--- title: "Personality Analysis for Social Media Users — Dandash 2024" description: "The paper asks whether Arabic-language Twitter activity can reveal a user's personality type, and whether personality changes the sentiment people…" authors: ["Mokhaiber Dandash", "Masoud Asadpour"] year: 2024 venue: "arXiv:2407.06314" source: "https://arxiv.org/abs/2407.06314" doi: "10.48550/arxiv.2407.06314" arxiv: "2407.06314" licence: "arXiv — non-exclusive license to distribute" licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/dandash-2024-personality-analysis-social-media-users/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/dandash-2024-personality-analysis-social-media-users/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/4/) / Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis # Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis **Authors:** Mokhaiber Dandash, Masoud Asadpour · **Year:** 2024 · **Venue:** arXiv:2407.06314 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/) **Links:** [Publisher's page](https://arxiv.org/abs/2407.06314) · [PDF](https://arxiv.org/pdf/2407.06314) · [DOI 10.48550/arxiv.2407.06314](https://doi.org/10.48550/arxiv.2407.06314) · [arXiv 2407.06314](https://arxiv.org/abs/2407.06314) · [OpenAlex](https://openalex.org/W4408283505) **Citations:** 5 citations (openalex-snapshot · 2026-10-14) · [2](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary The paper asks whether Arabic-language Twitter activity can reveal a user's personality type, and whether personality changes the sentiment people express online. Arabic is under-studied here: its morphology is rich, diacritics are usually omitted, and many dialects lack standard spelling. The authors found users who took the Arabic version of the 16personalities (MBTI-style) test and then posted their result on Twitter. They crawled the profiles and timelines of these users to build a corpus called "AraPers" with 3,250 people. They ran statistical comparisons using linguistic cues, profile attributes (gender, age, bio), emoticons and interaction counts. They also trained several machine-learning classifiers on a version of the data with an equal number of tweets per type. BERT performed best, at 74.86% accuracy. A separate sentiment classifier, applied per personality type, showed different positive, negative and neutral shares across types. The work matters because it gives Arabic a sizable personality-labelled resource. It also links user traits to sentiment, which is relevant to opinion tracking and political discourse analysis. ## Key points - Dataset "AraPers" covers 3,250 Twitter users whose labels come from their own shared results on 16personalities.com/ar (16 MBTI types, four opposed axes: E/I, S/N, T/F, J/P). - The features combine tweet language, profile statistics (gender, age, bio, etc.), emoticons, and interactions such as retweets and replies. - The best personality model was BERT, at 74.86% accuracy. Models were trained on a "Balanced Corpus" with equal tweet counts per type. - Sentiment was labelled by a logistic regression model trained on 14,744 tagged tweets, with three classes: positive, negative and neutral. - ESFJ and ESTP had the most positive tweets, INFJ and ISTJ the most negative, and ESTJ and ENTJ the most neutral. By axis, E/S/F leaned positive, I/P/N leaned negative, and J/T leaned neutral. The authors flag self-selection, cultural and socioeconomic bias in their sample. ## Interesting ideas - **Self-disclosed labels:** Labels are harvested from users who voluntarily publish a standardized test outcome, which avoids hand annotation while keeping a fixed label scheme. — For Mova (learn): We could mine texts where authors explicitly state a fact about their own content (e.g. "the answer is X") as cheap silver labels. These labels should be cross-checked by witness parsers before use. - **Balanced comparisons:** The data is equalized to the same number of tweets per class before training or comparing groups, so differences are not artifacts of class size. — For Mova (learn): Evaluation suites could be balanced per construction or frame type, so that a frequent phenomenon cannot mask regressions on rare ones. - **Factorized labels:** A 16-way label is broken into four binary axes, and results are reported per axis, which yields interpretable trends instead of 16 noisy cells. — For Mova (frames): Speech-act or stance labels could be decomposed into independent dimensions (e.g. assertive vs directive, polite vs blunt), so each decision stays separately explainable. - **Classifier as a probe:** An existing sentiment model is used as a measuring instrument over labelled populations, and its output distributions are compared per group rather than its accuracy. — For Mova (learn): A fixed tagger or parser could be run over different genres (fables, school tests, word problems), comparing label distributions to spot domain shift before it hurts answers. - **Naming sampling bias:** The authors state openly that volunteers who disclose results are not a representative sample, and they treat this as a limit on validity. — For Mova (learn): Silver data built only from sentences that all witnesses agree on is likewise biased toward easy cases. Negative controls should include the hard sentences that the agreement filter discards. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) ## Cited by Cited by 2 papers of our library cite it (of the 140884 whose bibliographies we read); 1 of them have a page here, 1 not yet. - [Is AI Catching Up to Human Expression? Exploring Emotion, Personality, Authorship, and Linguistic Style in English and Arabic with Six Large Language Models](https://md.research.elamur.ai/free/en/papers/alsadhan-2026-ai-catching-up-human-expression/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{dandash2024personality, title = {{Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis}}, author = {Dandash, Mokhaiber and Asadpour, Masoud}, year = {2024}, doi = {10.48550/arxiv.2407.06314}, eprint = {2407.06314}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.2407.06314} } ``` - [← previous Pedagogical Insights from Al-Mawahib Al-Rabbaniyah: Enhancing Arabic Grammar Learning through the Nazm of Al-Ajrumiyyah](https://md.research.elamur.ai/free/en/papers/solehudin-2024-pedagogical-insights-al-mawahib-al/) - [next → Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/4/) ## References 54 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Dandash, M., & Asadpour, M. (2023). Recognizing personality traits using Twitter & Facebook for Arabic speaking users in Lebanon. Inernational Journal of Information and Communication Technology Research, 15(1), 45-55. https://doi.org/10.61186/itrc.15.1.45 [↗](https://doi.org/10.61186/itrc.15.1.45) 2. McKenna, K. Y. A., Green, A. S., & Gleason, M. E. J. (2002). Relationship formation on the Internet: What’s the big attraction? Journal of Social Issues, 58(1), 9–31. https://doi.org/10.1111/1540-4560.00246 [↗](https://doi.org/10.1111/1540-4560.00246) 3. Gosling, S. D., Augustine, A. A., Vazire, S., Holtzman, N., & Gaddis, S. (2011). Manifestations of personality in online social networks: Self-reported Facebook-related behaviors and observable profile information. Cyberpsychology, Behavior, and Social Networking, 14(9), 483–488. https://doi.org/10.1089/cyber.2010.0087 [↗](https://doi.org/10.1089/cyber.2010.0087) 4. Cervone, D., & Pervin, L. A. (2015). Personality: Theory and research. John Wiley & Sons. [find](https://scholar.google.com/scholar?q=Cervone%2C+D%2E%2C+%26+Pervin%2C+L%2E+A%2E+%282015%29%2E+Personality%3A+Theory+and+research%2E+John+Wiley+%26+Sons%2E) 5. Alexa. (2017). The top 500 sites on the web. Alexa Internet. Retrieved March 28, 2017, from [https://www.alexa.com/topsites](https://www.alexa.com/topsites) [↗](https://www.alexa.com/topsites) 6. Shirky, C. (2011). The political power of social media: Technology, the public sphere, and political change. Foreign Affairs, 90(1), 28–41. Retrieved from [https://www.jstor.org/stable/25800379](https://www.jstor.org/stable/25800379) [↗](https://www.jstor.org/stable/25800379) 7. Carpenter, C. J. (2012). Narcissism on Facebook: Self-promotional and anti-social behavior. Personality and Individual Differences, 52(4), 482–486. https://doi.org/10.1016/j.paid.2011.11.011 [↗](https://doi.org/10.1016/j.paid.2011.11.011) 8. Correa, T., Hinsley, A. W., & Gil de Zúñiga, H. (2010). Who interacts on the Web? The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247–253. https://doi.org/10.1016/j.chb.2009.09.003 [↗](https://doi.org/10.1016/j.chb.2009.09.003) 9. Dupuis, M., Khadeer, S., & Huang, J. (2017). "I Got the Job!" An exploratory study examining the psychological factors related to status updates on Facebook. Computers in Human Behavior, 73, 132–140. https://doi.org/10.1016/j.chb.2017.03.020 [↗](https://doi.org/10.1016/j.chb.2017.03.020) 10. Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., … & Ungar, L. H. (2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS One, 8(9), e73791. https://doi.org/ 10.1371/journal.pone.0073791 [↗](https://doi.org/10.1371/journal.pone.0073791) 11. Wang, D. (2017). A study of the relationship between narcissism, extraversion, drive for entertainment, and narcissistic behavior on social networking sites. Computers in Human Behavior, 66, 138–148. https://doi.org/10.1016/j.chb.2016.09.036 [↗](https://doi.org/10.1016/j.chb.2016.09.036) 12. Caplan, S. E. (2006). Relations among loneliness, social anxiety, and problematic Internet use. CyberPsychology & Behavior, 10(2), 234–242. https://doi.org/10.1089/cpb.2006.9963 [↗](https://doi.org/10.1089/cpb.2006.9963) 13. Finn, S. (1997). Origins of media exposure: Linking personality traits to TV, radio, print, and film use. Communication Research, 24(5), 507–529. https://doi.org/10.1177/009365097024005003 [↗](https://doi.org/10.1177/009365097024005003) 14. Kim, H. K., & Davis, K. E. (2009). Toward a comprehensive theory of problematic Internet use: Evaluating the role of self-esteem, anxiety, flow, and the self-rated importance of Internet activities. Computers in Human Behavior, 25(2), 490–500. https://doi.org/10.1016/j.chb.2008.11.001 [↗](https://doi.org/10.1016/j.chb.2008.11.001) 15. Moody, E. J. (2001). Internet use and its relationship to loneliness. CyberPsychology & Behavior, 4(3), 393–401. https://doi.org/10.1089/109493101300210303 [↗](https://doi.org/10.1089/109493101300210303) 16. Qiu, L., Lin, H., Ramsay, J., & Yang, F. (2012). You are what you tweet: Personality expression and perception on Twitter. Journal of Research in Personality, 46(6), 710–718. https://doi.org/10.1016/j.jrp.2012.08.008 [↗](https://doi.org/10.1016/j.jrp.2012.08.008) 17. Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., Ungar, L. H., & Seligman, M. E. P. (2015). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108(6), 934–952. https://doi.org/10.1037/pspp0000020 [↗](https://doi.org/10.1037/pspp0000020) 18. Koban, K., Stein, J.-P., Eckhardt, V., & Ohler, P. (2018). Quid pro quo in Web 2.0: Connecting personality traits and Facebook usage intensity to uncivil commenting intentions in public online discussions. Computers in Human Behavior, 79, 9–18. https://doi.org/10.1016/j.chb.2017.10.015 [↗](https://doi.org/10.1016/j.chb.2017.10.015) 19. Cassidy, J., Sherman, L. J., & Jones, J. D. (2012). What’s in a word? Linguistic characteristics of adult attachment interviews. Attachment & Human Development, 14(1), 11–32. https://doi.org/10.1080/14616734.2012.636649 [↗](https://doi.org/10.1080/14616734.2012.636649) 20. Lin, J., Mao, W., & Zeng, D. D. (2017). Personality-based refinement for sentiment classification in microblog. Knowledge-Based Systems, 132, 204–214. https://doi.org/10.1016/j.knosys.2017.06.031 [↗](https://doi.org/10.1016/j.knosys.2017.06.031) 21. Orehek, E., & Human, L. J. (2017). Self-expression on social media: Do tweets present accurate and positive portraits of impulsivity, self-esteem, and attachment style? Personality and Social Psychology Bulletin, 43(1), 60–70. https://doi.org/10.1177/0146167216675332 [↗](https://doi.org/10.1177/0146167216675332) 22. Adali, S., & Golbeck, J. (2012). Predicting personality with social behavior. In 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 302–309). IEEE. https://doi.org/10.1109/ASONAM.2012.58 [↗](https://doi.org/10.1109/asonam.2012.58) 23. Arnoux, P.-H., Xu, A., Boyette, N., Mahmud, J., Akkiraju, R., & Sinha, V. (2017). 25 tweets to know you: A new model to predict personality with social media. In 11th International AAAI Conference on Web and Social Media. https://doi.org/10.1609/icwsm.v11i1.14963 [↗](https://doi.org/10.1609/icwsm.v11i1.14963) 24. Celli, F., Ghosh, A., Alam, F., & Riccardi, G. (2016). In the mood for sharing contents: Emotions, personality and interaction styles in the diffusion of news. Information Processing & Management, 52(1), 93–98. https://doi.org/10.1016/j.ipm.2015.08.002 [↗](https://doi.org/10.1016/j.ipm.2015.08.002) 25. Golbeck, J., Robles, C., Edmondson, M., & Turner, K. (2011). Predicting personality from Twitter. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom) (pp. 149–156). IEEE. https://doi.org/10.1109/PASSAT/SocialCom.2011.33 [↗](https://doi.org/10.1109/passat/socialcom.2011.33) 26. Lima, A. C. E. S., & De Castro, L. N. (2014). A multi-label, semi-supervised classification approach applied to personality prediction in social media. Neural Networks, 58, 122–130. https://doi.org/10.1016/j.neunet.2014.05.020 [↗](https://doi.org/10.1016/j.neunet.2014.05.020) 27. Markovikj, D., Gievska, S., Kosinski, M., & Stillwell, D. (2013). Mining Facebook data for predictive personality modeling. In 7th International AAAI Conference on Weblogs and Social Media. https://doi.org/10.1609/icwsm.v7i2.14466 [↗](https://doi.org/10.1609/icwsm.v7i2.14466) 28. Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011). Our Twitter profiles, our selves: Predicting personality with Twitter. In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing (pp. 180–185). https://doi.org/10.1109/PASSAT/SocialCom.2011.26 [↗](https://doi.org/10.1109/passat/socialcom.2011.26) 29. Souri, A., Hosseinpour, S., & Rahmani, A. M. (2018). Personality classification based on profiles of social networks’ users and the five-factor model of personality. Human-centric Computing and Information Sciences, 8(1), 1–15. https://doi.org/10.1186/s13673-018-0147-4 [↗](https://doi.org/10.1186/s13673-018-0147-4) 30. Tandera, T., Suhartono, D., Wongso, R., & Prasetio, Y. L. (2017). Personality prediction system from Facebook users. Procedia Computer Science, 116, 604–611. https://doi.org/10.1016/j.procs.2017.10.016 [↗](https://doi.org/10.1016/j.procs.2017.10.016) 31. Verhoeven, B., Daelemans, W., & De Smedt, T. (2013). Ensemble methods for personality recognition. In 7th International AAAI Conference on Weblogs and Social Media. https://doi.org/10.1609/icwsm.v7i2.14465 [↗](https://doi.org/10.1609/icwsm.v7i2.14465) 32. Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2018). Personality predictions based on user behavior on the Facebook social media platform. IEEE Access, 6, 61959–61969. https://doi.org/10.1109/ACCESS.2018.2876502 [↗](https://doi.org/10.1109/access.2018.2876502) 33. Wang, S., Cui, L., Liu, L., Lu, X., & Li, Q. (2020). Personality traits prediction based on users’ digital footprints in social networks via attention RNN. In 2020 IEEE International Conference on Services Computing (SCC’20) (pp. 54–56). IEEE. https://doi.org/10.1109/SCC49832.2020.00015 [↗](https://doi.org/10.1109/scc49832.2020.00015) 34. Zhao, J., Zeng, D., Xiao, Y., Che, L., & Wang, M. (2020). User personality prediction based on topic preference and sentiment analysis using LSTM model. Pattern Recognition Letters, 138, 397–402. https://doi.org/10.1016/j.patrec.2020.07.035 [↗](https://doi.org/10.1016/j.patrec.2020.07.035) 35. Habash, N. Y. (2010). Introduction to Arabic natural language processing. Synthesis Lectures on Human Language Technologies, 3(1), 1–187. [find](https://scholar.google.com/scholar?q=Habash%2C+N%2E+Y%2E+%282010%29%2E+Introduction+to+Arabic+natural+language+processing%2E+Synthesis+Lectures+on+Human+Language+Technologies%2C+3%281%29%2C+1%E2%80%93187%2E) 36. Liu, L., Preotiuc-Pietro, D., Samani, Z. R., Moghaddam, M. E., & Ungar, L. Analyzing personality through social media profile picture choice. https://doi.org/10.1609/icwsm.v10i1.14738 [↗](https://doi.org/10.1609/icwsm.v10i1.14738) 37. Sumner, C., Byers, A., Boochever, R., & Park, G. J. (2012). Predicting dark triad personality traits from Twitter usage and a linguistic analysis of tweets. In 2012 11th International Conference on Machine Learning and Applications (ICMLA) (Vol. 2, pp. 386– 393). IEEE. https://doi.org/10.1109/ICMLA.2012.218 [↗](https://doi.org/10.1109/icmla.2012.218) 38. Schwartz, H. A., Eichstaedt, J. C., Dziurzynski, L., Kern, M. L., Seligman, M. E. P., Ungar, L. H., Blanco, E., Kosinski, M., & Stillwell, D. (2013). Toward personality insights from language exploration in social media. AAAI Spring Symposium Series. https://doi.org/10.1371/journal.pone.0073791 [↗](https://doi.org/10.1371/journal.pone.0073791) 39. Farnadi, G., Zoghbi, S., Moens, M.-F., & De Cock, M. (2013). Recognizing personality traits using Facebook status updates. Seventh International AAAI Conference on Weblogs and Social Media. https://doi.org/10.1609/icwsm.v7i2.14470 [↗](https://doi.org/10.1609/icwsm.v7i2.14470) 40. Celli, F., Pianesi, F., Stillwell, D., & Kosinski, M. (2013). Workshop on Computational Personality Recognition: Shared Task. Seventh International AAAI Conference on Weblogs and Social Media. https://doi.org/10.1609/icwsm.v7i2.14467 [↗](https://doi.org/10.1609/icwsm.v7i2.14467) 41. Peats.de. (n.d.). Big Five - die Persönlichkeit in fünf Dimensionen. Retrieved October 28, 2021, from [https://peats.de/article/big-five-die-personlichkeit-in-funf-dimensionen](https://peats.de/article/big-five-die-personlichkeit-in-funf-dimensionen) [↗](https://peats.de/article/big-five-die-personlichkeit-in-funf-dimensionen) 42. Myers, I. B., & Myers, P. B. (1995). Gifts differing: Understanding personality type. Davies-Black Publishing. [find](https://scholar.google.com/scholar?q=Myers%2C+I%2E+B%2E%2C+%26+Myers%2C+P%2E+B%2E+%281995%29%2E+Gifts+differing%3A+Understanding+personality+type%2E+Davies%2DBlack+Publishing%2E) 43. The Myers & Briggs Foundation. (n.d.). MBTI® Basics. Retrieved October 28, 2021, from [https://www.myersbriggs.org/my-mbti-personality-type/mbti-basics/](https://www.myersbriggs.org/my-mbti-personality-type/mbti-basics/) [↗](https://www.myersbriggs.org/my-mbti-personality-type/mbti-basics/) 44. The Myers & Briggs Company. (n.d.). Myers-Briggs Type Indicator® (MBTI®) | Official Myers Briggs Personality Test. Retrieved October 28, 2021, from [https://www.themyersbriggs.com/](https://www.themyersbriggs.com/) [↗](https://www.themyersbriggs.com/) 45. Huber, D., Kaufmann, H., & Steinmann, M. (2017). The missing link: The innovation gap. In Bridging the Innovation Gap (pp. 21– 41). Springer International Publishing. https://doi.org/10.1007/978-3-319-55498-3_3 [↗](https://doi.org/10.1007/978-3-319-55498-3_3) 46. Isaak, J., & Hana, M. J. (2018). User data privacy: Facebook, Cambridge Analytica, and privacy protection. IEEE Computer, 51. https://doi.org/10.1109/MC.2018.3191268 [↗](https://doi.org/10.1109/mc.2018.3191268) 47. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415-444. https://doi.org/10.1146/annurev.soc.27.1.415 [↗](https://doi.org/10.1146/annurev.soc.27.1.415) 48. Sivic, J. (2009). Efficient visual search of videos cast as text retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4), 591–605. https://doi.org/10.1109/TPAMI.2008.111 [↗](https://doi.org/10.1109/tpami.2008.111) 49. Rajaraman, A., & Ullman, J. D. (2011). Data mining. In Mining of Massive Datasets (pp. 1–17). [find](https://scholar.google.com/scholar?q=Rajaraman%2C+A%2E%2C+%26+Ullman%2C+J%2E+D%2E+%282011%29%2E+Data+mining%2E+In+Mining+of+Massive+Datasets+%28pp%2E+1%E2%80%9317%29%2E) 50. Mikolov, T., et al. (2013). Efficient estimation of word representations in vector space. In International Conference on Learning Representations (ICLR2013). https://doi.org/10.48550/arXiv.1301.3781 [↗](https://doi.org/10.48550/arxiv.1301.3781) 51. Golbeck, J., Robles, C., & Turner, K. (2011). Predicting personality with social media. In CHI’11 Extended Abstracts on Human Factors in Computing Systems (pp. 253–262). https://doi.org/10.1145/1979742.1979614 [↗](https://doi.org/10.1145/1979742.1979614) 52. NERIS Analytics Limited. (2011-2023). Personality types. Retrieved from [https://www.16personalities.com/personality-types](https://www.16personalities.com/personality-types) [↗](https://www.16personalities.com/personality-types) 53. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805v2. https://doi.org/10.48550/arXiv.1810.04805 [↗](https://doi.org/10.48550/arxiv.1810.04805) 54. Antoun, W., Baly, F., & Hajj, H. (2020). AraBERT: Transformer-based model for Arabic language understanding. In LREC 2020 Workshop Language Resources and Evaluation Conference (pp. 11–16). https://doi.org/10.48550/arXiv.2003.00104 [↗](https://doi.org/10.48550/arxiv.2003.00104) [our card](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/)