Use of conditional generative adversarial network to produce synthetic data with the aim of improving the classification of users who publish fake news

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

esmaili arefeh; Farzi Saeed

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY Year:2021 | Volume:13 | Issue:47-48 Page(s): 136-159

Download Full-Text

Persian Verion

View:

270

Download:

Cites:

Information Journal Paper

Title

Use of conditional generative adversarial network to produce synthetic data with the aim of improving the classification of users who publish fake news

Author(s)

esmaili arefeh | Farzi Saeed | Issue Writer Certificate

Keywords

Fake news publisher user detection

Imbalanced datasets

Generative Adversarial Network

Graph of user interaction

Node Embedding

Abstract

For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has become a prominent issue in the research community. It is also important to detect the users who generate this false information and publish it on the network. This paper detects users who publish incorrect information on the Twitter social network in Persian. In this regard, a system has been established based on combining context-user and context-network features with the help of a conditional generative adversarial network (CGAN) for balancing the data set. The system also detects users who publish fake news by modeling the twitter social network into a graph of user interactions and embedding a node to feature vector by Node2vec. Also, by conducting several tests, the proposed system has improved evaluation metrics up to 11%, 13%, 12%, and 12% in precision, recall, F-measure and accuracy respectively, compared to its competitors and has been able to create about 99% precision, in detecting users who publish fake news.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

esmaili, arefeh, & Farzi, Saeed. (2021). Use of conditional generative adversarial network to produce synthetic data with the aim of improving the classification of users who publish fake news. IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY, 13(47-48 ), 136-159. SID. https://sid.ir/paper/950158/en

Vancouver: Copy

esmaili arefeh, Farzi Saeed. Use of conditional generative adversarial network to produce synthetic data with the aim of improving the classification of users who publish fake news. IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY[Internet]. 2021;13(47-48 ):136-159. Available from: https://sid.ir/paper/950158/en

IEEE: Copy

arefeh esmaili, and Saeed Farzi, “Use of conditional generative adversarial network to produce synthetic data with the aim of improving the classification of users who publish fake news,” IRANIAN COMMUNICATION AND INFORMATION TECHNOLOGY, vol. 13, no. 47-48 , pp. 136–159, 2021, [Online]. Available: https://sid.ir/paper/950158/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops