Abstract
Obtaining news from social media platforms has become increasingly popular due to their ease of access and high speed of information dissemination. These same factors have, however, also increased the range and speed at which misinformation and fake news spread. While machine-run accounts (bots) contribute significantly to the spread of misinformation, human users on these platforms also play a key role in contributing to the spread. Thus, there is a need for an in-depth understanding of the relationship between users and the spread of fake news. This paper proposes a new data-driven metric called User Impact Factor (UIF) aims to show the importance of user content analysis and neighbourhood influence to profile a fake news spreader on Twitter. Tweets and retweets of each user are collected and classified as ‘fake’ or ‘not fake’ using Natural Language Processing (NLP). These labeled posts are combined with data on the number of the user’s followers and retweet potential in order to generate the user’s impact factor. Experiments are performed using data collected from Twitter and the results show the effectiveness of the proposed approach in identifying fake news spreaders.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Afrasiabi Rad, A., Benyoucef, M.: Towards detecting influential users in social networks. In: Babin, G., Stanoevska-Slabeva, K., Kropf, P. (eds.) MCETECH 2011. LNBIP, vol. 78, pp. 227–240. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20862-1_16
de Barcelos, T., Muniz, L.N., Dantas, D.M., Cotrim Junior, D.F., Cavalcante, J.R., Faerstein, E.: Analysis of fake news disseminated during the covid-19 pandemic in brazilanálisis de las noticias falsas divulgadas durante la pandemia de covid-19 en brasil. Revista Panamericana de Salud Publica= Pan Am. J. Public Health 45, e65–e65 (2021)
Bodaghi, A., Oliveira, J.: The theater of fake news spreading, who plays which role? A study on real graphs of spreading on Twitter. Expert Syst. Appl. 189, 116110 (2022)
Buskens, V.: Spreading information and developing trust in social networks to accelerate diffusion of innovations. Trends Food Sci. Technol. 106, 485–488 (2020)
Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G., Previti, M.: Mutual influence of users credibility and news spreading in online social networks. Future Internet 13(5), 107 (2021)
Center PR: News use across social media platforms in 2020 (2021). https://www.pewresearch.org/journalism/2021/01/12/news-use-across-social-media-platforms-in-2020/
Courier-Tribune: Religious zeal drives N.C. man in ‘pizzagate’ (2016). https://www.courier-tribune.com/story/news/state/2016/12/06/religious-zeal-drives-nc-man-in-8216pizzagate8217/24407347007/
Data, Society: Why influence matters in the spread of misinformation (2018). https://points.datasociety.net/why-influence-matters-in-the-spread-of-misinformation-fc99ee69040e
Doerr, B., Fouz, M., Friedrich, T.: Why rumors spread so quickly in social networks. Commun. ACM 55(6), 70–75 (2012). https://doi.org/10.1145/2184319.2184338
First Draft: Online influencers have become powerful vectors in promoting false information and conspiracy theories (2020). https://firstdraftnews.org/articles/influencers-vectors-misinformation/
Dyer, O.: Covid-19: Moderna and Pfizer vaccines prevent infections as well as symptoms, CDC study finds. BMJ: Br. Med. J. (Online) 373 (2021)
Fernández, J.L., Ramírez, J.A.L.: Approaches to the profiling fake news spreaders on Twitter task in English and Spanish. In: CLEF (Working Notes) (2020)
Flick, D.: Combatting fake news: alternatives to limiting social media misinformation and rehabilitating quality journalism. SMU Sci. Tech. L. Rev. 20, 375 (2017)
Heidari, M., et al.: BERT model for fake news detection based on social bot activities in the Covid-19 pandemic. In: 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0103–0109. IEEE (2021)
Huang, Q., Zhou, C., Wu, J., Wang, M., Wang, B.: Deep structure learning for rumor detection on Twitter. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2019)
Imaduwage, S., Kumara, P., Samaraweera, W.: Importance of user representation in propagation network-based fake news detection: a critical review and potential improvements. In: 2022 2nd International Conference on Advanced Research in Computing (ICARC), pp. 90–95. IEEE (2022)
Kula, S., Choraś, M., Kozik, R.: Application of the BERT-based architecture in fake news detection. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds.) CISIS 2019. AISC, vol. 1267, pp. 239–249. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-57805-3_23
Liu, Y., Wu, Y.F.B.: FNED: a deep network for fake news early detection on social media. ACM Trans. Inf. Syst (TOIS) 38(3), 1–33 (2020)
Luo, M., Hancock, J.T., Markowitz, D.M.: Credibility perceptions and detection accuracy of fake news headlines on social media: effects of truth-bias and endorsement cues. Commun. Res. 49(2), 171–195 (2022)
Mishra, S., Shukla, P., Agarwal, R.: Analyzing machine learning enabled fake news detection techniques for diversified datasets. Wirel. Commun. Mob. Comput. 2022 (2022)
Montagni, I., et al.: Acceptance of a Covid-19 vaccine is associated with ability to detect fake news and health literacy. J. Public Health 43(4), 695–702 (2021)
Nassif, A.B., Elnagar, A., Elgendy, O., Afadar, Y.: Arabic fake news detection based on deep contextualized embedding models. Neural Comput. Appl. 34, 16019–16032 (2022). https://doi.org/10.1007/s00521-022-07206-4
Patwa, P., et al.: Overview of CONSTRAINT 2021 shared tasks: detecting English COVID-19 fake news and Hindi hostile posts. In: Chakraborty, T., Shu, K., Bernard, H.R., Liu, H., Akhtar, M.S. (eds.) CONSTRAINT 2021. CCIS, vol. 1402, pp. 42–53. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73696-5_5
Pizarro, J.: Using N-grams to detect fake news spreaders on Twitter. In: CLEF (Working Notes) (2020)
Raza, S., Ding, C.: Fake news detection based on news content and social contexts: a transformer-based approach. Int. J. Data Sci. Anal. 13, 335–362 (2022)
Rocha, Y.M., et al.: The impact of fake news on social media and its influence on health during the Covid-19 pandemic: a systematic review. J. Public Health 1–10 (2021)
Ruan, Y., Durresi, A., Alfantoukh, L.: Using Twitter trust network for stock market analysis. Knowl.-Based Syst. 145, 207–218 (2018)
Saeed, U., Fahim, H., Shirazi, F.: Profiling fake news spreaders on Twitter. In: CLEF (Working notes) (2020)
Shaghaghi, N., Calle, A.M., Manuel Zuluaga Fernandez, J., Hussain, M., Kamdar, Y., Ghosh, S.: Twitter sentiment analysis and political approval ratings for situational awareness. In: 2021 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), pp. 59–65 (2021). https://doi.org/10.1109/CogSIMA51574.2021.9475935
Shahid, W., Li, Y., Staples, D., Amin, G., Hakak, S., Ghorbani, A.: Are you a cyborg, bot or human?—a survey on detecting fake news spreaders. IEEE Access 10, 27069–27083 (2022)
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context and spatialtemporal information for studying fake news on social media. arXiv preprint arXiv:1809.01286 (2018)
Singh, M., Kaur, R., Iyengar, S.R.S.: Multidimensional analysis of fake news spreaders on Twitter. In: Chellappan, S., Choo, K.-K.R., Phan, N.H. (eds.) CSoNet 2020. LNCS, vol. 12575, pp. 354–365. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66046-8_29
Wang, W.Y.: “Liar, liar pants on fire”: a new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648 (2017)
Ye, S., Wu, S.F.: Measuring message propagation and social influence on Twitter.com. In: Bolc, L., Makowski, M., Wierzbicki, A. (eds.) SocInfo 2010. LNCS, vol. 6430, pp. 216–231. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16567-2_16
Acknowledgement
Many Thanks are due to the department of Mathematics & Computer Science (MCS) of the College of Arts & Sciences (CAS), the department of Computer Science & Engineering (CSEN) and the Frugal Innovation Hub (FIH) of the School of Engineering (SoE), and the department of Information Systems & Analytics (ISA) of the School of Business at Santa Clara University in California, USA; as well as, the Computer Engineering department of the school of Engineering at Universidad Pontificia Bolivariana in Medellín, Colombia for their continued support of the project. Also thanks to Mubashir Hussain another member of the research team who worked on UIF graphs.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Ghosh, S., Fernandez, J.M.Z., González, I.Z., Calle, A.M., Shaghaghi, N. (2023). Detecting Fake News Spreaders on Twitter Through Follower Networks. In: Hou, R., Huang, H., Zeng, D., Xia, G., A. Ghany, K.K., Zawbaa, H.M. (eds) Big Data Technologies and Applications. BDTA BDTA 2022 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 480. Springer, Cham. https://doi.org/10.1007/978-3-031-33614-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-33614-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33613-3
Online ISBN: 978-3-031-33614-0
eBook Packages: Computer ScienceComputer Science (R0)