Abstract
Online Social Network (OSN) is one of the biggest platforms that spread real and fake news. Many OSN users spread malicious data, fake news, and hoaxes using fake or social bot account for business, political and entertainment purposes. These accounts are also used to spread malicious URLs, viruses and malware. This paper proposes UCred (User Credibility) model to classify user accounts as fake or real. This model uses the combined results of RoBERT (Robustly optimized BERT), Bi-LSTM (Bidirectional LSTM) and RF (Random Forest) for the classification of profile. The output generated from all three techniques is fed into the voting classifier to improve the classification accuracy compared to state-of-the-art approaches. The proposed UCred model gives 98.96% accuracy, notably higher than the state-of-the-art model.
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References
Alowibdi JS, Buy UA, Yu PS, Ghani S, Mokbel M (2015) Deception detection in twitter. Soc Netw Anal Min 5(32):1–16
Cheng Y, Chen ZF (2020) The influence of presumed fake news influence: examining public support for corporate corrective response, media literacy interventions, and governmental regulation. Mass Commun Soc 23(5):705–729
Ciampaglia GL, Shiralkar P, Rocha LM, Bollen J, Menczer F, Flammini A (2015) Computational fact checking from knowledge networks. PLOS ONE 10(6):1–13, 06
Gravanis G, Vakali A, Diamantaras K, Karadais P (2019) Behind the cues: a benchmarking study for fake news detection. Expert Syst Appl 128:201–213
Liu Y, Mu Y, Chen K, Li Y, Guo J (2020) Daily activity feature selection in smart homes based on Pearson correlation coefficient. Neural Process Lett 51:1771–1787
Nickel M, Murphy K, Tresp V, Gabrilovich E (2016) A review of relational machine learning for knowledge graphs. Proc IEEE 104(1):11–33
Ruan X, Wu Z, Wang H, Jajodia S (2016) Profiling online social behaviors for compromised account detection. IEEE Trans Inf Forens Secur 11:176–187
Rubin VL, Lukoianova T (2015) Truth and deception at the rhetorical structure level. J Assoc Inf Sci Technol 66(5):905–917
Shi B, Weninger T (2016) Discriminative predicate path mining for fact checking in knowledge graphs. Knowl-Based Syst 104:123–133
Wanda P, Jie HJ (2020) Deepprofile: finding fake profile in online social network using dynamic CNN. J Inf Secur Appl 52:1–13
Wardle C, Derakhshan H (2017) Information disorder: toward an interdisciplinary framework for research and policy making. Council Europe Rep 27:1–109
Zhou X, Zafarani R (2020) A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput Surv 53(5):1–40
Akyon FC, Esat Kalfaoglu M (2019) Instagram fake and automated account detection. In: Innovations in intelligent systems and applications conference (ASYU). Izmir, Turkey, IEEE, pp 1–7
Al-Qurishi M, Al-Rakhami M, Alamri A, Alrubaian M, Rahman SMM, Hossain MS (2017) Sybil defense techniques in online social networks: a survey. IEEE Access, pp 1200–1219
Dickerson JP, Kagan V, Subrahmanian VS (2014) Using sentiment to detect bots on twitter: are humans more opinionated than bots? Advances in Social Networks Analysis and Mining. Beijing, China, IEEE/ACM, pp 620–627
Ersahin B, Aktas O, Kilinc D, Akyol C (2017) Twitter fake account detection. In: International conference on computer science and engineering (UBMK). IEEE, Antalya, Turkey, pp 388–392
Facebook may have over 100 million fake accounts globally (2014) https://www.gadgetsnow.com/tech-news/Facebook-may-have-over-100-million-fake-accounts-globally/articleshow/34672084.cms. Accessed 6 Oct 2020
Facebook Principles (2015) Facebook profile: terms of service. https://www.facebook.com/legal/terms. Accessed 6 Oct 2020
Facebook, twitter remove russia-backed fake account network (2020) https://www.gadgetsnow.com/tech-news/facebook-twitter-remove-russia-backed-fake-account-network/articleshow/74626530.cms. Accessed 6 Oct 2020
Liu Y, Wu YB (2018) Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the thirty-second AAAI conference on artificial intelligence. AAAI Press, New Orleans, Louisiana, USA, pp 354–361
Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach, pp 1–13
Mateen M, Iqbal MA, Aleem M, Islam MA (2017) A hybrid approach for spam detection for twitter. In: 14th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, Islamabad, Pakistan, pp 466–471
Monti F, Frasca F, Eynard D, Mannion D, Bronstein MM (2019) Fake news detection on social media using geometric deep learning, pp 1–15
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, Santa Fe, New Mexico, USA, pp 3391–3401
Pew Research Center (2021) Social media fact sheet. https://www.pewresearch.org/internet/fact-sheet/social-media/. Accessed 27 Sept 2020
Potthast M, Kiesel J, Reinartz K, Bevendorff J, Stein B (2018) A stylometric inquiry into hyperpartisan and fake news. In: Proceedings of the 56th annual meeting of the association for computational linguistics (vol 1: Long Papers). Melbourne, Australia: Association for Computational Linguistics, pp 231–240
Sanh V, Debut L, Chaumond J, Wolf T (2019) Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, pp 1–5
Taylor K (2016) A viral rumor that mcdonald’s uses ground worm filler in burgers has been debunked. https://www.businessinsider.in/A-viral-rumor-that-McDonalds-uses-ground-worm-filler-in-burgers-has-been-debunked/articleshow/50676282.cms. Accessed 6 Oct 2020
Tchakounté F, Amadou Calvin K, Ari AAA, Fotsa Mbogne DJ (2020) A smart contract logic to reduce hoax propagation across social media. J. King Saudi Univ - Comput Inf Sci, pp 1–9
Verma PK, Agrawal P (2020) Study and detection of fake news: \({P}^2{C}^2\)-based machine learning approach. In: 4th International conference on data management, analytics and innovation. Springer, Delhi, pp 261–278
Walt EVD, Eloff J (2018) Using machine learning to detect fake identities: Bots vs humans. IEEE Access, pp 6540–6549
Wang AH (2010) Detecting spam bots in online social networking sites: a machine learning approach. In: Data and applications security and privacy XXIV. Springer, Berlin, pp 335–342
Wani MA, Jabin S (2018) Mutual clustering coefficient-based suspicious-link detection approach for online social networks. J King Saudi Univ - Comput Inf Sci, pp 1–14
Wu K, Yang S, Zhu KQ (2015) False rumors detection on sina weibo by propagation structures. In: 2015 IEEE 31st international conference on data engineering, pp 651–662
Zhou Y, Chen K, Song L, Yang X, He J (2012) Feature analysis of spammers in social networks with active honeypots: a case study of chinese microblogging networks. In: International conference on advances in social networks analysis and mining. IEEE/ACM, Istanbul, Turkey, pp 728–729
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Verma, P.K., Agrawal, P., Madaan, V. et al. UCred: fusion of machine learning and deep learning methods for user credibility on social media. Soc. Netw. Anal. Min. 12, 54 (2022). https://doi.org/10.1007/s13278-022-00880-1
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DOI: https://doi.org/10.1007/s13278-022-00880-1