Week          Topic                                                      Expected hours for preparing this course outside the classroom

1                Introduction                                                                   3

2               Import/Export data in R                                                   3

3               Processing unstructured and textual data in R                    6

4               Loops in R (e.g., for, apply, sapply, lapply)                          9

5               Web crawler for plain texts                                               7

6               Web Crawler for webpages with CSS                                 9

7               Midterm Exam

8               Data preprocessing and descriptive statistical analysis         7

                 (e.g., word frequency, word clouds, etc)

9               SVD (singular value decomposition) and LSA (latent             9

                 semantic analysis) I

10              SVD (singular value decomposition) and LSA (latent             9

                 semantic analysis) II

11              Word segmentation for Chinese documents                         9

12             Classification with SVM (suport vector machine)                   9

13             Regression and SVM (suport vector machine)                       9

14             Stylometric analysis in R                                                    9

15             Association rules in R                                                        9

16             Social network on Facebook (or other social media)              9

17             API of Google Maps                                                           6

18     Final Exam

My TA's email: 106752011@nccu.edu.tw

The final score is the mean of the scores of all home assignments. For those who finish a project by the end of semester, extra bonus points (up to 10) will be added to their final score.

References (and more to come)

Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Political Science, 348, 1130-1132.

Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329, 1194. doi: 10.1126/science.1185231

Centola, D. (2011). An experimental study of homophily in the adoption of health behavior. Science, 334, 1269-1271.

Coviello, L., Sohn, Y., Kramer, A. D. I., Marlow, C., Franceschetti, M., Christakis, N. A., & Fowler, J. H. (2014). Detecting emotional contagion in massive social networks. PLOS ONE 9(3): e90315. doi:10.1371/journal.pone.0090315

Dyevre, A. (2015). Promise and pitfalls of automated text-scaling techniques for the analysis of judical opinions. https://www.researchgate.net/publication/279764826

Frewen, P. A., Schmittmann, V. D., Bringmann, L. F., & borsboom, D. (2013). Perceived causal relations between anziety, posttraumatic stress and depresion: Extension to moderation, mediation, and network analysis. European Journal of Psychotraumatology, 4: 20656 - http://dx.doi.org/10.3402/ejpt.v4i0.20656

Harlow, L. L., & Oswald, F. L. (2016). Big data in psychology: Introduction to the special issue. Psychological Methods, 21, 447-457.

Kern, M. L., Park, G., Eichstaedt, J. C., Schwartz, H. A., Sap, M., Smith, L. K., & Ungar, L. H. (2016, August 8). Gaining Insights From Social Media Language: Methodologies and Challenges. Psychological Methods. Advance online publication. http://dx.doi.org/10.1037/met0000091

Kern, M. L., Eichstaedt, J. C., Schwartz, H. A., Dziurzynski, L., Ungar, L. H., Stillwell, D. J., ... Seligman, M. E. P. (2014a). The online social self: An open vocabulary approach to personality. Assessment, 21, 158-169. http://dx.doi.org/10.1177/1073191113514104

Kern, M. L., Eichstaedt, J. C., Schwartz, H. A., Park, G., Ungar, L. H., Stillwell, D. J., . . . Seligman, M. E. P. (2014b). From “Sooo excited!!!” to “So proud”: Using language to study development. Developmental Psychology, 50, 178–188. http://dx.doi.org/10.1037/a0035048

Kosinski, M., Matz, S., Gosling, S. D., & Stillwell, D. (2015). Facebook as a research tool for the social sciences. American Psychologists, 70, 543-556. doi: 10.1037/a0039210.

Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital recores of human behavior. PNAS, 110, 5802-5805.

Kosinski, M., Wang, Y., Lakkaraju, H., & Leskovec, J. (2016). Ming big data to extract patterns and predict real-life outcomes. Psychological Methods, 21, 493-506.

Markowetz, A., Blaszkiewicz, K., Montag, C., Switala, C., & Schlaepfer, T. E. (2014). Psycho-informatics: Big data shaping modern psychometrics. Medical Hypotheses, 82, 405-411. 

Moat, H. S., Curme, C., Avakian, A., kenett, D. Y., Stanley, H. E., & Preis, T. (2013). Quantifying Wikipedia usage patterns before stock market moves. Scientific Report, 3: 1801, doi: 10.1038/srep01801

Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., . . . Seligman, M. E. P. (2015). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108, 934 –952. http://dx.doi.org/10.1037/ pspp0000020

Schmittmann, V. D., Cramer, A. O. J., Waldorp, L. J., & Epskamp, S. (2013). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31, 43-53.

Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., . . . Ungar, L. H. (2013b). Personality, gender, and age in the language of social media: The open-vocabulary approach. PLoS ONE, 8, e73791. http://dx.doi.org/10.1371/journal.pone.0073791

Schwartz, H. A., Park, G. J., Sap, M., Weingarten, E., Eichstaedt, J. C., Kern, M. L., . . . Ungar, L. H. (2015). Extracting human temporal orientation from Facebook language. In Proceedings of the 2015 Con- ference of the North American chap. of the Association for Computa- tional Linguistics—Human Language Technologies. Stroudsburg, PA: Association for Computational Linguistics. http://dx.doi.org/10.3115/v1/ N15-1044

Zehner, F., Goldhammer, F., & Salzer, C. (2018). Automatically analyzing text rsponses for exploring gender-specific cognitions in PISA. Large-scal Assessments, in Education, 6, https://doi.org/10.1186/s40536?018?0060?3