Type of Credit: Elective
Credit(s)
Number of Students
Since the era of Web 2.0, people have got used to sharing their lives on miscellaneous platforms on the internet, such as bulletin board systems, personal blogs, social media, and so on. The texts, music, and photographs that people post on social media thus become an enormous database of human behaviors for psychologists to study. In this course, I will focus on introducing how to collect data from websites, how to analyze those data in appropriate ways, and how to do text analysis (including sentiment analysis). To these ends, R is a good tool. Therefore, I will also teach R in this class. Of course, it would be even better if students can start learning it on their own before class. There will be home assignments for students every two weeks. Project is optional, however, the students who submit a project by the end of this semester will gain extra bonus points.
能力項目說明
The goals of this course is to introduce how the algorithms/models in maching learning can help psychologists do research with the big data, sepcifically those collected from webpages. Therefore, students after this semester are expected to be able to (1) make their own web crawller to collect online data by reading HTML codes or by calling the API of some applications, (2) do text analysis for textual materials, including making word clouds with word frequency and latent semantic analysis (LSA), (3) do social network analysis such as visualizing the relationships between friends on Facebook, and (4) do visualization for the happenings of events in terms of time and geographic information.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
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 Data preprocessing and descriptive statistical analysis 7
(e.g., word frequency, word clouds, etc)
8 SVD (singular value decomposition) and LSA (latent 9
semantic analysis) I
9 Midterm Exam
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 Oral presentation for project 6
18 Oral presentation for project 6
The final score consists of two parts: homeworks (70%) and project (30%). Almost every week, we will have a home assignment. The final homework score is the mean of all scores of the home assignments. In addition to homeworks, students are requested to finish a project by the end of semeter. The project objective can be quite flexible, as long as it can demonstrate how well students can conduct a small study of social media.
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