Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: May 2024
Next issue: Aug 2024
Avg review time: 55 days
Avg accept to publ: 60 days
APC: 300 EUR


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


TRAFFIC STATS

2,628,782 unique visits
1,044,094 downloads
Since November 1, 2009



Robots online now
Googlebot


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 2 / 2024
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
Issue 2/2022

AbstractPlus






LATEST NEWS

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

Read More »


    
 

  1/2024 - 7

Multidisciplinary Fusion Perspective Analysis Method for False Information Recognition

FAN, W. See more information about FAN, W. on SCOPUS See more information about FAN, W. on IEEExplore See more information about FAN, W. on Web of Science, WANG, Y. See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,551 KB) | Citation | Downloads: 491 | Views: 394

Author keywords
artificial intelligence, machine learning, support vector machines, social computing, natural language processing

References keywords
news(24), fake(18), detection(12), approach(7), social(6), networks(6), media(6), text(5), personality(5), learning(5)
No common words between the references section and the paper title.

About this article
Date of Publication: 2024-02-29
Volume 24, Issue 1, Year 2024, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.01007
SCOPUS ID: 85189456069

Abstract
Quick view
Full text preview
Combating misinformation is one of the urgent social crises. Much research has shown that disinformation can lead to social panic and adversely affect society. It is crucial to promptly detect and counteract misinformation to reduce its adverse effects. Although progress in text-based fact verification has been made, the community needs further exploration into the user-oriented results. To address this gap, we integrate theories from linguistics, journalism, psychology, and cognitive science. We propose a disinformation detection algorithm based on multidimensional content analysis. This algorithm combines human factors and user perception in text interactive media to establish six dimensions for comprehensive content analysis. We have proposed a quantitative calculation method corresponding to six dimensions to detect misinformation. The average accuracy of the proposed model test on four datasets is 95.28%. The results show that this algorithm can effectively analyze from a multidisciplinary theoretical perspective and effectively identify misinformation in Chinese and English.


References | Cited By  «-- Click to see who has cited this paper

[1] W. Fan, Y. Wang, H. Hu, "Mimicking human verification behavior for news media credibility evaluation," Applied Sciences, 2023, 13(17): 9553.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]


[2] S. Devi, S. Kannimuthu, G. Ravikumar, et al., "Author profiling in code-mixed WhatsApp messages using stacked convolution networks and contextualized embedding based text augmentation," Neural Processing Letters 55.1 (2023): 589-614.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 12]


[3] A. Bovet, H. A. Makse, "Influence of fake news in Twitter during the 2016 US presidential election," Nature communications, vol. 10 no.1, 2019.
[CrossRef] [Web of Science Times Cited 413] [SCOPUS Times Cited 548]


[4] D. Orso, N. Federici, R. Copetti et al., "Infodemic and the spread of fake news in the COVID-19-era," European Journal of Emergency Medicine, 2020.
[CrossRef] [Web of Science Times Cited 162] [SCOPUS Times Cited 197]


[5] A. Yahyaoui, J. Rasheed, S. Alsubai, et al., "Performance comparison of deep and machine learning approaches toward COVID-19 detection," Intelligent Automation & Soft Computing, vol. 37, no. 2, 2023.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[6] A. Qaiser, S. Hina, A. Kazi, et al., "Fake news encoder classifier (FNEC) for online published news related to COVID-19 vaccines," Intelligent Automation & Soft Computing, vol. 37, no. 1, 2023.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[7] L. Wei, Z. Ruiping, C. Yixin, "Cognitive confrontation based on social media platforms in the Russia-Ukraine conflict: Reasons, characteristics and enlightenment," Journal of Intelligence, vol. 42, no. 5, 2023.
[CrossRef]


[8] C. Daniel, "The misinformation threat: A techno-governance approach for curbing the fake news of tomorrow," Digital Government: Research and Practice, 2023.
[CrossRef] [SCOPUS Times Cited 1]


[9] B. Hu, Q. Sheng, J. Cao et al., "Learn over past, evolve for future: Forecasting temporal trends for fake news detection," in Proc. ACL, Toronto, Canada, 2023.
[CrossRef]


[10] J. Gaspers, A. Kumar et al., "Temporal generalization for spoken language understanding," in Proc. NAACL, Seattle, Washington, pp. 37-44, 2022.
[CrossRef]


[11] Y. Mu, K. Bontcheva, N. Aletras, "It's about time: Rethinking evaluation on rumor detection benchmarks using chronological splits," Findings of the Association for Computational Linguistics: EACL 2023.
[CrossRef]


[12] X. Zhang, J. Cao, X. Li et al., "Mining dual emotion for fake news detection," in Proc. WWW, Ljubljana, Slovenia, pp. 3465-3476, 2021.
[CrossRef] [Web of Science Times Cited 79] [SCOPUS Times Cited 126]


[13] C. Wardle, H. Derakhshan, "Toward an interdisciplinary framework for research and policymaking," Strasbourg: Council of Europe, 2017

[14] P. Devarsh, N. D'Souza, and R. Gawande, "Automatic Twitter rumour detection using machine learning," 2022 IEEE Bombay Section Signature Conference (IBSSC). IEEE, 2022.
[CrossRef] [SCOPUS Times Cited 1]


[15] V. L. Rubin, N. J. Conroy, Y. Chen, "Towards news verification: Deception detection methods for news discourse," in Proc. HICSS, pp. 5-8, 2015.
[CrossRef]


[16] M. Potthast, J. Kiesel, K. Reinartz et al., "A stylometric inquiry into hyperpartisan and fake news," arXiv preprint, arXiv:1702.05638, 2017.
[CrossRef] [SCOPUS Times Cited 265]


[17] S. Castelo, T. Almeida, A. Elghafari et al., "A topic-agnostic approach for identifying fake news pages," in Proc. WWW, San Francisco, USA, pp. 975-980, 2019.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 62]


[18] S. Vosoughi, D. Roy, S. Aral, "The spread of true and false news online," science, vol. 359 no.6380, pp.1146-1151, 2018.
[CrossRef] [Web of Science Times Cited 3243] [SCOPUS Times Cited 4271]


[19] K. P. Kumar, G. Geethakumari, "Detecting misinformation in online social networks using cognitive psychology," Human-centric Computing and Information Sciences, vol. 4, no. 1, pp. 1-22, 2014.
[CrossRef] [Web of Science Times Cited 117] [SCOPUS Times Cited 168]


[20] X. Guo, Y. Sun, S. Vosoughi, "Emotion-based modeling of mental disorders on social media," in Proc. WI-IAT, Melbourne, Australia, pp. 8-16, 2021.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]


[21] K. Mansooreh, T. H. Nazer, H. Liu. "Profiling fake news spreaders on social media through psychological and motivational factors," Proceedings of the 32nd ACM conference on hypertext and social media. 2021, pp. 225-230.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 18]


[22] B. Sampat, S. Raj, "Fake or real news? Understanding the gratifications and personality traits of individuals sharing fake news on social media platforms," Aslib Journal of Information Management, vol.74, no.5, pp. 840-876, 2022.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 30]


[23] K. Anoop, P. Deepak, V. L. Lajish, "Emotion cognizance improves health fake news identification," in Proc. IDEAS, Incheon (Seoul), South Korea and Montreal, Canada, 2020.
[CrossRef] [SCOPUS Times Cited 6]


[24] S. Aneja, N. Aneja, B. Bhargava, and R. R. Chowdhury, "Device fingerprinting using deep convolutional neural networks," International Journal of Communication Networks and Distributed Systems, vol. 28, no. 2, pp. 171-198, 2022.
[CrossRef] [SCOPUS Times Cited 16]


[25] S. Aneja, N. Aneja, P. E. Abas, and A. G. Naim, "Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 11, no. 3, pp. 961-968, 2022.
[CrossRef] [SCOPUS Times Cited 5]


[26] S. Ryan, T. Parr, K. J. Friston. "Simulating emotions: An active inference model of emotional state inference and emotion concept learning," Frontiers in psychology, vol 10, 2019: 2844.
[CrossRef] [Web of Science Times Cited 66] [SCOPUS Times Cited 70]


[27] H. Alotaibi, "The role of lexical cohesion in writing quality," International Journal of Applied Linguistics and English Literature, vol. 4, no.1, pp. 261-269, 2015.
[CrossRef] [SCOPUS Times Cited 4]


[28] L. Qian, R. Xu, Z. Zhou, "MRDCA: A multimodal approach for fine-grained fake news detection through integration of RoBERTa and DenseNet based upon fusion mechanism of co-attention," Annals of Operations Research (2022): 1-22.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 3]


[29] N. Reimers, I. Gurevych, "Sentence-bert: Sentence embeddings using siamese bert-networks," arXiv preprint, arXiv:1908.10084, 2019.
[CrossRef]


[30] C. E. Shannon, "A mathematical theory of communication," The Bell system technical journal, vol. 27, no. 3, pp. 379-423, 1948.
[CrossRef] [SCOPUS Times Cited 33265]


[31] Z. C. Ren, Q. Shen, X. Diao, H. Xu, "A sentiment-aware deep learning approach for personality detection from text," Information Processing & Management, vol. 58, no.3, 2021.
[CrossRef] [Web of Science Times Cited 60] [SCOPUS Times Cited 77]


[32] G. J. Boyle, "Myers‐Briggs type indicator (MBTI): Some psychometric limitations," Australian Psychologist, vol. 30 no. 1, pp. 71-74, 1995.
[CrossRef] [Web of Science Times Cited 73] [SCOPUS Times Cited 103]


[33] L. Bailly, "Lacan: A beginner's guide," Simon and Schuster, 2012

[34] B. W. Lee, J. H. J. Lee, "Traditional readability formulas compared for English," arXiv preprint arXiv:2301.02975, 2023.
[CrossRef]


[35] C. Kinnvall, C. Tereza, "The psychology of extremist identification," European Psychologist, 2021.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[36] J. W. Pennebaker, M. R. Mehl, K. G. Niederhoffer, "Psychological aspects of natural language use: Our words, our selves," Annual review of psychology, vol. 54 no. 1, pp. 547-577, 2003.
[CrossRef] [Web of Science Times Cited 1442] [SCOPUS Times Cited 1787]


[37] X. Y. Zhou, Z. Reza, "Network-based fake news detection: A pattern-driven approach," ACM SIGKDD explorations newsletter, vol. 21 no. 2, pp. 48-60, 2019.
[CrossRef]


[38] H. Ahmed, T. Issa, S. Sherif, "Detecting opinion spams and fake news using text classification," Security and Privacy, vol. 1 no.1, e9, 2018.
[CrossRef] [Web of Science Times Cited 169]


[39] P. Patwa, S. Sharma, S. Pykl, V. Guptha, G. Kumari, M. S. Akhtar et al., "Fighting an infodemic: Covid-19 fake news dataset," in Combating Online Hostile Posts in Regional Languages during Emergency Situation: First International Workshop, CONSTRAINT 2021, Collocated with AAAI 2021, Virtual Event, Online, pp. 21-29, 2021.
[CrossRef] [SCOPUS Times Cited 161]


[40] Q. Nan, J. Cao, Y. Zhu et al., "MDFEND: Multi-domain fake news detection," in Proc. CIKM, Gold Coast, Queensland, Australia, 2021.
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 70]


[41] Z. Ma, M. Liu, G. Fang, Y. Shen, "LTCR: Long-text chinese rumor detection dataset," arXiv preprint arXiv:2306.07201, 2023.
[CrossRef]


[42] C. Blackledge, A. Atapour-Abarghouei, "Transforming fake news: Robust generalisable news classification using transformers, " in 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, pp: 3960-3968, 2021.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 8]


[43] J. Ayoub, X. Yang, F. Zhou, "Combat COVID-19 infodemic using explainable natural language processing models," Information Processing & Management, vol. 58, no. 4, 2021.
[CrossRef] [Web of Science Times Cited 58] [SCOPUS Times Cited 87]


[44] T. Yang, J. Deng, X. Quan, Q. Wang, "Orders are unwanted: Dynamic deep graph convolutional network for personality detection," Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 11. 2023.
[CrossRef]


[45] S. Bharadwaj, S. Sridhar, R. Choudhary and R. Srinath, "Persona Traits Identification based on Myers-Briggs Type Indicator(MBTI) - A Text Classification Approach," 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, 2018, pp. 1076-1082,
[CrossRef]


[46] M. H. Amirhosseini, H. Kazemian, "Machine learning approach to personality type prediction based on the Myers-Briggs type indicator®," Multimodal Technologies and Interaction, 2020, 4(1): 9.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 76]


[47] Y. Mehta, S. Fatehi, A. Kazameini, et al., "Bottom-up and top-down: Predicting personality with psycholinguistic and language model features," 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020: 1184-1189.
[CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 78]




References Weight

Web of Science® Citations for all references: 6,127 TCR
SCOPUS® Citations for all references: 41,531 TCR

Web of Science® Average Citations per reference: 128 ACR
SCOPUS® Average Citations per reference: 865 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-06-17 10:26 in 306 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: 


DNS Made Easy