Ensembles to Detect Fake News – An Approach Based on Specialized Classifiers
Authors: 
Patrick Antunes dos Santos, Paulo Márcio Souza Freire, Ronaldo Ribeiro GoldschmidtAuthors Info & Claims
Pages 154 - 158
Abstract
The growing spread of Fake News is a consequence of the popularization of digital media as tools that facilitate the propagation and consumption of information. Ensemble-based approaches to detect this harmful type of news have shown promising performance. Despite their ability to combine machine learning classifiers to achieve more robust results, so far, these approaches have been limited to traditional classifiers (i.e., classifiers that can be applied to problems other than Fake News detection, such as decision trees, K-NN, SVM, etc...). Hence, the present work proposes Ensembles that use, in addition to the traditional ones, classifiers specifically designed to detect Fake News. Preliminary experiments with two datasets showed evidence that the proposed Ensembles have the potential to overcome those that exclusively use traditional classifiers.
References
[1]
Herley Shaori Al-Ash, Mutia Fadhila Putri, Petrus Mursanto, and Alhadi Bustamam. 2019. Ensemble Learning Approach on Indonesian Fake News Classification. ICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences: Accelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings (2019). https://doi.org/10.1109/ICICoS48119.2019.8982409
[2]
Ranojoy Barua, Rajdeep Maity, Dipankar Minj, Tarang Barua, and Ashish Kumar Layek. 2019. F-NAD: An Application for Fake News Article Detection using Machine Learning Techniques. 2019 IEEE Bombay Section Signature Conference, IBSSC 2019 2019Januar (2019), 0–5. https://doi.org/10.1109/IBSSC47189.2019.8973059
[3]
Nicolo Bonettini, Edoardo Daniele Cannas, Sara Mandelli, Luca Bondi, Paolo Bestagini, and Stefano Tubaro. 2021. Video Face Manipulation Detection Through Ensemble of CNNs. (2021), 5012–5019. https://doi.org/10.1109/icpr48806.2021.9412711
[4]
Mohamed K. Elhadad, Kin Fun Li, and Fayez Gebali. 2020. Detecting Misleading Information on COVID-19. IEEE Access 8 (2020), 165201–165215. https://doi.org/10.1109/access.2020.3022867 arxiv:D7
[5]
Paulo Freire and Ronaldo Goldschmidt. 2019. Uma introdução ao combate automático às fake news em redes sociais virtuais. In Tópicos de Gerenciamento de Dados e Informação. SBC, Fortaleza, CE, Brazil, 38–67. https://doi.org/10.5753/sbc.6251.1
[6]
Paulo Freire and Ronaldo Goldschmidt. 2022. Fake News Detection Based on Explicit and Implicit Signals of a Hybrid Crowd: Proposal, Impacts and Perspectives. In Anais Estendidos do XXVIII Simpósio Brasileiro de Sistemas Multimídia e Web (Curitiba). SBC, Porto Alegre, RS, Brasil, 27–30. https://doi.org/10.5753/webmedia_estendido.2022.224573
[7]
Parvathy Ganesh, Lekshmi Priya, and R Nandakumar. 2021. Fake News Detection - A Comparative Study of Advanced Ensemble Approaches. (2021), 1003–1008. https://doi.org/10.1109/icoei51242.2021.9453061
[8]
Emmanuel Goldschmidt, Ronaldo e Passos. 2015. Data mining: Conceitos, técnias, algoritmos, orientações e aplicações. Gulf Professional Publishing.
[9]
Yin Fu Huang and Po Hong Chen. 2020. Fake news detection using an ensemble learning model based on Self-Adaptive Harmony Search algorithms. Expert Systems with Applications 159 (2020), 113584. https://doi.org/10.1016/j.eswa.2020.113584 arxiv:D3
[10]
Rohit Kumar Kaliyar, Anurag Goswami, and Pratik Narang. 2019. Multiclass Fake News Detection using Ensemble Machine Learning. Proceedings of the 2019 IEEE 9th International Conference on Advanced Computing, IACC 2019 (2019), 103–107. https://doi.org/10.1109/IACC48062.2019.8971579
[11]
Sawinder Kaur, Parteek Kumar, and Ponnurangam Kumaraguru. 2020. Automating fake news detection system using multi-level voting model. Soft Computing 24, 12 (2020), 9049–9069. https://doi.org/10.1007/s00500-019-04436-y arxiv:D19
[12]
Mohammad Zubair Khan and Omar Hussain Alhazmi. 2020. Study and analysis of unreliable news based on content acquired using ensemble learning (prevalence of fake news on social media). International Journal of Systems Assurance Engineering and Management 11, s2 (2020), 145–153. https://doi.org/10.1007/s13198-020-01016-4 arxiv:D20
[13]
Sachin Kumar, Rohan Asthana, Shashwat Upadhyay, Nidhi Upreti, and Mohammad Akbar. 2020. Fake news detection using deep learning models: A novel approach. Transactions on Emerging Telecommunications Technologies 31, 2 (2020), 1–23. https://doi.org/10.1002/ett.3767 arxiv:D13
[14]
John Joy Kurian, Deborah Zenobia Rachael Menezes, Avinash Ronanki, Gaurang Sharma, Sandeep Krishna Prasad, Ashish Chouhan, and Ajinkya Prabhune. 2021. EnFVe: An Ensemble Fact Verification Pipeline. (2021), 80–89. https://doi.org/10.1109/wiiat50758.2020.00016
[15]
Songqian Li, Kun Ma, Xuewei Niu, Yufeng Wang, Ke Ji, Ziqiang Yu, and Zhenxiang Chen. 2019. Stacking-based ensemble learning on low dimensional features for fake news detection. Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 (2019), 2730–2735. https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00383 arxiv:D2
[16]
Ana Carolina Lorena, João Gama, and Katti Faceli. 2011. Inteligência Artificial: uma abordagem de aprendizado de máquina. Grupo Gen - LTC.
[17]
Atik Mahabub. 2020. A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers. SN Applied Sciences 2 (2020), 1–9. Issue 4. https://doi.org/10.1007/s42452-020-2326-y
[18]
Igor Maffei Libonati Maia, Marcelo Pereira de Souza, Flávio Roberto Matias da Silva, Paulo Márcio Souza Freire, and Ronaldo Ribeiro Goldschmidt. 2021. A Sentiment-Based Multimodal Method to Detect Fake News. In Proceedings of the Brazilian Symposium on Multimedia and the Web (Belo Horizonte, Minas Gerais, Brazil) (WebMedia ’21). Association for Computing Machinery, New York, NY, USA, 213–216. https://doi.org/10.1145/3470482.3479467
[19]
Priyanka Meel, Puneet Chawla, Sahil Jain, and Utkarsh Rai. 2020. Web Text Content Credibility Analysis using Max Voting and Stacking Ensemble Classifiers. Proceedings - 2020 Advanced Computing and Communication Technologies for High Performance Applications, ACCTHPA 2020 (2020), 157–161. https://doi.org/10.1109/ACCTHPA49271.2020.9213234 arxiv:D21
[20]
Palagati Bhanu Prakash, Mandi Pavan Kumar, Ganjikunta VenkataManaswini, and K. M. Mehata. 2019. Fake data analysis and detection using ensembled hybrid algorithm. Proceedings of the 3rd International Conference on Computing Methodologies and Communication, ICCMC 2019Iccmc (2019), 890–897. https://doi.org/10.1109/ICCMC.2019.8819741 arxiv:D6
[21]
Harita Reddy, Namratha Raj, Manali Gala, and Annappa Basava. 2020. Text-mining-based Fake News Detection Using Ensemble Methods. International Journal of Automation and Computing 17, 2 (2020), 210–221. https://doi.org/10.1007/s11633-019-1216-5 arxiv:D16
[22]
Saarthak Sangamnerkar, R. Srinivasan, M. R. Christhuraj, and Rajeev Sukumaran. 2020. An ensemble technique to detect fabricated news article using machine learning and natural language processing techniques. 2020 International Conference for Emerging Technology, INCET 2020June (2020). https://doi.org/10.1109/INCET49848.2020.9154053 arxiv:D8
[23]
Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake News Detection on Social Media: A Data Mining Perspective. SIGKDD Explor. Newsl. 19, 1 (Sept. 2017), 22–36. https://doi.org/10.1145/3137597.3137600
[24]
W Z R W Shv, R I Xvhuv, Qdpho Dqg, Xvlqj Vnohduq, L Q Wkrq, and Phdvxuhv Vxfk. 2021. $ QDO VLV RI (QVHPEOH / HDUQLQJ 0RGHOV IRU, GHQWLI LQJ 6SDP RYHU 6RFLDO 1HWZRUNV XVLQJ. (2021), 713–718.
[25]
Paulo Márcio Souza Freire, Flávio Roberto Matias da Silva, and Ronaldo Ribeiro Goldschmidt. 2021. Fake news detection based on explicit and implicit signals of a hybrid crowd: An approach inspired in meta-learning. Expert Systems with Applications 183 (2021), 115414. https://doi.org/10.1016/j.eswa.2021.115414
[26]
Ting Su, Craig Macdonald, and Iadh Ounis. 2019. Ensembles of recurrent networks for classifying the relationship of fake news titles. SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information RetrievalJune (2019), 893–896. https://doi.org/10.1145/3331184.3331305 arxiv:D1
[27]
Gustavo Testoni, Marcelo Souza, Paulo Márcio Freire, and Ronaldo Goldschimidt. 2021. Um Método Linguístico que combina Polaridade, Emoção e Aspectos Gramaticais para Detecção de Fake News em Inglês. In Anais do X Brazilian Workshop on Social Network Analysis and Mining (Evento Online). SBC, Porto Alegre, RS, Brasil, 151–162. https://doi.org/10.5753/brasnam.2021.16133
[28]
Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science 359, 6380 (2018), 1146–1151. https://doi.org/10.1126/science.aap9559 arXiv:https://www.science.org/doi/pdf/10.1126/science.aap9559
[29]
Patrick Wang, Rafael Angarita, and Ilaria Renna. 2018. Is This the Era of Misinformation Yet: Combining Social Bots and Fake News to Deceive the Masses. In Companion Proceedings of the The Web Con 2018 (Lyon, France) (WWW ’18). International World Wide Web Con Steering Committee, Republic and Canton of Geneva, Switzerland, 1557–1561. https://doi.org/10.1145/3184558.3191610
Index Terms
- Ensembles to Detect Fake News – An Approach Based on Specialized Classifiers
Recommendations
Constructing a multi-class classifier using one-against-one approach with different binary classifiers
For the one-against-one approach, all the binary classifiers that form a one-against-one classifier should be sufficiently competent. If some of the classifiers are not competent, the consequences might be invalid classification results. To address the ...
Ensembles of Region Based Classifiers
CIT '07: Proceedings of the 7th IEEE International Conference on Computer and Information TechnologyIn machine learning, ensemble classifiers have been introduced for more accurate pattern classification than single classifiers. We propose a new ensemble learning method that employs a set of region based classifiers. Since the distribution of data can ...
Examining the Robustness of an Ensemble Learning Model for Credibility Based Fake News Detection
Image Analysis and Processing - ICIAP 2023 WorkshopsAbstractEnsemble learning is a technique of combining multiple base machine learning models and using the blended results as the final classification output. Such models provide a unique perspective on the classification results as it produces a more ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2023
285 pages
ISBN:9798400709081
DOI:10.1145/3617023
Copyright © 2023 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 the author(s) 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].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 October 2023
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Conference
WebMedia '23
WebMedia '23: Brazilian Symposium on Multimedia and the Web
October 23 - 27, 2023
Ribeirão Preto, Brazil
Acceptance Rates
Overall Acceptance Rate 270 of 873 submissions, 31%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 36Total Downloads
- Downloads (Last 12 months)27
- Downloads (Last 6 weeks)2
Reflects downloads up to 25 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format