iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s11042-023-17377-4
A comprehensive review on automatic detection of fake news on social media | Multimedia Tools and Applications Skip to main content

Advertisement

Springer Nature Link
Log in

A comprehensive review on automatic detection of fake news on social media

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Social media sites are now quite popular among internet users for sharing news and opinions. This has become possible due to the inexpensive Internet, easy availability of digital devices, and the no-cost policy to create a user account on social media platforms. People are fascinated by social media sites because they can easily connect with others to share their interests, news, and opinions. According to studies, someone who lacks credibility is more likely to spread false information in order to achieve goals of any kind, be it influencing political opinions, earning attention, or making money. The automatic detection of social media related fake news has thus emerged as a highly anticipated research area in recent years. This paper offers a comprehensive review of the automatic detection of fake news on social media platforms. It contains details of the key models or techniques related to machine/deep learning proposed (or developed) during the period of the year 2011 to the year 2022 along with the performance metrics of each model or technique. The paper discusses (a). the key challenges faced during the development of an effective and efficient fake news detection system, (b). some popular datasets for carrying out fake news detection related research, and (c). the major research gaps, and future research directions in the area of automatic fake news detection on social media.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Notes

  1. https://www.scopus.com

  2. https://www.kaggle.com/general/232077

  3. https://paperswithcode.com/dataset/liar

  4. https://www.kaggle.com/datasets/mdepak/fakenewsnet

  5. https://github.com/thiagovas/bs-detector-dataset/

  6. https://www.kaggle.com/general/35739

  7. https://github.com/BuzzFeedNews/

  8. https://github.com/ww-rm/weibo-rmdt

  9. https://www.kaggle.com/datasets/usharengaraju/pheme-dataset

  10. https://github.com/compsocial/CREDBANK-data

References

  1. Kietzmann JH, Hermkens K, McCarthy IP, Silvestre BS (2011) Social media get serious! understanding the functional building blocks of social media. Bus Horiz 54(3):241–251

    Google Scholar 

  2. Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211–36

    Google Scholar 

  3. Perrin A (2015) Social media usage. Pew Research Center 125:52–68

    Google Scholar 

  4. Howell L et al (2013) Digital wildfires in a hyperconnected world. WEF Report 3(2013):15–94

    Google Scholar 

  5. Sailunaz K, Kawash J, Alhajj R (2022) Tweet and user validation with supervised feature ranking and rumor classification. Multimed Tools Appl 81(22):31907–31927

    Google Scholar 

  6. Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newsletter 19(1):22–36

    Google Scholar 

  7. Shao C, Ciampaglia GL, Varol O, Yang K-C, Flammini A, Menczer F (2018) The spread of low-credibility content by social bots. Nat Commun 9(1):1–9

    Google Scholar 

  8. Tandoc EC Jr, Lim ZW, Ling R (2018) Defining fake news a typology of scholarly definitions. Digit Journal 6(2):137–153

    Google Scholar 

  9. Aldwairi M, Alwahedi A (2018) Detecting fake news in social media networks. Procedia Comput Sci 141:215–222

    Google Scholar 

  10. Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th international conference on world wide web pp 675–684

  11. Niklewicz K (2017) Weeding out fake news: an approach to social media regulation. European View 16(2):335–335

    Google Scholar 

  12. Pierri F, Ceri S (2019) False news on social media: a data-driven survey. ACM Sigmod Record 48(2):18–27

    Google Scholar 

  13. Fleming C, O’Carroll J (2010) The art of the hoax. Parallax 16(4):45–59

    Google Scholar 

  14. Wang C-C (2020) Fake news and related concepts: definitions and recent research development. Contemp Manag Res 16(3):145–174

    Google Scholar 

  15. Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Sci 359(6380):1146–1151

    Google Scholar 

  16. Volkova S, Shaffer K, Jang JY, Hodas N (2017) Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter. In: Proceedings of the 55th annual meeting of the association for computational linguistics, (vol 2: Short Papers), pp 647–653

  17. Chen Y, Conroy N.J, Rubin VL (2015) Misleading online content: recognizing clickbait as false news. In: Proceedings of the 2015 ACM on workshop on multimodal deception detection, pp 15–19

  18. Alkhodair SA, Ding SH, Fung BC, Liu J (2020) Detecting breaking news rumors of emerging topics in social media. Inf Process Manag 57(2):102018

    Google Scholar 

  19. Shelke S, Attar V (2022) Rumor detection in social network based on user content and lexical features. Multimed Tools Appl 81(12):17347–17368

    Google Scholar 

  20. Rubin VL, Chen Y, Conroy NK (2015) Deception detection for news: three types of fakes. Proc Assoc Inf Sci Technol 52(1):1–4

  21. Elhadad MK, Li KF, Gebali F (2019) Fake news detection on social media: a systematic survey. In: 2019 IEEE Pacific rim conference on communications computers and signal processing (PACRIM). IEEE, pp 1–8

  22. Wardle C, Derakhshan H et al (2018) Thinking about information disorder: formats of misinformation disinformation and mal-information. Ireton Cherilyn; Posetti Julie. Journalism fake news & disinformation. Paris: Unesco, pp 43–54

  23. Kshetri N, Voas J (2017) The economics of fake news. IT Professional 19(6):8–12

    Google Scholar 

  24. Rastogi S, Bansal D (2022) Disinformation detection on social media: an integrated approach. Multimed Tools Appl 81(28):40675–40707

    Google Scholar 

  25. Kucharski A (2016) Study epidemiology of fake news. Nature 540(7634):525–525

    Google Scholar 

  26. Upadhyay R, Pasi G, Viviani M (2022) Vec4cred: a model for health misinformation detection in web pages. Multimed Tools Appl pp 1–20

  27. Zhou X, Zafarani R (2020) A survey of fake news: fundamental theories detection methods and opportunities. ACM Comput Surv 53(5):1–40

    Google Scholar 

  28. Rousidis D, Koukaras P, Tjortjis C (2020) Social media prediction: a literature review. Multimed Tools Appl 79(9–10):6279–6311

    Google Scholar 

  29. Howard PN, Kollanyi B (2016) Bots# strongerin and# brexit: computational propaganda during the uk-eu referendum. Available at SSRN 2798311

  30. Rapoza K (2017) Can fake news impact the stock market. Forbes News

  31. Ferrara E, Varol O, Davis C, Menczer F, Flammini A (2016) The rise of social bots. Commun ACM 59(7):96–104

    Google Scholar 

  32. How fake news in the middle east is a powder keg waiting to blow. Independent

  33. Spohr D (2017) Fake news and ideological polarization: filter bubbles and selective exposure on social media. Bus Inf Rev 34(3):150–160

    Google Scholar 

  34. Shayan Sardarizadeh OR (2022) Ukraine invasion: false claims the war is a hoax go viral. BBC Monitoring

  35. Salve P (2020) Manipulative fake news on the rise in india under lockdown: study. indiaspend.com

  36. PTI (2022) Govt orders blocking of 18 indian four pakistani youtube-based news channels. timesofindia.indiatimes.com

  37. Jin Z, Cao J, Zhang Y, Zhou J, Tian Q (2016) Novel visual and statistical image features for microblogs news verification. IEEE Trans Multimedia 19(3):598–608

    Google Scholar 

  38. Kwon S, Cha M, Jung K, Chen W, Wang Y (2013) Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th international conference on data mining. IEEE, pp 1103–1108

  39. Ma J, Gao W, Wei Z, Lu Y, Wong K-F (2015) Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pp 1751–1754

  40. Ruchansky N, Seo S, Liu Y (2017) Csi: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 797–806

  41. Jin Z, Cao J, Zhang Y, Luo J (2016) News verification by exploiting conflicting social viewpoints in microblogs. In: Proceedings of the AAAI conference on artificial intelligence, vol 30

  42. Assiroj P, Hidayanto AN, Prabowo H, Warnars HLHS et al (2018) Hoax news detection on social media: a survey. In: 2018 Indonesian association for pattern recognition international conference (INAPR). IEEE, pp 186–191

  43. Vlachos A, Riedel S (2014) Fact checking: task definition and dataset construction. In: Proceedings of the ACL 2014 workshop on language technologies and computational social science, pp 18–22

  44. Cohen S, Hamilton JT, Turner F (2011) Computational journalism. Commun ACM 54(10):66–71

    Google Scholar 

  45. Rubin VL, Lukoianova T (2015) Truth and deception at the rhetorical structure level. J Assoc Inf Sci Technol 66(5):905–917

    Google Scholar 

  46. Atanasova P, Nakov P, Márquez L, Barrón-Cedeño A, Karadzhov G, Mihaylova T, Mohtarami M, Glass J (2019) Automatic fact-checking using context and discourse information. J Data Inf Qual 11(3):1–27

    Google Scholar 

  47. Mohammad SM, Sobhani P, Kiritchenko S (2017) Stance and sentiment in tweets. ACM Trans Internet Technol 17(3):1–23

    Google Scholar 

  48. Moher D, Liberati A, Tetzlaff J, Altman DG, Altman D, Antes G, Atkins D, Barbour V, Barrowman N, Berlin JA et al (2009) Preferred reporting items for systematic reviews and meta-analyses: the prisma statement (chinese edition). J Chin Integr Med 7(9):889–896

  49. Özgöbek Ö Gulla JA (2017) Towards an understanding of fake news. In: CEUR workshop proceedings, vol 354

  50. Meinert J, Mirbabaie M, Dungs S, Aker A (2018) Is it really fake–towards an understanding of fake news in social media communication. In: International conference on social computing and social media. Springer, pp 484–497

  51. Kumar S, Shah N (2018) False information on web and social media: a survey. arXiv:1804.08559

  52. Parikh S.B, Atrey PK (2018) Media-rich fake news detection: a survey. In: 2018 IEEE conference on multimedia information processing and retrieval (MIPR). IEEE, pp 436–441

  53. Sharma K, Qian F, Jiang H, Ruchansky N, Zhang M, Liu Y (2019) Combating fake news: a survey on identification and mitigation techniques. ACM Trans Intell Syst Technol 10(3):1–42

    Google Scholar 

  54. Cardoso Durier da Silva F, Vieira R, Garcia AC (2019) Can machines learn to detect fake news a survey focused on social media. In: Proceedings of the 52nd Hawaii international conference on system sciences

  55. Zhang X, Ghorbani AA (2020) An overview of online fake news: characterization detection and discussion. Inf Process Manage 57(2):102025

  56. Mridha MF, Keya AJ, Hamid MA, Monowar MM, Rahman MS (2021) A comprehensive review on fake news detection with deep learning. IEEE Access 9:156151–156170

    Google Scholar 

  57. Bani-Hani A, Adedugbe O, Benkhelifa E, Majdalawieh M (2022) Fandet semantic model: an owl ontology for context-based fake news detection on social media. Combating fake news with computational intelligence techniques, pp 91–125

  58. Rubin VL, Conroy NJ, Chen Y (2015) Towards news verification: deception detection methods for news discourse. In: Hawaii international conference on system sciences, pp 5–8

  59. Ferreira W, Vlachos A (2016) Emergent: a novel data-set for stance classification. In: Proceedings of the 2016 conference of the north American chapter of the association for computational linguistics: human language technologies. ACL

  60. Magnini B, Zanoli R, Dagan I, Eichler K, Neumann G, Noh T-G, Pado S, Stern A, Levy O (2014) The excitement open platform for textual inferences. In: Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, pp 43–48

  61. Jin Z, Cao J, Jiang Y-G, Zhang Y (2014) News credibility evaluation on microblog with a hierarchical propagation model. In: 2014 IEEE international conference on data mining. IEEE, pp 230–239

  62. Janze C, Risius M (2017) Automatic detection of fake news on social media platforms

  63. Wang WY (2017) liar liar pants on fire: a new benchmark dataset for fake news detection. In: Proceedings of the 55th annual meeting of the association for computational linguistics. Association for Computational Linguistics, (vol 2: Short Papers), pp 422–426. https://doi.org/10.18653/v1/P17-2067. https://aclanthology.org/P17-2067

  64. Buntain C, Golbeck J (2017) Automatically identifying fake news in popular twitter threads. In: 2017 IEEE international conference on smart cloud (SmartCloud). IEEE, pp 208–215

  65. Castillo C, Mendoza M, Poblete B (2013) Predicting information credibility in time-sensitive social media. Internet Res

  66. Kotteti CMM, Dong X, Li N, Qian L (2018) Fake news detection enhancement with data imputation. In: 2018 IEEE 16th intl conf on dependable autonomic and secure computing 16th intl conf on pervasive intelligence and computing 4th intl conf on big data intelligence and computing and cyber science and technology congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, pp 187–192

  67. Pan JZ, Pavlova S, Li C, Li N, Li Y, Liu J (2018) Content based fake news detection using knowledge graphs. In: International semantic web conference. Springer, pp 669–683

  68. Della Vedova ML, Tacchini E, Moret S, Ballarin G, DiPierro M, de Alfaro L (2018) Automatic online fake news detection combining content and social signals. In: 2018 22nd conference of open innovations association (FRUCT). IEEE, pp 272–279

  69. Atodiresei C-S, Tănăselea A, Iftene A (2018) Identifying fake news and fake users on twitter. Procedia Comput Sci 126:451–461

    Google Scholar 

  70. Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K, Su L, Gao J (2018) Eann: event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th Acm Sigkdd international conference on knowledge discovery & data mining, pp 849–857

  71. Ajao O, Bhowmik D, Zargari S (2018) Fake news identification on twitter with hybrid cnn and rnn models. In: Proceedings of the 9th international conference on social media and society, pp 226–230

  72. Karimi H, Roy P, Saba-Sadiya S, Tang J (2018) Multi-source multi-class fake news detection. In: Proceedings of the 27th international conference on computational linguistics, pp 1546–1557

  73. Wu L, Liu H (2018) Tracing fake-news footprints: characterizing social media messages by how they propagate. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 637–645

  74. Gupta S, Thirukovalluru R, Sinha M, Mannarswamy S (2018) Cimtdetect: a community infused matrix-tensor coupled factorization based method for fake news detection. In: 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 278–281

  75. Dong M, Yao L, Wang X, Benatallah B, Sheng QZ, Huang H (2018) Dual: A deep unified attention model with latent relation representations for fake news detection. In: International conference on web information systems engineering. Springer, pp 199–209

  76. Guacho GB, Abdali S, Shah N, Papalexakis EE (2018) Semi-supervised content-based detection of misinformation via tensor embeddings. In: 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 322–325

  77. Helmstetter S, Paulheim H (2018) Weakly supervised learning for fake news detection on twitter. In: 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 274–277

  78. Liu Y, Wu Y-F (2018) Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

  79. Shu K, Zhou X, Wang S, Zafarani R, Liu H (2019) The role of user profiles for fake news detection. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, pp 436–439

  80. Reis JC, Correia A, Murai F, Veloso A, Benevenuto F (2019) Supervised learning for fake news detection. IEEE Intell Syst 34(2):76–81

    Google Scholar 

  81. Olivieri A, Shabani S, Sokhn M, Cudré-Mauroux P (2019) Creating task-generic features for fake news detection. In: Proceedings of the 52nd Hawaii international conference on system sciences

  82. Bharadwaj P, Shao Z (2019) Fake news detection with semantic features and text mining. Int J Nat Lang Comput 8

  83. Rasool T, Butt WH, Shaukat A, Akram MU (2019) Multi-label fake news detection using multi-layered supervised learning. In: Proceedings of the 2019 11th international conference on computer and automation engineering, pp 73–77

  84. Traylor T, Straub J, Snell N et al (2019) Classifying fake news articles using natural language processing to identify in-article attribution as a supervised learning estimator. In: 2019 IEEE 13th international conference on semantic computing (ICSC). IEEE, pp 445–449

  85. Shu K, Mahudeswaran D, Liu H (2019) Fakenewstracker: a tool for fake news collection detection and visualization. Comput Math Organ Theory 25(1):60–71

    Google Scholar 

  86. Yang S, Shu K, Wang S, Gu R, Wu F, Liu H (2019) Unsupervised fake news detection on social media: a generative approach. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 5644–5651

  87. Kesarwani A, Chauhan SS, Nair AR (2020) Fake news detection on social media using k-nearest neighbor classifier. In: 2020 International conference on advances in computing and communication engineering (ICACCE). IEEE, pp 1–4

  88. Shu K, Mahudeswaran D, Wang S, Liu H (2020) Hierarchical propagation networks for fake news detection: investigation and exploitation. In: Proceedings of the international AAAI conference on web and social media, vol 14, pp 626–637

  89. Zhou X, Jain A, Phoha VV, Zafarani R (2020) Fake news early detection: a theory-driven model. Digit Threat Res Pract 1(2):1–25

    Google Scholar 

  90. Lu Y-J, Li C-T (2020) Gcan: graph-aware co-attention networks for explainable fake news detection on social media. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 505–514. https://doi.org/10.18653/v1/2020.acl-main.48

  91. Ni S, Li J, Kao H-Y (2021) Mvan: multi-view attention networks for fake news detection on social media. IEEE Access 9:106907–106917

    Google Scholar 

  92. Sahoo SR, Gupta BB (2021) Multiple features based approach for automatic fake news detection on social networks using deep learning. Appl Soft Comput 100:106983

  93. Kaliyar RK, Goswami A, Narang P (2021) Fakebert: fake news detection in social media with a bert-based deep learning approach. Multimed Tools Appl 80(8):11765–11788

    Google Scholar 

  94. Dou Y, Shu K, Xia C, Yu PS, Sun L (2021) User preference-aware fake news detection. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 2051–2055

  95. Verma PK, Agrawal P, Amorim I, Prodan R (2021) Welfake: word embedding over linguistic features for fake news detection. IEEE Trans Comput Soc Syst 8(4):881–893

    Google Scholar 

  96. Ozbay FA, Alatas B (2021) Adaptive salp swarm optimization algorithms with inertia weights for novel fake news detection model in online social media. Multimed Tools Appl 80(26–27):34333–34357

    Google Scholar 

  97. Nasir JA, Khan OS, Varlamis I (2021) Fake news detection: a hybrid cnn-rnn based deep learning approach. Int J Inf Manag Data Insights 1(1):100007

  98. Iwendi C, Mohan S, Ibeke E, Ahmadian A, Ciano T et al (2022) Covid-19 fake news sentiment analysis. Comput Electr Eng 101:107967

    Google Scholar 

  99. Seddari N, Derhab A, Belaoued M, Halboob W, Al-Muhtadi J, Bouras A (2022) A hybrid linguistic and knowledge-based analysis approach for fake news detection on social media. IEEE Access

  100. Min E, Rong Y, Bian Y, Xu T, Zhao P, Huang J, Ananiadou S (2022) Divide-and-conquer: post-user interaction network for fake news detection on social media. In: Proceedings of the acm web conference 2022, pp 1148–1158

  101. Galli A, Masciari E, Moscato V, Sperlí G (2022) A comprehensive benchmark for fake news detection. J Intell Inf Syst 59(1):237–261

    Google Scholar 

  102. Sadeghi F, Bidgoly AJ, Amirkhani H (2022) Fake news detection on social media using a natural language inference approach. Multimed Tools Appl 81(23):33801–33821

    Google Scholar 

  103. Chi H, Liao B (2022) A quantitative argumentation-based automated explainable decision system for fake news detection on social media. Knowl-Based Syst 242:108378

    Google Scholar 

  104. Choudhury D, Acharjee T (2022) A novel approach to fake news detection in social networks using genetic algorithm applying machine learning classifiers. Multimed Tools Appl 1–17

  105. Shao C, Hui P-M, Wang L, Jiang X, Flammini A, Menczer F, Ciampaglia GL (2018) Anatomy of an online misinformation network. Plos One 13(4):0196087

    Google Scholar 

  106. Pérez-Rosas V, Kleinberg B, Lefevre A, Mihalcea R (2018) Automatic detection of fake news. In: Proceedings of the 27th international conference on computational linguistics. Association for Computational Linguistics, pp 3391–3401. https://aclanthology.org/C18-1287

  107. Singh MK, Ahmed J, Raghuvanshi KK, Alam MA (2023) Bharatfakenewskosh: a data repository for fake news research in India. In: International conference on smart computing and communication. Springer, pp 277–288

  108. Al-Rubaie A, Al-Jadir L, Al-Hashimi M (2019) Detecting arabic fake news using machine learning techniques. In: Proceedings of the 2019 3rd international conference on computer science and artificial intelligence (CSAI), pp 201–206

  109. Beauvais C (2022) Fake news: why do we believe it. Joint Bone Spine 89(4):105371

  110. Djenouri Y, Belhadi A, Srivastava G, Lin JC-W (2023) Advanced pattern-mining system for fake news analysis. IEEE Trans Comput Soc Syst

  111. Raponi S, Khalifa Z, Oligeri G, Di Pietro R (2022) Fake news propagation: a review of epidemic models datasets and insights. ACM Trans Web 16(3):1–34

    Google Scholar 

  112. Ahmed U, Lin JC-W, Diaz VG (2022) Automatically temporal labeled data generation using positional lexicon expansion for focus time estimation of news articles. ACM Trans Asian Low-Resour Lang Inf Process

  113. Ahmed U, Lin JC-W, Srivastava G, Yun U (2023) Semi-supervised lexicon-aware embedding for news article time estimation. ACM Trans Asian Low-Resour Lang Inf Process

  114. Raza S, Ding C (2022) Fake news detection based on news content and social contexts: a transformer-based approach. Int J Data Sci Anal 13(4):335–362

    Google Scholar 

Download references

Funding

The authors received no specific funding for this work.

Author information

Authors and Affiliations

  1. Department of Computer Science Engineering, School of Engineering Science & Technology, Jamia Hamdard University, New Delhi, 110062, India

    Manish Kumar Singh, Jawed Ahmed & Mohammad Afshar Alam

  2. Department of Computer Science, Ramanujan College, University of Delhi, Kalkaji, New Delhi, 110019, Delhi, India

    Kamlesh Kumar Raghuvanshi

  3. Cluster Innovation Center, University of Delhi, GC Narang Road, New Delhi, 110007, Delhi, India

    Sachin Kumar

Authors
  1. Manish Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jawed Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Afshar Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kamlesh Kumar Raghuvanshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sachin Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manish Kumar Singh.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, M.K., Ahmed, J., Alam, M.A. et al. A comprehensive review on automatic detection of fake news on social media. Multimed Tools Appl 83, 47319–47352 (2024). https://doi.org/10.1007/s11042-023-17377-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17377-4

Keywords

Search

Navigation