• F. E. AYO Department of Physical and Computer Sciences, McPherson University, Seriki Sotayo, Ogun State, Nigeria
  • O. FOLORUNSO Department of Computer Science, College of Physical Sciences, Federal University of Agriculture, Abeokuta, P. M. B 2240, Nigeria.
  • A. ABAYOMI-ALLI Department of Computer Science, College of Physical Sciences, Federal University of Agriculture, Abeokuta, P. M. B 2240, Nigeria.
  • A. C. OLUBIYI Department of Physical and Computer Sciences, McPherson University, Seriki Sotayo, Ogun State, Nigeria



Social Networking Sites, Personality traits, Feedforward Neural Networks, Sigmoid transfer function, Logistic  function


Researchers have reflected on personal traits that may predict Social Networking Sites (SNS) addiction. However, most of the researchers involved in the findings of personality traits predictor for social networking addiction either postulate or based their conclusions on analytical tools. Moreso, a review of the literature reveals that the prediction of social networking addiction using classifiers have not been well researched. We examined the prediction of SNS addiction from a well-structured questionnaire consisting of sixteen (16) personality traits. The questionnaire was administered on the google form with a response rate of 95% out of the 102-sample size. Additionally, a three (3) variant sigmoid transfer feed- forward neural networks was developed for the prediction of SNS addiction. Result indicated that pertinence (β = 0.251, p  0.01) was the most powerful predictor of social networking addiction in general and less obscurity addiction (β = 0.244, p  0.01). Experimental results also showed that the developed classifier correctly predict SNS addiction with 98% accuracy compared to similar classifiers.





Albrect, U., Kirschner, N. E., Grüsser, S. M. 2007. Diagnostic instruments for behavioural addiction: An overview. GMS Psycho-Social-Medicine, 4: 1–11.

Andreassen, C. S., Pallesen, S., Griffiths, M. D. 2017. The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. Addictive behaviors, 64: 287-293.

Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M., Vanoli, G. P. 2017. Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy, 118: 999-1017.

Bengio, Y. 2009. Learning deep architectures for AI. Foundations and trends in Machine Learning, 2(1): 1-127.

Brajevic, I., & Tuba, M. 2013. Training feed-forward neural networks using firefly algorithm. In Proceedings of the 12th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED’13) (pp. 156-161).

Brenner, V. 1997. Psychology of computer use: XLVII. Parameters of Internet use, abuse and addiction: the first 90 days of the Internet Usage Survey. Psychological reports, 80(3):879-882.

Brewer, P. R., Habegger, M., Harrington, R., Hoffman, L. H., Jones, P. E., Lambe, J. L. 2016. Interactivity between candidates and citizens on a social networking site: Effects on perceptions and vote intentions. Journal of Experimental Political Science, 3(01):84-96.

Carillo, K., Scornavacca, E., Za, S. 2017. The role of media dependency in predicting continuance intention to use ubiquitous media systems. Information & Management, 54(3): 317-335.

Corte-Valiente, A. D., Castillo-Sequera, J. L., Castillo-Martinez, A., Gómez-Pulido, J. M., Gutierrez-Martinez, J. M. 2017. An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems. Energies, 10(2):175.

Deng, S., Liu, Y., Li, H., & Hu, F. 2013. How does personality matter? An investigation of the impact of extraversion on individuals’ SNS use. Cyberpsychology, Behavior and Social Networking, 16(8): 575–581.

Donelly, E., Kuss, D. J. 2016. Depression among users of social networking sites (SNSs): the role of SNS addiction and increased usage. Journal of Addiction and Preventive Medicine, 1(2): 107.

Echeburua, E., & de Corral, P. 2010. Addiction to new technologies and to online social networking in young people: A new challenge. Adicciones, 22(2): 91–95.

El-Dahshan, E. S. A., Mohsen, H. M., Revett, K., Salem, A. B. M. 2014. Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert systems with Applications, 41(11): 5526-5545.

Elhai, J. D., Levine, J. C., Dvorak, R. D., Hall, B. J. 2017. Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use. Computers in Human Behavior, 69: 75-82.

Foerster, M., & Röösli, M. 2017. A latent class analysis on adolescents media use and associations with health related quality of life. Computers in Human Behavior, 71: 266-274.

Greydanus, D.E., & Greydanus, M.M. 2012. Internet use, misuse, and addiction in adolescents: Current issues and challenges. International Journal of Adolescent Medicine and Health, 24(4): 283–289.

Griffiths, M. 2005. A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4): 191-197.

Griffiths, M. D. 1999. Internet addiction: Fact or fiction? Psychologist, 12: 246–250

Griffiths, M. D. 2000. Does Internet and computer “addiction” exist? Some case-study evidence. CyberPsychology & Behavior, 3 (2): 211–218.

Griffiths, M. D., Kuss, D. J., Pontes, H. M., & Billieux, J. 2016. Where do gambling and internet ‘addictions’ belong? The status of ‘other’addictions. The SAGE handbook of drug & alcohol studies biological approaches, pp446-470.

Griffiths, M.D. 1996. Nicotine, tobacco, and addiction. Nature, 384:18.

Griths, M. D., Kuss, D. J., & Demetrovics, Z. 2014. Social networking addiction: An overview of preliminary findings. Behavioral addictions: Criteria, evidence and treatment, pp119-141.

Hagan, M. T., & Menhaj, M. B. 1994. Training feedforward networks with the Marquardt algorithm. IEEE transactions on Neural Networks, 5(6): 989-993.

Han, J., & Moraga, C. 1995. The influence of the sigmoid function parameters on the speed of backpropagation learning. From Natural to Artificial Neural Computation, pp195-201.

Holmgren, H. G., & Coyne, S. M. 2017. Can’t stop scrolling: pathological use of social networking sites in emerging adulthood. Addiction Research & Theory, pp1-8.

Hou, X. L., Wang, H. Z., Guo, C., Gaskin, J., Rost, D. H., & Wang, J. L. 2017. Psychological resilience can help combat the effect of stress on problematic social networking site usage. Personality and Individual Differences, 109: 61-66.

Ifinedo, P. 2016. Applying uses and gratifications theory and social influence processes to understand students' pervasive adoption of social networking sites: Perspectives from the Americas. International Journal of Information Management, 36(2): 192-206.

Kalogirou, S. A. 2001. Artificial neural networks in renewable energy systems applications: a review. Renewable and sustainable energy reviews, 5(4): 373-401.

Kaplan, A. M., Haenlein, M. 2010. Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53(1): 59-68.

King, A. L. S., Valença, A. M., Silva, A. C. O., Baczynski, T., Carvalho, M. R., Nardi, A. E. 2013. Nomophobia: Dependency on virtual environments or social phobia? Computers in Human Behavior, 29(1): 140-144.

Kuss, D. J., Griffiths, M. D. 2011. Addiction to social networks on the internet: A literature review of empirical research. International Journal of Environment and Public Health, 8(9): 3528-3552.

Labovitz, C., Iekel-Johnson, S., McPherson, D., Oberheide, J., & Jahanian, F. 2010. Internet inter-domain traffic. In ACM SIGCOMM Computer Communication Review, ACM. 40(4):75-86.

Lee, E., Ahn, J., & Kim, Y. J. 2014. Personality traits and self-presentation at Facebook. Personality and Individual Differences, 69: 162–167.

Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. 2017. A survey of deep neural network architectures and their applications. Neurocomputing, 234: 11-26.

Montana, D. J., & Davis, L. 1989. Training Feedforward Neural Networks Using Genetic Algorithms. In IJCAI, 89: 762-767.

Nadkarni, A., & Hofmann, S. G. 2012. Why do people use Facebook? Personality and Individual Differences, 52(3): 243–249.

Oberst, U., Wegmann, E., Stodt, B., Brand, M., Chamarro, A. 2017. Negative consequences from heavy social networking in adolescents: the mediating role of fear of missing out. Journal of Adolescence, 55: 51-60.

Ojha, V. K., Abraham, A., Snášel, V. 2017. Metaheuristic design of feedforward neural networks: A review of two decades of research. Engineering Applications of Artificial Intelligence, 60: 97-116.

Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., James, A. E. 2017. Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69: 1-9.

Radovic, A., Gmelin, T., Stein, B. D., Miller, E. 2017. Depressed adolescents' positive and negative use of social media. Journal of Adolescence, 55: 5-15.

Ryan, T., Chester, A., Reece, J., & Xenos, S. 2016. A qualitative exploration of Facebook addiction: Working toward construct validity. Addicta: The Turkish Journal on Addictions, 3(1): 55-76.

Seabrook, E. M., Kern, M. L., & Rickard, N. S. 2016. Social networking sites, depression, and anxiety: a systematic review. JMIR Mental Health, 3(4): e50.

Seidman, G. 2013. Self-presentation and belonging on Facebook: How personality influences social media use and motivations. Personality and Individual Differences, 54(3): 402–407.

Shaffer, H. J., LaPlante, D. A., LaBrie, R. A., Kidman, R. C., Donato, A. N., Stanton, M. V. 2004. Toward a syndrome model of addiction: Multiple expressions, common etiology. Harvard review of psychiatry, 12(6): 367-374.

Shah, G. H., Alfonso, M. L., Jolani, N., Landis, D. C., Alfonso, M. L., Luque, J. S., Lindenberger, J. 2016. Social Media and Alcohol Use: Adverse Impact of Facebook and... Public Health and Welfare: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications, 437pp.

Sharma, L. K., Vishal, V., Singh, T. N. 2017. Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties. Measurement, 102: 158-169.

Singh, S., Sao, A., Nagare, T. B., & Dharmarajan, A. 2017. Role of Social Media Marketing In Brand Building: The New Age Marketing Strategy. International Journal of Scientific Research, 5(9).

Sollazzo, G., Fwa, T. F., Bosurgi, G. 2017. An ANN model to correlate roughness and structural performance in asphalt pavements. Construction and Building Materials, 134: 684-693.

Soror, A. A., Hammer, B. I., Steelman, Z. R., Davis, F. D., Limayem, M. M. 2015. Good habits gone bad: Explaining negative consequences associated with the use of mobile phones from a dual‐systems perspective. Information Systems Journal, 25(4): 403-427.

Tang, C. S. K., & Koh, Y. Y. W. 2017. Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder. Asian Journal of Psychiatry, 25: 175-178.

Turel, O., Serenko, A. 2012. The benefits and dangers of enjoyment with social networking websites. European Journal of Information Systems, 21(5) : 512-528.

Verhulst, P. F. 1845. Recherches mathématiques sur la loi d'accroissement de la population. Nouveaux mémoires de l'académie royale des sciences et belles-lettres de Bruxelles, 18 : 14-54.

Wang, J. L., Jackson, L. A., Wang, H. Z., & Gaskin, J. 2015. Predicting social networking site (SNS) use: Personality, attitudes, motivation and internet self-efficacy. Personality and Individual Differences, 80: 119-124.

Wartella, E., Rideout, V., Montague, H., Beaudoin-Ryan, L., Lauricella, A. 2016. Teens, health and technology: A national survey. Media and Communication, 4(3).

Wozniak, T., Stangl, B., Schegg, R., Liebrich, A. 2017. The return on tourism organizations’ social media investments: preliminary evidence from Belgium, France, and Switzerland. Information Technology & Tourism, pp1-26.

Wu, X. S., Zhang, Z. H., Zhao, F., Wang, W. J., Li, Y. F., Bi, L., Gong, F. F. 2016. Prevalence of Internet addiction and its association with social support and other related factors among adolescents in China. Journal of Adolescence, 52: 103-111.

Xu, H., Tan, B. C. Y. 2012. Why do i keep checking Facebook: Effects of message characteristics on the formation of social network services addiction. Located at ticle=1216&context= icis2012.

Yao, X. 1999. Evolving artificial neural networks. Proceedings of the IEEE, 87(9): 1423-1447.