Abstract
Recently, fake news has been incurring many problems to our society. As a result, many researchers have been working on identifying fake news. Most of the fake news detection systems utilize the linguistic feature of the news. However, they have difficulty in sensing highly ambiguous fake news which can be detected only after identifying meaning and latest related information. In this paper, to resolve this problem, we shall present a new Korean fake news detection system using fact DB which is built and updated by human's direct judgement after collecting obvious facts. Our system receives a proposition, and search the semantically related articles from Fact DB in order to verify whether the given proposition is true or not by comparing the proposition with the related articles in fact DB. To achieve this, we utilize a deep learning model, Bidirectional Multi-Perspective Matching for Natural Language Sentence(BiMPM), which has demonstrated a good performance for the sentence matching task. However, BiMPM has some limitations in that the longer the length of the input sentence is, the lower its performance is, and it has difficulty in making an accurate judgement when an unlearned word or relation between words appear. In order to overcome the limitations, we shall propose a new matching technique which exploits article abstraction as well as entity matching set in addition to BiMPM. In our experiment, we shall show that our system improves the whole performance for fake news detection.
| Original language | English |
|---|---|
| Title of host publication | JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering |
| Subtitle of host publication | Knowledge Evolution Towards Singularity of Man-Machine Intelligence |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 209-212 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781728107196 |
| DOIs | |
| Publication status | Published - 2019 Jul |
| Event | 16th International Joint Conference on Computer Science and Software Engineering, JCSSE 2019 - Chonburi, Thailand Duration: 2019 Jul 10 → 2019 Jul 12 |
Publication series
| Name | JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence |
|---|
Conference
| Conference | 16th International Joint Conference on Computer Science and Software Engineering, JCSSE 2019 |
|---|---|
| Country | Thailand |
| City | Chonburi |
| Period | 19/7/10 → 19/7/12 |
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Keywords
- BiLSTM model
- Deep learning
- Fake news detection
- Natural Language Processing
- Sentence matching
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Human-Computer Interaction
- Signal Processing
- Software
- Information Systems and Management
Cite this
Fake News Detection System using Article Abstraction. / Kim, Kyeong Hwan; Jeong, Chang Sung.
JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence. Institute of Electrical and Electronics Engineers Inc., 2019. p. 209-212 8864154 (JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Fake News Detection System using Article Abstraction
AU - Kim, Kyeong Hwan
AU - Jeong, Chang Sung
PY - 2019/7
Y1 - 2019/7
N2 - Recently, fake news has been incurring many problems to our society. As a result, many researchers have been working on identifying fake news. Most of the fake news detection systems utilize the linguistic feature of the news. However, they have difficulty in sensing highly ambiguous fake news which can be detected only after identifying meaning and latest related information. In this paper, to resolve this problem, we shall present a new Korean fake news detection system using fact DB which is built and updated by human's direct judgement after collecting obvious facts. Our system receives a proposition, and search the semantically related articles from Fact DB in order to verify whether the given proposition is true or not by comparing the proposition with the related articles in fact DB. To achieve this, we utilize a deep learning model, Bidirectional Multi-Perspective Matching for Natural Language Sentence(BiMPM), which has demonstrated a good performance for the sentence matching task. However, BiMPM has some limitations in that the longer the length of the input sentence is, the lower its performance is, and it has difficulty in making an accurate judgement when an unlearned word or relation between words appear. In order to overcome the limitations, we shall propose a new matching technique which exploits article abstraction as well as entity matching set in addition to BiMPM. In our experiment, we shall show that our system improves the whole performance for fake news detection.
AB - Recently, fake news has been incurring many problems to our society. As a result, many researchers have been working on identifying fake news. Most of the fake news detection systems utilize the linguistic feature of the news. However, they have difficulty in sensing highly ambiguous fake news which can be detected only after identifying meaning and latest related information. In this paper, to resolve this problem, we shall present a new Korean fake news detection system using fact DB which is built and updated by human's direct judgement after collecting obvious facts. Our system receives a proposition, and search the semantically related articles from Fact DB in order to verify whether the given proposition is true or not by comparing the proposition with the related articles in fact DB. To achieve this, we utilize a deep learning model, Bidirectional Multi-Perspective Matching for Natural Language Sentence(BiMPM), which has demonstrated a good performance for the sentence matching task. However, BiMPM has some limitations in that the longer the length of the input sentence is, the lower its performance is, and it has difficulty in making an accurate judgement when an unlearned word or relation between words appear. In order to overcome the limitations, we shall propose a new matching technique which exploits article abstraction as well as entity matching set in addition to BiMPM. In our experiment, we shall show that our system improves the whole performance for fake news detection.
KW - BiLSTM model
KW - Deep learning
KW - Fake news detection
KW - Natural Language Processing
KW - Sentence matching
UR - http://www.scopus.com/inward/record.url?scp=85074241092&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074241092&partnerID=8YFLogxK
U2 - 10.1109/JCSSE.2019.8864154
DO - 10.1109/JCSSE.2019.8864154
M3 - Conference contribution
AN - SCOPUS:85074241092
T3 - JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence
SP - 209
EP - 212
BT - JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
ER -