Volume 15, Issue 1 (Special Issue on AI in ICT 2023)                   itrc 2023, 15(1): 45-55 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Dandash M, Asadpour M. Recognizing Personality Traits using Twitter & Facebook for Arabic Speaking Users in Lebanon. itrc 2023; 15 (1) : 5
URL: http://ijict.itrc.ac.ir/article-1-536-en.html
1- Electrical and Computer Engineering Artificial intelligence and robotics group Ph.D. Student, University of Tehran Beirut, Lebanon
2- Electrical and Computer Engineering Assistant Prof. Director of Social Networks Lab University of Tehran Tehran, Iran , asadpour@ut.ac.ir
Abstract:   (1059 Views)
Nowadays, Social media is heading toward personalization more and more. People express themselves and reveal their beliefs, interests, habits, and activities, simply giving a glimpse of their personality traits. The thing that pushed us toward further investigating the mutual relation between personality and social media, taking into consideration the shortage in covering such important topic, especially in rich morphological languages. In this paper, we work on the connection between usage of Arabic language on social outlets (mainly Facebook and Twitter) and personality traits. We indicate the personality traits of users based on the information extracted from their activities and the content of their posts/tweets in Social Networks. We use linguistic features, beside some other features like emoticons. We gathered personality data using Arabic personality test based on Myers-Briggs Type Indicator (MBTI), which contains Thinking, Feeling, Intuition, Introversion, Sensation, Extroversion, Perceiving and Judgement traits. We collected our dataset from 522 volunteers, who permitted us to crawl their tweets and posts in Twitter and Facebook. Analysis of this dataset proved that some linguistic features could be used to differentiate between different personality traits. We used and implemented Deep Learning, and BERT to reveal personality and create a model for this purpose. Up to our knowledge, this is the first work on detection of personality traits from social network’s data in Arabic language.
Article number: 5
Full-Text [PDF 1130 kb]   (403 Downloads)    
Type of Study: Research | Subject: Information Technology

1. [1] Habash NY. Introduction to Arabic natural language processing. Synth. Lect. Hum. Lang. Technol. 2010;3(1):1-187. [DOI:10.2200/S00277ED1V01Y201008HLT010]
2. [2] N. Habash, R. Eskander, A. Hawwari, A morphological analyzer for Egyptian Arabic, Presented at the Proceedings of the Twelfth Meeting of the Special Interest Group on Computational Morphology and Phonology, 2012, pp. 1-9.
3. [3] M. Abdul-Mageed, M.T. Diab, SANA: a Large Scale multigenre, multi-dialect lexicon for Arabic subjectivity and sentiment analysis, Presented at the LREC, 2014, pp. 1162-1169.
4. [4] ElSahar H, El-Beltagy SR. Building large Arabic multi-domain resources for sentiment analysis. In: Computational Linguistics and Intelligent Text Processing. Springer; 2015. p. 23-34. [DOI:10.1007/978-3-319-18117-2_2]
5. [5] Badaro G, Baly R, Hajj H, Habash N, El-Hajj W. A Large Scale Arabic sentiment lexicon for Arabic opinion mining. ANLP 2014. [DOI:10.3115/v1/W14-3623]
6. [6] M. Abdul-Mageed, M.T. Diab, AWATIF: a multi-genre corpus for modern standard Arabic subjectivity and sentiment analysis, Presented at the LREC, 2012, pp. 3907-3914.
7. [7] E. Refaee, V. Rieser, An Arabic twitter corpus for subjectivity and sentiment analysis, Presented at the LREC, 2014, pp. 2268-2273.
8. [8] Yilun Wang, Understanding Personality through Social Media Department of Computer Science, Stanford University.
9. [9] Jennifer Golbeck, Cristina Robles, Michon Edmondson, and Karen Turner. 2011. Predicting personality from twitter. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), pages 149-156. IEEE. [DOI:10.1109/PASSAT/SocialCom.2011.33]
10. [10] Leqi Liu, Daniel Preotiuc-Pietro, Zahra Riahi Samani, Mohsen E. Moghaddam, Lyle Ungar, Analyzing Personality through Social Media Profile Picture Choice.
11. [11] D. Quercia, M. Kosinski, D. Stillwell, and J. Crowcroft, "Our Twitter Profiles, Our Selves: Predicting Personality with Twitter," in 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing, 2011, pp. 180-185. [DOI:10.1109/PASSAT/SocialCom.2011.26]
12. [12] Chris Sumner, Alison Byers, Rachel Boochever, and Gregory J Park. 2012. Predicting dark triad personality traits from twitter usage and a linguistic analysis of tweets. (ICMLA), 2012 11th International Conference on Machine Learning and Applications, volume 2, pages 386-393. IEEE. [DOI:10.1109/ICMLA.2012.218]
13. [13] H.Andrew Schwartz, Johannes C.Eichstaedt ,Lukasz Dziurzynski,, Margaret L.Kern, Martin E.P.Seligman, Lyle H.Ungar, Eduardo Blanco, Michal Kosinski and David Stillwell, Toward Personality Insights from Language Exploration in Social Media. 2013 AAAI Spring Symposium Series.
14. [14] Golnoosh Farnadi, Susana Zoghbi, Marie-Francine Moens, Martine De Cock, Recognizing Personality Traits Using Facebook Status Updates. Seventh International AAAI Conference on Weblogs and Social Media.2013
15. [15] Fabio Celli, Fabio Pianesi, David Stillwell, Michal Kosinski. Workshop on Computational Personality Recognition: Shared Task. Seventh International AAAI Conference on Weblogs and Social Media.2013
16. [16] Big Five - the personality in five dimensions https://peats.de/article/big-five-die-personlichkeit-in-funfdimensionen
17. [17] Shrout PE, Fiske ST (1995). Personality research, methods, and theory. Psychology Press.
18. [18] Allport GW, Odbert HS (1936). "Trait names: A psycholexical study". PsychologicalMonographs. 47:211. doi:10.1037/h009 3360 [DOI:10.1037/h0093360]
19. [19] Bagby RM, Marshall MB, Georgiades S (February 2005). "Dimensional personality traits and the prediction of DSM-IV personality disorder symptom counts in a nonclinical sample". Journal of Personality Disorders. 19 (1): 53-67. doi:10.1521/pedi. PMID 15899720 [DOI:10.1521/pedi.] [PMID]
20. [20] Tupes EC, Christal RE (1961). "Recurrent personality factors based on trait ratings". USAF ASD Tech. Rep. 60 (61-97): 225-51. doi:10.1111/j.1467-6494.1992.tb00973.x. PMID 1635043. [DOI:10.1111/j.1467-6494.1992.tb00973.x] [PMID]
21. [21] Norman WT (June 1963). "Toward an adequate taxonomy of personality attributes: replicated factors structure in peer nomination personality ratings". Journal of Abnormal and Social Psychology. 66 (6): 574-83. doi:10.1037/h0040291. PMID 13938947 [DOI:10.1037/h0040291] [PMID]
22. [22] DeYoung CG, Quilty LC, Peterson JB (November 2007). "Between facets and domains: 10 aspects of the Big Five". Journal of Personality and Social Psychology. 93 (5): 880-96. doi:10.1037/0022-3514.93.5.880. PMID 17983306. [DOI:10.1037/0022-3514.93.5.880] [PMID]
23. [23] Myers, Isabel B.; Myers, Peter B. (1995) [1980]. Gifts Differing: Understanding Personality Type. Mountain View, CA: Davies-Black Publishing. ISBN 978-0-89106-074-1.
24. [24] "MBTI® Basics". The Myers & Briggs Foundation. Archived from the original on 2021-10-12. Retrieved 2021-10-28.
25. [25] "Myers-Briggs Type Indicator® (MBTI®) | Official Myers Briggs Personality Test". www.themyersbriggs.com.
26. [26] Huber, Daniel; Kaufmann, Heiner; Steinmann, Martin (2017). The Missing Link: The Innovation Gap. Bridging the Innovation Gap. Cham: Springer International Publishing. pp.21-41. doi:10.1007/978-3-319-55498-3_3. ISBN 978-3-319-55497-6. Retrieved 2021-10-28. [DOI:10.1007/978-3-319-55498-3_3] []
27. [27] Jim Isaak, Mina J.Hana (2018). "User data privacy: Facebook, Cambridge Analytica, and privacy protection".Ieee.org computer 51 (8),56-59. [DOI:10.1109/MC.2018.3191268]
28. [28] http://jwm.life/uploads/mbti/mbti-test-eman-azmi1.pdf
29. [29] M. McPherson, L. Smith-Lovin, and J. Cook. (August 2001)."Birds of a Feather: Homophily in Social Networks" Annual Review of Sociology; Vol. 27: 415-444. [DOI:10.1146/annurev.soc.27.1.415]
30. [30] Sivic, Josef (April 2009). "Efficient visual search of videos cast as text retrieval". IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 31,NO. 4. IEEE. pp. 591-605. [DOI:10.1109/TPAMI.2008.111] [PMID]
31. [31] Rajaraman, A.; Ullman, J.D. (2011). "Data Mining". Mining of Massive Datasets. pp. 1-17. https://doi.org/10.1017/CBO9781139058452 https://doi.org/10.1017/CBO9781139058452.002 [DOI:10.1017/CBO9781139058452.005]
32. [32] Tomas Mikolov; et al. (2013). "Efficient Estimation of Word Representations in Vector Space". International Conference on Learning Representations (ICLR 2013).
33. [33] F. Celli, "Unsupervised Personality Recognition for Social Network Sites," in ICDS 2012, The Sixth International Conference on Digital Society, 2012, no. c, pp. 59-62.
34. [34] J. Golbeck, C. Robles, and K. Turner, "Predicting personality with social media," in CHI'11 extended abstracts on human factors in computing systems, 2011, pp. 253-262. [DOI:10.1145/1979742.1979614]
35. [35] Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina (11 October 2018). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". arXiv:1810.04805v2.
36. [36] @inproceedings{antoun2020arabert,title={AraBERT: Transformer-based Model for Arabic Language Understanding},author={Antoun, Wissam and Baly, Fady and Hajj, Hazem},booktitle={LREC 2020 Workshop Language Resources and Evaluation Conference 11--16 May 2020},pages={9}}.

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.