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Authors:
Jose Ramon Saura , ORCID: https://orcid.org/0000-0002-9457-7745 Rey Juan Carlos University (Spain) Jonathan Gomez Punzon , ORCID: https://orcid.org/0000-0003-2493-4935 Rey Juan Carlos University (Spain)
Pages: 231-236
Language: English
DOI: https://doi.org/10.21272/mmi.2020.4-18
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Abstract
Nowadays, internet users spend much of their time on social networks, where they share and generate content, support the causes and activities they like, get in touch with their peers, and generate audio-visual content. Besides, they also share their opinions with other users, thus producing User-Generated Content (UGC). The authors noted that UGC lacks proven scientific, professional, or academic quality. However, when content is generated massively in social networks, it can get viral and achieve the most significant engagement of users in the community. Furthermore, there is evidence that the content with the most significant impact on other users is the one that achieves the greatest engagement and support. The scientific review analysis indicated that usually, the content that achieves more impact and engagement in social media is related to fake news or published by fake users. In this context, the present study aims to theorize and define the concept of «faker» based on a review of previous studies. Main results show that a «faker» is a user who is not a real person, but pretends to be such. Based on the results of the exploratory analysis, the following 6 types of users classified as fakers were identified and analyzed: conspiranoid (users who share compulsive and self-taught content in which they share minimal details of the theory they support, have powerful firm beliefs, and always find a way to verify their hypotheses); proselytizing (users who try to gain followers by any means and convince other followers to follow them); narcissists (users who base their content on love and attraction to themselves and generate false content that reflects their own image as the main message); creators of chaos (users whose main objective is to generate chaos in social networks and base their arguments and theories on personal, professional, or political relationships among other users to generate conflicts that will increase the chaos within a closed community); satyr humor (users who generate content focused on the satire targeting public, mythological, ideological, or other characters or entities and defame others by focusing on the actions of public characters); paranoid tyrants (users who focus on the analysis of the information overload, which makes it difficult to interpret the contents on the Internet today). In the frame of this paper, the authors provided a discussion of important theoretical and practical implications of obtained results for the marketing industry and digital marketing in social media.
Keywords: faker, fake content, social media, social network, UGC.
JEL Classification: O1, M31, M37.
Cite as: Saura, J. R., & Punzon, J. G (2020). Defining the types of «fakers» in social media. Marketing and Management of Innovations, 4, 231-236. https://doi.org/10.21272/mmi.2020.4-18
This work is licensed under a Creative Commons Attribution 4.0 International License
References
- Apuke, O. D., & Omar, B. (2020). Fake news and COVID-19: modelling the predictors of fake news sharing among social media users. Telematics and Informatics, 101475. [Google Scholar][CrossRef]
- Aronson, Z. H., Reilly, R. R., & Lynn, G. S. (2008). The role of leader personality in new product development success: an examination of teams developing radical and incremental innovations. International Journal of Technology Management, 44(1-2), 5. [Google Scholar] [CrossRef]
- Atodiresei, C. S., Tănăselea, A., & Iftene, A. (2018). Identifying fake news and fake users on twitter. Procedia Computer Science, 126, 451-461. [Google Scholar] [CrossRef]
- Balaanand, M., Karthikeyan, N., Karthik, S., Varatharajan, R., Manogaran, G., & Sivaparthipan, C. B. (2019). An enhanced graph-based semi-supervised learning algorithm to detect fake users on Twitter. The Journal of Supercomputing, 75(9), 6085-6105. [Google Scholar] [CrossRef]
- Borges‐Tiago, T., Tiago, F., Silva, O., Guaita Martínez, J. M., & Botella‐Carrubi, D. (2020). Online users’ attitudes toward fake news: Implications for brand management. Psychology & Marketing, 37(9), 1171-1184. [Google Scholar] [CrossRef]
- Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., … & Kumar, V. (2020). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 102168. [Google Scholar] [CrossRef]
- Fire, M., Kagan, D., Elyashar, A., & Elovici, Y. (2014). Friend or foe? Fake profile identification in online social networks. Social Network Analysis and Mining, 4(1), 194. [Google Scholar][CrossRef]
- Gurajala, S., White, J. S., Hudson, B., Voter, B. R., & Matthews, J. N. (2016). Profile characteristics of fake Twitter accounts. Big Data & Society, 3(2), 2053951716674236. [Google Scholar][Google Scholar]
- Kaur, D., Uslu, S., & Durresi, A. (2019). Trust-based security mechanism for detecting clusters of fake users in social networks. In Workshops of the International Conference on Advanced Information Networking and Applications (pp. 641-650). Springer, Cham. [Google Scholar] [CrossRef]
- Krombholz, K., Merkl, D., & Weippl, E. (2012). Fake identities in social media: A case study on the sustainability of the Facebook business model. Journal of Service Science Research, 4(2), 175-212. [Google Scholar] [CrossRef]
- Lies, J. (2019). Marketing Intelligence and Big Data: Digital Marketing Techniques on their Way to Becoming Social Engineering Techniques in Marketing. International Journal of Interactive Multimedia & Artificial Intelligence, 5(5). [Google Scholar] [CrossRef]
- Lyulyov, O., Chygryn, O., & Pimonenko, T. (2018). National brand as a marketing determinant of macroeconomic stability. Marketing and Management of Innovations, (3), 142–152. [Google Scholar] [CrossRef]
- Masood, F., Almogren, A., Abbas, A., Khattak, H. A., Din, I. U., Guizani, M., & Zuair, M. (2019). Spammer detection and fake user identification on social networks. IEEE Access, 7, 68140-68152 [Google Scholar] [CrossRef]
- Mohammadrezaei, M., Shiri, M. E., & Rahmani, A. M. (2018). Identifying fake accounts on social networks based on graph analysis and classification algorithms. Security and Communication Networks, 2018. [Google Scholar] [CrossRef]
- Palos-Sanchez, P., Saura, J. R., & Correia, M. B. (2020). Do tourism applications’ quality and user experience influence its acceptance by tourists?. Review of Managerial Science, 1-37. [Google Scholar] [CrossRef]
- Purba, K. R., Asirvatham, D., & Murugesan, R. K. (2020). Classification of instagram fake users using supervised machine learning algorithms. International Journal of Electrical and Computer Engineering, 10(3), 2763. [Google Scholar] [CrossRef]
- Reyes-Menendez, A., Saura, J. R., & Filipe, F. (2019). The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review. PeerJ Computer Science, 5, 219. [Google Scholar] [CrossRef]
- Reyes-Menendez, A., Saura, J. R., & Thomas, S. B. (2020). Exploring key indicators of social identity in the #MeToo era: Using discourse analysis in UGC. International Journal of Information Management, 54, 102129. [Google Scholar] [CrossRef]
- Saura, J. R. (2020). Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics. Journal of Innovation & Knowledge. [Google Scholar] [CrossRef]
- Saura, J. R., Herráez, B. R., & Reyes-Menendez, A. (2019). Comparing a traditional approach for financial Brand Communication Analysis with a Big Data Analytics technique. IEEE Access, 7, 37100-37108. [Google Scholar] [CrossRef]
- Saura, J. R., Palos-Sanchez, P., & Blanco-González, A. (2019a). The importance of information service offerings of collaborative CRMs on decision-making in B2B marketing. Journal of Business & Industrial Marketing. [Google Scholar] [CrossRef]
- Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International journal of information management, 39, 156-168. [Google Scholar] [CrossRef]
- Talwar, S., Dhir, A., Kaur, P., Zafar, N., & Alrasheedy, M. (2019). Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. Journal of Retailing and Consumer Services, 51, 72-82. [Google Scholar] [CrossRef]
- Wang, B., Zhang, L., & Gong, N. Z. (2018, September). Sybilblind: Detecting fake users in online social networks without manual labels. In International Symposium on Research in Attacks, Intrusions, and Defenses (pp. 228-249). Springer, Cham. [Google Scholar] [CrossRef]
- Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: writing a literature review. MIS Quarterly, 26(2), 13-23. [Google Scholar]
- Yevdokimov, Y., Melnyk, L., Lyulyov, O., Panchenko, O., & Kubatko, V. (2018). Economic freedom and democracy: Determinant factors in increasing macroeconomic stability. Problems and Perspectives in Management, 16(2), 279-290. [Google Scholar]
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