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Reflection of people’s professions on social media platforms

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Abstract

John Holland asserts that most people are one of the six personality types such as realistic, social, investigative, entrepreneurial, traditional, and artistic. Moreover, he claims that personality is an important factor in career choice, career success, and satisfaction. According to his theory of career choice, people’s careers are determined by the interaction between their personality and their environment. The theory points out that people prefer jobs surrounded by others who are like them. It also states that people seek environments that allow them not only to use their skills and abilities but also to express their attitudes and values. On the other hand, people from different professions express their thoughts through Online Social Networks (OSNs). They use social media to express themselves, discuss their interests, connect with friends, and grow their careers. Every day we witness the same person criticizing events in different expertise, such as political events, economic events, etc. Moreover, OSNs connect individuals with like-minded interests and let them share their thoughts, feelings, insights, and emotions. Herein, the reflection of people’s profession on OSNs was examined. Inspired by John Holland’s theory of career choice, the consistency of personality and work environment would be determined from which an individual’s personality can be inferred. In the study, tweets from four different professions: businessman, politician, sportsman, and actor were used to examine whether they are related to the profession. We developed two models using Long Short-Term Memory Neural Networks and Gated Recurrent Unit Neural Networks. The former received macro average accuracy of 94.025% while the latter received 93.025%. According to the simulation results, the proposed models sound and are promising.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Alterkavı S, Erbay H (2021) Design and analysis of a novel authorship verification framework for hijacked social media accounts compromised by a human. Secur Commun Netw 2021

  2. Alterkavı S, Erbay H (2021) Novel authorship verification model for social media accounts compromised by a human. Multimid Tools Appl 80(9):13575–13591

    Article  Google Scholar 

  3. Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR (2021) Abcdm: An attention-based bidirectional cnn-rnn deep model for sentiment analysis. Futur Gen Comput Syst 115:279–294

    Article  Google Scholar 

  4. Bouchaour N, Mazouzi S (2022) Deep pattern-based tumor segmentation in brain MRIs. Neural Comput Appl 1–10

  5. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078

  6. Deniz E, Erbay H, Coşar M (2022) Multi-label classification of e-commerce customer reviews via machine learning. Axioms 11(9):436

    Article  Google Scholar 

  7. Dharma EM, Gaol FL, Leslie H, Warnars H, Soewito B (2022) The accuracy comparison among word2vec, glove, and fasttext towards convolution neural network (CNN) text classification. J Theor Appl Inf Technol 100(2):31

    Google Scholar 

  8. Fanny F, Muliono Y, Tanzil F (2018) A comparison of text classification methods k-NN, naïve bayes, and support vector machine for news classification. Jurnal Informatika: Jurnal Pengembangan IT 3(2):157–160

    Google Scholar 

  9. Ghanem R, Erbay H (2022) Spam detection on social networks using deep contextualized word representation. Multimed Tools Appl 1–16

  10. Gosling J (1995) Introductory statistics. Pascal Press

  11. Hayit T, Erbay H, Varçın F, Hayit F, Akci N (2021) Determination of the severity level of yellow rust disease in wheat by using convolutional neural networks. J Plant Pathol 103(3):923–934

    Article  Google Scholar 

  12. Hegde SU, Zaiba A, Nagaraju Y et al (2021) Hybrid CNN-LSTM model with glove word vector for sentiment analysis on football specific tweets. In: 2021 international conference on advances in electrical, computing, communication and sustainable technologies (ICAECT), pp 1–8. IEEE

  13. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  14. Holland, JL (1997) Making vocational choices: a theory of vocational personalities and work environments. Psychol Assess Resour

  15. Li X, Xie Q, Jiang J, Zhou Y, Huang L (2019) Identifying and monitoring the development trends of emerging technologies using patent analysis and twitter data mining: The case of perovskite solar cell technology. Technol Forecast Soc Chang 146:687–705

    Article  Google Scholar 

  16. Loria S et al (2020) textblob documentation: Release 0.16.0. https://buildmedia.readthedocs.org/media/pdf/textblob/latest/textblob.pdf

  17. Ni R, Cao H (2020) Sentiment analysis based on GloVe and LSTM-GRU. In: 2020 39th Chinese control conference (CCC), pp 7492–7497. IEEE

  18. OBERLO: OBERLO. https://www.oberlo.com/blog/twitter-statistics. Accessed: 2022-02-11

  19. Parolin ES, Salam S, Khan L, Brandt P, Holmes J (2019) Automated verbal-pattern extraction from political news articles using cameo event coding ontology. In: 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE intl conference on high performance and smart computing, (HPSC) and IEEE intl conference on intelligent data and security (IDS), pp 258–266. IEEE

  20. Pennington J, Socher R, Manning CD (2014) GloVe: global vectors for word representation. In: Empirical methods in natural language processing (EMNLP), pp 1532–1543. http://www.aclweb.org/anthology/D14-1162

  21. Pratama BY, Sarno R (2015) Personality classification based on twitter text using naive bayes, knn and svm. In: 2015 international conference on data and software engineering (ICoDSE), pp 170–174. IEEE

  22. Rostamian A, O’Hara JG (2022) Event prediction within directional change framework using a CNN-LSTM model. Neural Comput Appl 1–13

  23. similarweb: similarweb. https://www.similarweb.com/website/twitter.com/#overview. Accessed: 2022-02-11

  24. Singh T, Kumari M (2016) Role of text pre-processing in twitter sentiment analysis. Proc Comput Sci 89:549–554

    Article  Google Scholar 

  25. Stats IL Internet Live Stats-Twitter Usage Statistics. https://www.internetlivestats.com/twitter-statistics/. Accessed: 2022-02-11

  26. Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B, et al (2014) Learning sentiment-specific word embedding for twitter sentiment classification. In: ACL (1), pp 1555–1565. Citeseer

  27. Varcin F, Erbay H, Cetin E, Cetin I, Kultur T (2021) End-to-end computerized diagnosis of spondylolisthesis using only lumbar x-rays. J Digit Imaging 34(1):85–95

    Article  Google Scholar 

  28. Zhang J, Li Y, Tian J, Li T (2018) LSTM-CNN hybrid model for text classification. In: 2018 IEEE 3rd advanced information technology, electronic and automation control conference (IAEAC), pp 1675–1680. IEEE

  29. Zulqarnain M, Ghazali R, Ghouse MG, Mushtaq MF (2019) Efficient processing of GRU based on word embedding for text classification. Int J Inf Vis JOIV

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Authors and Affiliations

  1. Department of Computer Technology, Boğazlıyan Vocational School, Yozgat Bozok University, Yozgat, Türkiye

    Ömer Dağıstanlı

  2. Computer Engineering Department, Engineering Faculty, University of Turkish Aeronautical Association, Etimesgut, Ankara, 06790, Türkiye

    Hasan Erbay

  3. Department of Computer Engineering, Engineering Faculty, Hitit University, Çorum, Türkiye

    Hakan Kör

  4. Computer Engineering Department, Engineering Faculty, Afyon Kocatepe University, Erenler, Afyon, 03204, Türkiye

    Ahmet Haşim Yurttakal

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  1. Ömer Dağıstanlı
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  2. Hasan Erbay
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  3. Hakan Kör
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Corresponding author

Correspondence to Ahmet Haşim Yurttakal.

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Future study

Examine the statement that OSNs reveal the consistency of individuals’ personalities and work environment, as well as OSNs reveal individuals’ personality.

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Dağıstanlı, Ö., Erbay, H., Kör, H. et al. Reflection of people’s professions on social media platforms. Neural Comput & Applic 35, 5575–5586 (2023). https://doi.org/10.1007/s00521-022-07987-8

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  • DOI: https://doi.org/10.1007/s00521-022-07987-8

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