Araştırma Makalesi
BibTex RIS Kaynak Göster

Facebook Kullanıcılarının Kişilik, İlgi Alanı ve Yaşam Memnuniyeti Analizi

Yıl 2019, , 87 - 94, 05.11.2019
https://doi.org/10.18506/anemon.615115

Öz

Günümüzde,
e-ticaret sistemleri tarafından toplanan veriler, müşterilerin geleneksel
istatistiksel ve demografik bilgilerinin ötesindedir. Sağlanan yorumlar,
beğeniler, etiketler, fotoğraflar vb. sayesinde bir uygulamanın kullanıcıları
hakkında daha fazla bilgi sahibi olmak mümkün olmaktadir. Kullanıcı
davranışlarını ve özelliklerini analiz etmek amacıyla, bu çalışmada, myPersonality
veri setinin kişilik,  Zeka
Katsayısı,Yaşam Memnuniyeti Ölçeği puanlarına sahip kullanıcıları içeren 3
altkümesi, kullanıcıların beğendiği 
kayıtlar ile birleştirilerek, on iki ilgi kategorisine ayrılımış ve
Apriori algoritması kullanarak test edilmiştir. Sonuç olarak, türetilmiş
ilişkilendirme kuralları sayesinde, Facebook kullanıcılarının kişisel
özellikleri ile ilgi kategorileri arasındaki ilişkiler elde edilmiştir.

Kaynakça

  • Agrawal, R., Imielinski, T., Swami, A. (1993, May). Mi-ng associations between sets of items in massive databases. In Proc. 1993 ACM-SIGMOD Int. Conf (pp. 207-216).
  • Alam, F., Stepanov, Evgeny A.; Rıccardı, Giusepp. (2013, June). Personality traits recog-tion on social network-facebook. In Seventh International AAAI Conference on Weblogs and Social Media.
  • Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., & Stillwell, D. (2012, June). Personality and patterns of Facebook usage. In Proceedings of the 4th annual ACM web science conference (pp. 24-32). ACM.
  • Brin, S., Motwa-, R., Ullman, J. D., & Tsur, S.S. (1997a). Dynamic itemset counting and implication rules for market basket data. Acm Sigmod Record, 26(2), 255-264.
  • Brin, S., Motwa-, R., Ullman, J. D., & Tsur, S.( 1997b) Dynamic itemset counting and implication rules for market basket data. In SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, pages 255-264, Tucson, Arizona, USA,
  • Brusilovski, P., Alfred K., and Wolfgang N. (2007). The adaptive web: methods and strategies of web personalization (Vol. 4321). Springer Science & Business Media.
  • Cantador, I. Fernández-Tobías, I., Bellogín, A. (2013). Relating personality types with user preferences in multiple entertainment domains. In CEUR workshop proceedings.
  • Celli, F., Pianesi, F., Stillwell, D., & Kosinski, M. (2013, June). Workshop on computational personality recog-tion: Shared task. In Seventh International AAAI Conference on Weblogs and Social Media.
  • Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling tech-que. Journal of artificial intelligence research, 16, 321-357.
  • Costa, P.T., McCrae, Robert. R. (2008). The revised neo personality inventory (neo-pi-r)." The SAGE handbook of personality theory and assessment 2, 2(2), 179-198.
  • Digman, J.M. (1989). Five robust trait dimensions: Development, stability, and utility. Journal of personality, 57(2), 195-214.
  • Farnadi, G., Zoghbi, S., Moens, M. F., & De Cock, M. (2013, June). Recog-sing personality traits using Facebook status updates. In Seventh International AAAI Conference on Weblogs and Social Media.
  • Garner, S. R. (1995, April). Weka: The waikato environment for knowledge analysis. In Proceedings of the New Zealand computer science research students conference (pp. 57-64).
  • Golbeck, J., Robles, C. T. (2011, May). Predicting personality with social media. In CHI'11 extended abstracts on human factors in computing systems (pp. 253-262). ACM.
  • Goldberg, L.R., Johnson, J.A., Eber, H.W., Hogan, R., Ashton, M.C., Clo-nger, C.R., & Gough, H.G. (2006). The international personality item pool and the future of public-domain personality measures. Journal of Research in personality, 40(1), 84-96.
  • HU, R., PU, P. (2011, October). Enhancing collaborative filtering systems with personality information. In Proceedings of the fifth ACM conference on Recommender systems (pp. 197-204). ACM.
  • Kaufman, S.B., Quilty, L. C., Grazioplene, R.G., Hirsh, J. B., Gray, J. R., Peterson, J. B., & DeYoung, C. G. (2016). Openness to experience and intellect differentially predict creative achievement in the arts and sciences. Journal of personality, 84(2), 248-258.
  • Kosinski, M., & Stillwell, D. J. (2011). myPersonality research wiki. myPersonality project. Unpublished manuscript.
  • Markovikj, D., Gievska, S., Kosinski, M., & Stillwell, D. J. (2013, June). Mi-ng facebook data for predictive personality modeling. In Seventh International AAAI Conference on Weblogs and Social Media.
  • Pennebaker, J. W., Francıs, M. E., Booth, R. J. (2001). Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates, 71(2001)
  • Pennebaker, J.W.; King, L. A. (1999). Linguistic styles: Language use as an individual difference. Journal of personality and social psychology, 77(6), 1296.
  • Piatetsky-Shapiro, G. (1991). Discovery, analysis, and presentation of strong rules. Knowledge discovery in databases, 229-238.
  • Roshchina, A. (2012). TWIN: Personality-based Recommender System. Institute of Technology Tallaght, Dublin.
  • Schacter, D. L., Gilbert, D. T., & Wegner, D. M. (2010). Implicit memory and explicit memory. Psychology, 238.
  • Schimmack, U., Radhakrishnan, P., Oishi, S., Dzokoto, V., & Ahadi, S. (2002). Culture, personality, and subjective well-being: Integrating process models of life satisfaction. Journal of personality and social psychology, 82(4), 582.
  • 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.
  • Sternberg, R.J., Kaufman, Scott B. (2011). The Cambridge handbook of intelligence. Cambridge U-versity Press.
  • Witten I.H., Frank E.( 2000). Data Mi-ng: Practical Machine Lear-ng Tool and Tech-que with Java Implementation. Morgan Kaufmann; 2000.
  • Yarko-, T. (2010). Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of research in personality, 44(3), 363-373.
  • Zhan L., Sun Y., Wang, N., & Zhang, X. (2016). Understanding the influence of social media on people’s life satisfaction through two competing explanatory mecha-sms. Aslib Journal of Information Management, 68(3), 347-361.

The Relationship among Personality, Interest, and Life Satisfaction of Facebook Users

Yıl 2019, , 87 - 94, 05.11.2019
https://doi.org/10.18506/anemon.615115

Öz



Today, the data collected by e-commerce systems are beyond the traditional
statistical and demographic information of customers. The data provided by
comments, likes, tags, photos and more in social media, enable marketing
researchers to better evaluate the behavior of the users. Therefore to analyze
user behavior and characteristics, in this paper, 3 balanced subset of
myPersonality Facebook dataset is tested by Apriori algorithm. As a result the
relationship among personality traits, intelligence quotient , satisfaction
with life scale and the assigned 12 interest categories of users are analyzed. 



Kaynakça

  • Agrawal, R., Imielinski, T., Swami, A. (1993, May). Mi-ng associations between sets of items in massive databases. In Proc. 1993 ACM-SIGMOD Int. Conf (pp. 207-216).
  • Alam, F., Stepanov, Evgeny A.; Rıccardı, Giusepp. (2013, June). Personality traits recog-tion on social network-facebook. In Seventh International AAAI Conference on Weblogs and Social Media.
  • Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., & Stillwell, D. (2012, June). Personality and patterns of Facebook usage. In Proceedings of the 4th annual ACM web science conference (pp. 24-32). ACM.
  • Brin, S., Motwa-, R., Ullman, J. D., & Tsur, S.S. (1997a). Dynamic itemset counting and implication rules for market basket data. Acm Sigmod Record, 26(2), 255-264.
  • Brin, S., Motwa-, R., Ullman, J. D., & Tsur, S.( 1997b) Dynamic itemset counting and implication rules for market basket data. In SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, pages 255-264, Tucson, Arizona, USA,
  • Brusilovski, P., Alfred K., and Wolfgang N. (2007). The adaptive web: methods and strategies of web personalization (Vol. 4321). Springer Science & Business Media.
  • Cantador, I. Fernández-Tobías, I., Bellogín, A. (2013). Relating personality types with user preferences in multiple entertainment domains. In CEUR workshop proceedings.
  • Celli, F., Pianesi, F., Stillwell, D., & Kosinski, M. (2013, June). Workshop on computational personality recog-tion: Shared task. In Seventh International AAAI Conference on Weblogs and Social Media.
  • Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling tech-que. Journal of artificial intelligence research, 16, 321-357.
  • Costa, P.T., McCrae, Robert. R. (2008). The revised neo personality inventory (neo-pi-r)." The SAGE handbook of personality theory and assessment 2, 2(2), 179-198.
  • Digman, J.M. (1989). Five robust trait dimensions: Development, stability, and utility. Journal of personality, 57(2), 195-214.
  • Farnadi, G., Zoghbi, S., Moens, M. F., & De Cock, M. (2013, June). Recog-sing personality traits using Facebook status updates. In Seventh International AAAI Conference on Weblogs and Social Media.
  • Garner, S. R. (1995, April). Weka: The waikato environment for knowledge analysis. In Proceedings of the New Zealand computer science research students conference (pp. 57-64).
  • Golbeck, J., Robles, C. T. (2011, May). Predicting personality with social media. In CHI'11 extended abstracts on human factors in computing systems (pp. 253-262). ACM.
  • Goldberg, L.R., Johnson, J.A., Eber, H.W., Hogan, R., Ashton, M.C., Clo-nger, C.R., & Gough, H.G. (2006). The international personality item pool and the future of public-domain personality measures. Journal of Research in personality, 40(1), 84-96.
  • HU, R., PU, P. (2011, October). Enhancing collaborative filtering systems with personality information. In Proceedings of the fifth ACM conference on Recommender systems (pp. 197-204). ACM.
  • Kaufman, S.B., Quilty, L. C., Grazioplene, R.G., Hirsh, J. B., Gray, J. R., Peterson, J. B., & DeYoung, C. G. (2016). Openness to experience and intellect differentially predict creative achievement in the arts and sciences. Journal of personality, 84(2), 248-258.
  • Kosinski, M., & Stillwell, D. J. (2011). myPersonality research wiki. myPersonality project. Unpublished manuscript.
  • Markovikj, D., Gievska, S., Kosinski, M., & Stillwell, D. J. (2013, June). Mi-ng facebook data for predictive personality modeling. In Seventh International AAAI Conference on Weblogs and Social Media.
  • Pennebaker, J. W., Francıs, M. E., Booth, R. J. (2001). Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates, 71(2001)
  • Pennebaker, J.W.; King, L. A. (1999). Linguistic styles: Language use as an individual difference. Journal of personality and social psychology, 77(6), 1296.
  • Piatetsky-Shapiro, G. (1991). Discovery, analysis, and presentation of strong rules. Knowledge discovery in databases, 229-238.
  • Roshchina, A. (2012). TWIN: Personality-based Recommender System. Institute of Technology Tallaght, Dublin.
  • Schacter, D. L., Gilbert, D. T., & Wegner, D. M. (2010). Implicit memory and explicit memory. Psychology, 238.
  • Schimmack, U., Radhakrishnan, P., Oishi, S., Dzokoto, V., & Ahadi, S. (2002). Culture, personality, and subjective well-being: Integrating process models of life satisfaction. Journal of personality and social psychology, 82(4), 582.
  • 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.
  • Sternberg, R.J., Kaufman, Scott B. (2011). The Cambridge handbook of intelligence. Cambridge U-versity Press.
  • Witten I.H., Frank E.( 2000). Data Mi-ng: Practical Machine Lear-ng Tool and Tech-que with Java Implementation. Morgan Kaufmann; 2000.
  • Yarko-, T. (2010). Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of research in personality, 44(3), 363-373.
  • Zhan L., Sun Y., Wang, N., & Zhang, X. (2016). Understanding the influence of social media on people’s life satisfaction through two competing explanatory mecha-sms. Aslib Journal of Information Management, 68(3), 347-361.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi
Yazarlar

Vesile Evrim 0000-0001-7733-5229

Yahya Nissoul Bu kişi benim 0000-0001-7017-5162

Yayımlanma Tarihi 5 Kasım 2019
Kabul Tarihi 1 Kasım 2019
Yayımlandığı Sayı Yıl 2019

Kaynak Göster

APA Evrim, V., & Nissoul, Y. (2019). The Relationship among Personality, Interest, and Life Satisfaction of Facebook Users. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 7, 87-94. https://doi.org/10.18506/anemon.615115

Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.