as a related and more widely studied problem of subjectivity vs objectivity classification of statements. Combining these techniques and some new features we design a framework for classifying facts and non-facts in news articles. We present extensive experiments on this task using several features and combinations of those on two datasets, one of which was used for subjectivity classification in previous works. We show that standard textual dataset dependent features such as n-grams produce good results on both datasets, but more general features such as part of speech tags and entity types produce inconsistent results. We analyze the results based on the nature of the datasets to present insights on the usefulness of the features and their applicability in the classification task we are considering.

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Cited By

Ng HChia GYap TGoh V(2022)Modelling sentiments based on objectivity and subjectivity with self-attention mechanismsF1000Research10.12688/f1000research.73131.210(1001)Online publication date: 17-May-2022
https://doi.org/10.12688/f1000research.73131.2
Ng HChia GYap TGoh V(2021)Modelling sentiments based on objectivity and subjectivity with self-attention mechanismsF1000Research10.12688/f1000research.73131.110(1001)Online publication date: 4-Oct-2021
https://doi.org/10.12688/f1000research.73131.1

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CODS '17: Proceedings of the 4th ACM IKDD Conferences on Data Sciences

March 2017

136 pages

ISBN:9781450348461

DOI:10.1145/3041823

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
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Published: 09 March 2017

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CODS '17

CODS '17: Fourth ACM IKDD Conferences on Data Sciences

March 9 - 11, 2017

Chennai, India

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Overall Acceptance Rate 197 of 680 submissions, 29%

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Cited By

Ng HChia GYap TGoh V(2022)Modelling sentiments based on objectivity and subjectivity with self-attention mechanismsF1000Research10.12688/f1000research.73131.210(1001)Online publication date: 17-May-2022
https://doi.org/10.12688/f1000research.73131.2
Ng HChia GYap TGoh V(2021)Modelling sentiments based on objectivity and subjectivity with self-attention mechanismsF1000Research10.12688/f1000research.73131.110(1001)Online publication date: 4-Oct-2021
https://doi.org/10.12688/f1000research.73131.1

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