-
Notifications
You must be signed in to change notification settings - Fork 3
/
schedule.Rmd
412 lines (257 loc) · 24 KB
/
schedule.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
---
title: Course outline and readings
---
Note: slides may be updated as the course progresses.
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-1" class="panel-title">
<a data-toggle="collapse" href="#collapse1">Week 1: 08/24. Introduction</a>
</h2>
</div>
<div id="collapse1" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Introductions and course overview. What is Computational Social Science? Introduction to version control and GitHub.
[LINK TO SLIDES (.pdf)](slides/01-intro.pdf)
#### Background reading
- Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., … & Jebara, T. (2009). [Life in the network: the coming age of computational social science.](http://fowler.ucsd.edu/computational_social_science.pdf) *Science*, 323(5915), 721-3.
- Lazer, D., Hargittai, E., Freelon, D., Gonzalez-Bailon, S., Munger, K., Ognyanova, K., & Radford, J. (2021). [Meaningful measures of human society in the twenty-first century.](https://www.nature.com/articles/s41586-021-03660-7) *Nature*, 1-8.
- Grimmer, J. (2015). [We are all social scientists now: how big data, machine learning, and causal inference work together.](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1049096514001784) *PS: Political Science & Politics*, 48(1), 80-83.
</div>
</div>
</div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-2" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 2: 08/31. Ethics. </a>
</h2>
</div>
<div id="collapse2" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Computational social science: research opportunities and challenges. Ethics of social science research in the digital age. Big data, big bias?
[LINK TO SLIDES (.pdf)](slides/03-css-ethics.pdf)
#### Background reading
- Barberá, P. & and Steinert-Threlkeld, Z. (2020). [How to Use Social Media Data for Political Science Research.](http://pablobarbera.com/static/social-media-data-generators.pdf) Curini, L., and Franzese, R. (eds) The SAGE Handbook of Research Methods in Political Science and International Relations, London: Sage, 404-423.
- Lazer, D. M., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., ... & Wagner, C. (2020). [Computational social science: Obstacles and opportunities.](https://science.sciencemag.org/content/369/6507/1060.summary) *Science*, 369(6507), 1060-1062.
- [Chapter 6](http://www.bitbybitbook.com/en/ethics/) of Salganik, M. (2017). [Bit by Bit: Social Research in the Digital Age](http://www.bitbybitbook.com/). Princeton, NJ: Princeton University Press. Open review edition.
- Persily, N., & Tucker, J. A. (2020). [Conclusion: The Challenges and Opportunities for Social Media Research.](https://fsi-live.s3.us-west-1.amazonaws.com/s3fs-public/persily_tucker_conclusion.pdf) In *Social Media and Democracy: The State of the Field, Prospects for Reform*, 313-324.
#### Readings for discussion
- Hargittai, E. (2018). [Potential Biases in Big Data: Omitted Voices on Social Media.](https://journals.sagepub.com/doi/abs/10.1177/0894439318788322) *Social Science Computer Review*, forthcoming.
- Vlasceanu, M., & Amodio, D. M. (2022). [Propagation of Societal Gender Inequality by Internet Search Algorithms.](https://www.pnas.org/doi/10.1073/pnas.2204529119) *Proceedings of the National Academy of Sciences*, 119 (29) e2204529119.
- Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). [Experimental evidence of massive-scale emotional contagion through social networks.](http://www.pnas.org/content/111/24/8788.short) *Proceedings of the National Academy of Sciences*, 111(24), 8788-8790.
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-3" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 3: 09/07. Online experiments.</a>
</h2>
</div>
<div id="collapse3" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Large-scale online experiments. Data collection: automated data collection from the web.
[LINK TO SLIDES (.pdf)](slides/05-experiments.pdf)
#### Background reading
- Guess, A. M. (2021). [Experiments Using Social Media Data.](https://osf.io/gvbyc/) Advances in Experimental Political Science, 184.
#### Readings for discussion
- Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). [A 61-million-person experiment in social influence and political mobilization](https://www.nature.com/nature/journal/v489/n7415/abs/nature11421.html). *Nature*, 489(7415), 295-298.
- King, G., Pan, J., & Roberts, M. E. (2014). [Reverse-engineering censorship in China: Randomized experimentation and participant observation.](http://scholar.harvard.edu/files/gking/files/experiment_0.pdf) *Science*, 345(6199), 1251722.
- Siegel, A. A., & Badaan, V. (2020). [#No2Sectarianism: Experimental approaches to reducing sectarian hate speech online.](https://www.cambridge.org/core/journals/american-political-science-review/article/abs/no2sectarianism-experimental-approaches-to-reducing-sectarian-hate-speech-online/27157485824C8E071CB2DD3E26012EA3) *American Political Science Review*, 114(3), 837-855.
- Asimovic, N., Nagler, J., Bonneau, R., & Tucker, J. A. (2021). [Testing the effects of Facebook usage in an ethnically polarized setting.](https://www.pnas.org/content/pnas/118/25/e2022819118.full.pdf) *Proceedings of the National Academy of Sciences*, 118(25).
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-4" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 4: 09/14. Text (I). </a>
</h2>
</div>
<div id="collapse4" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Introduction to automated text analysis: key concepts; selecting documents and features. Sources of textual data. String manipulation in R. Regular expressions. Text processing with quanteda.
[LINK TO SLIDES (.pdf)](slides/07-text-intro.pdf)
#### Background reading
- Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. **Chapter 2.**
- Benoit, K. (2020). [Text as data: an overview.](https://kenbenoit.net/pdfs/28%20Benoit%20Text%20as%20Data%20draft%202.pdf) In Curini, L., and Franzese, R. (eds) *The SAGE Handbook of Research Methods in Political Science and International Relations*, London: Sage.
- Wilkerson, J. and Casas, A. (2017). [Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges.](https://www.annualreviews.org/doi/abs/10.1146/annurev-polisci-052615-025542?journalCode=polisci) *Annual Review of Political Science*, 20, 529:544.
#### No readings for discussion this week
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-5" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 5: 09/21. Text (II). </a>
</h2>
</div>
<div id="collapse5" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Describing and comparing textual data.
[LINK TO SLIDES (.pdf)](slides/09-text-descriptives.pdf)
#### Background reading
- Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. **Chapter 5, 7, and 11.**
- Denny, M. J., & Spirling, A. (2018). [Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it.](https://www.cambridge.org/core/journals/political-analysis/article/text-preprocessing-for-unsupervised-learning-why-it-matters-when-it-misleads-and-what-to-do-about-it/AA7D4DE0AA6AB208502515AE3EC6989E) *Political Analysis*, 26(2): 168-189.
#### Readings for discussion
- Carter, E. B., & Carter, B. L. (2021). [Questioning More: RT, Outward-Facing Propaganda, and the Post-West World Order.](http://www.erinbcarter.org/documents/RT.pdf) *Security Studies*, 30(1), 49-78.
- Liu, A. H. (2021). [Pronoun Usage as a Measure of Power Personalization: A General Theory with Evidence from the Chinese-Speaking World.](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/B8A422F904339BCB902CA318AAE810A3/S0007123421000181a.pdf/div-class-title-pronoun-usage-as-a-measure-of-power-personalization-a-general-theory-with-evidence-from-the-chinese-speaking-world-div.pdf) *British Journal of Political Science*, 1-18.
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-6" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 6: 09/28. Text (III).</a>
</h2>
</div>
<div id="collapse6" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Dictionary methods.
[LINK TO SLIDES (.pdf)](slides/10-dictionaries.pdf)
#### Background reading
- Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. **Chapters 15 and 16.**
- González-Bailón, S., & Paltoglou, G. (2015). [Signals of public opinion in online communication: A comparison of methods and data sources.](https://journals.sagepub.com/doi/pdf/10.1177/0002716215569192?casa_token=a0RzWG5Ax84AAAAA:1Bz3VYxGncl41uzHDY8GAiQiXUJN10ExZGtpDuu2n-qxPwI20hn-l-omjrI5SkwB8X8ToB-6K1M_) *The ANNALS of the American Academy of Political and Social Science*, 659(1), 95-107.
- Barberá, P., Boydstun, A. E., Linn, S., McMahon, R., & Nagler, J. (2021). [Automated text classification of news articles: A practical guide.](https://www.cambridge.org/core/journals/political-analysis/article/abs/automated-text-classification-of-news-articles-a-practical-guide/10462DB284B1CD80C0FAE796AD786BC6) *Political Analysis*, 29(1), 19-42.
#### Readings for discussion
- Engler, S., Gessler, T., Abou-Chadi, T., & Leemann, L. (2022). [Democracy challenged: how parties politicize different democratic principles.](https://www.tandfonline.com/doi/pdf/10.1080/13501763.2022.2099956?needAccess=true) *Journal of European Public Policy*, 1-23.
- Rathje, S., Van Bavel, J. J., & van der Linden, S. (2021). [Out-group animosity drives engagement on social media.](https://www.pnas.org/content/pnas/118/26/e2024292118.full.pdf) *Proceedings of the National Academy of Sciences*, 118(26).
- Tausczik, Y. R., & Pennebaker, J. W. (2010). [The psychological meaning of words: LIWC and computerized text analysis methods.](http://journals.sagepub.com/doi/abs/10.1177/0261927X09351676) *Journal of language and social psychology*, 29(1), 24-54.
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-7" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 7: 10/05. Text (IV).</a>
</h2>
</div>
<div id="collapse7" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Supervised machine learning applied to text classification. Crowd-sourcing the creation of training datasets.
[LINK TO SLIDES (.pdf)](slides/11-machine-learning.pdf)
#### Background reading
- Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. **Chapters 17, 18, 19, and 20.**
- Benoit, K., Conway, D., Lauderdale, B. E., Laver, M., & Mikhaylov, S. (2016). [Crowd-sourced text analysis: reproducible and agile production of political data.](https://www.cambridge.org/core/journals/american-political-science-review/article/abs/crowd-sourced-text-analysis-reproducible-and-agile-production-of-political-data/EC674A9384A19CFA357BC2B525461AC3) *American Political Science Review*, 110(2), 278-295.
#### Readings for discussion
- Theocharis, Y., Barberá, P., Fazekas, Z., & Popa, S. A. (2020). [The dynamics of political incivility on Twitter.](https://journals.sagepub.com/doi/pdf/10.1177/2158244020919447) *Sage Open*, 10(2), 2158244020919447.
- Mitts, T., Phillips, G., & Walter, B. (2021). [Studying the Impact of ISIS Propaganda Campaigns.](https://www.dropbox.com/s/8uzexq3ot7kh4pp/propaganda.pdf?dl=0) *Journal of Politics*, forthcoming. (Read also [Appendix](https://www.dropbox.com/s/6x3bip56swtoc6z/propaganda_SI.pdf?dl=0).)
- Kim, J. Y. (2021). [Integrating human and machine coding to measure political issues in ethnic newspaper articles.](https://link.springer.com/article/10.1007/s42001-020-00097-2) *Journal of Computational Social Science*, 1-28.
- Eberhardt, M., Facchini, G., & Rueda, V. (2022). [Gender Differences in Reference Letters: Evidence from the Economics Job Market.](https://www.econstor.eu/bitstream/10419/252179/1/dp15055.pdf) *Working paper*.
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-8" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 8: 10/12. Text (V).</a>
</h2>
</div>
<div id="collapse8" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Unsupervised machine learning (topic models).
[LINK TO SLIDES (.pdf)](slides/12-topic-models.pdf)
#### Background reading
- Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. **Chapters 12 and 13.**
- Blei, D. M. (2012). [Probabilistic topic models.](http://dl.acm.org/citation.cfm?id=2133826) *Communications of the ACM*, 55(4), 77-84.
- Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010). [How to analyze political attention with minimal assumptions and costs.](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-5907.2009.00427.x) *American Journal of Political Science*, 54(1), 209-228.
#### Readings for discussion
- Barberá, P., Casas, A., Nagler, J., Egan, P. J., Bonneau, R., Jost, J. T., & Tucker, J. A. (2019). [Who leads? Who follows? Measuring issue attention and agenda setting by legislators and the mass public using social media data.](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/D855849CE288A241529E9EC2E4FBD3A8/S0003055419000352a.pdf/div-class-title-who-leads-who-follows-measuring-issue-attention-and-agenda-setting-by-legislators-and-the-mass-public-using-social-media-data-div.pdf) *American Political Science Review*, 113(4), 883-901.
- Terman, R. (2017). [Islamophobia and Media Portrayals of Muslim Women: A Computational Text Analysis of US News Coverage.](https://academic.oup.com/isq/article-abstract/61/3/489/4609692) *International Studies Quarterly*, 61(2): 489-502.
- Motolinia, L. (2021). [Electoral Accountability and Particularistic Legislation: Evidence from an Electoral Reform in Mexico.](https://www.cambridge.org/core/journals/american-political-science-review/article/abs/electoral-accountability-and-particularistic-legislation-evidence-from-an-electoral-reform-in-mexico/BCFD6B0C73B041C8C410594BDB232DB1) *American Political Science Review*, 115(1), 97-113.
- de Vries, E., Schoonvelde, M. & Schumacher, G. (2018). [No Longer Lost in Translation: Evidence that Google Translate Works for Comparative Bag-of-Words Text Applications.](https://www.cambridge.org/core/journals/political-analysis/article/no-longer-lost-in-translation-evidence-that-google-translate-works-for-comparative-bagofwords-text-applications/43CB03805973BB8AD567F7AE50E72CA6) *Political Analysis*, 26(4), 417-430.
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-9" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 9: 10/19. Text (VI).</a>
</h2>
</div>
<div id="collapse9" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Word embeddings.
[LINK TO SLIDES (.pdf)](slides/13-word-embeddings.pdf)
#### Background reading
- Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press. **Chapter 8.**
- Rodriguez, P., & Spirling, A. (2021). [Word Embeddings: What works, what doesn't, and how to tell the difference for applied research.](http://arthurspirling.org/documents/embed.pdf) *Journal of Politics*, forthcoming. See also [FAQ](https://github.com/ArthurSpirling/EmbeddingsPaper/blob/master/Project_FAQ/faq.md).
#### Readings for discussion
- Rodman, E. (2020). [A timely intervention: Tracking the changing meanings of political concepts with word vectors.](https://www.cambridge.org/core/journals/political-analysis/article/abs/timely-intervention-tracking-the-changing-meanings-of-political-concepts-with-word-vectors/DDF3B5833A12E673EEE24FBD9798679E) *Political Analysis*, 28(1), 87-111.
- Osnabrügge, M., Hobolt, S. B., & Rodon, T. (2021). [Playing to the Gallery: Emotive Rhetoric in Parliaments.](https://www.cambridge.org/core/journals/american-political-science-review/article/playing-to-the-gallery-emotive-rhetoric-in-parliaments/2A47C797136261391DA27F3A16F64886) *American Political Science Review*, 1-15.
- Card, D., Chang, S., Becker, C., Mendelsohn, J., Voigt, R., Boustan, L., ... & Jurafsky, D. (2022). [Computational analysis of 140 years of US political speeches reveals more positive but increasingly polarized framing of immigration.](https://www.pnas.org/doi/abs/10.1073/pnas.2120510119) *Proceedings of the National Academy of Sciences*, 119(31), e2120510119.
- Yang, E., & Roberts, M. E. (2021). [Censorship of Online Encyclopedias: Implications for NLP Models.](https://dl.acm.org/doi/pdf/10.1145/3442188.3445916) In *Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency* (pp. 537-548).
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-10" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 10: 10/26. Networks (I).</a>
</h2>
</div>
<div id="collapse10" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Basic concepts in social network analysis. Network visualization.
[LINK TO SLIDES (.pdf)](slides/14-networks-intro.pdf)
#### Background reading
- Sinclair, B. (2016). Network Structure and Social Outcomes: Network Analysis for Social Science, in Alvarez, M. (ed.) Computational Social Science. Cambridge: Cambridge University Press. [Full text available through USC libraries]
- Chapters 1, 2, and 3 in Easley, D., & Kleinberg, J. (2010). [Networks, crowds, and markets.](https://www.cs.cornell.edu/home/kleinber/networks-book/) Cambridge University Press.
#### Readings for discussion
- Steinert-Threlkeld, Z. C. (2017). [Spontaneous collective action: Peripheral mobilization during the Arab Spring.](https://www.cambridge.org/core/journals/american-political-science-review/article/spontaneous-collective-action-peripheral-mobilization-during-the-arab-spring/2E9A10C26CA53918CCAD479E6F7E4646) *American Political Science Review*, 111(2), 379-403.
- Chetty, R., Jackson, M. O., Kuchler, T., Stroebel, J., et al. (2022). [Social capital I: measurement and associations with economic mobility.](https://www.nature.com/articles/s41586-022-04996-4) *Nature*, 1-14.
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-11" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 11: 11/02. Networks (II).</a>
</h2>
</div>
<div id="collapse11" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Social contagion processes: homophily vs influence. Collecting social media data.
[LINK TO SLIDES (.pdf)](slides/15-networks-data.pdf)
#### Background reading
- McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). [Birds of a feather: Homophily in social networks.](http://www.annualreviews.org/doi/abs/10.1146/annurev.soc.27.1.415) *Annual review of sociology*, 27(1), 415-444.
- Shalizi, C.R. and Thomas, A. (2011). [Homophily and Contagion Are Generically Confounded in Observational Social Network Studies.](https://journals.sagepub.com/doi/pdf/10.1177/0049124111404820) *Sociological Methods & Research*, 40(2): 211-239.
#### Readings for discussion
- Christakis, N. A., & Fowler, J. H. (2007). [The spread of obesity in a large social network over 32 years.](http://www.nejm.org/doi/full/10.1056/NEJMsa066082#t=article) *New England Journal of Medicine*, 357, 370-379.
- Aral, S., Muchnik, L., & Sundararajan, A. (2009). [Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks.](http://www.pnas.org/content/106/51/21544.short) *Proceedings of the National Academy of Sciences*, 106(51), 21544-21549.
- Bakshy, E., Rosenn, I., Marlow, C. and Adamic, L. (2012) [The Role of Social Networks in Information Diffusion.](https://arxiv.org/pdf/1201.4145.pdf) ArXiv Preprint: 1201.4145v2
- Vosoughi, S., Roy, D., & Aral, S. (2018). [The spread of true and false news online.](https://science.sciencemag.org/CONTENT/359/6380/1146.abstract) *Science*, 359(6380), 1146-1151.
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-12" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 12: 11/09. Networks (III).</a>
</h2>
</div>
<div id="collapse12" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Dimensionality reduction. Latent space network models.
[LINK TO SLIDES (.pdf)](slides/16-latent-variables.pdf)
#### Readings for discussion
- Treier, S., & Jackman, S. (2008). [Democracy as a latent variable.](http://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2007.00308.x/full) *American Journal of Political Science*, 52(1), 201-217.
- Barberá, P. (2014). [Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data.](https://www.cambridge.org/core/journals/political-analysis/article/div-classtitlebirds-of-the-same-feather-tweet-together-bayesian-ideal-point-estimation-using-twitter-datadiv/91E37205F69AEA32EF27F12563DC2A0A) *Political Analysis*, 23(1), 76-91.
- González-Bailón et al (2011). [The Dynamics of Protest Recruitment through an Online Network.](https://www.nature.com/articles/srep00197/fig_tab) *Nature Scientific Reports*, 1: 197.
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-13" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 13: 11/16. Big data. </a>
</h2>
</div>
<div id="collapse13" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Introduction to SQL.
[LINK TO SLIDES (.pdf)](slides/17-SQL.pdf)
#### Background reading
- Beaulieu, A. (2009). [Learning SQL: Master SQL Fundamentals.](https://books.google.co.uk/books?hl=en&lr=&id=1PgCCVryjOQC&oi=fnd&pg=PR3&dq=learning+sql+alan&ots=X6M3Iaz1wO&sig=38Fp1kDlxM8TF7miw0K2CNcKib4#v=onepage&q=learning%20sql%20alan&f=false) O’Reilly Media, Inc.
- Stephens, R., Plew, R., & Jones, A. D. (2009). [Sams teach yourself SQL in one hour a day.](https://books.google.co.uk/books?hl=en&lr=&id=9fDZ_rVoxx0C&oi=fnd&pg=PR5&dq=%22Sams+Teach+Yourself+SQL+in+24+Hours%22&ots=UkaClJDMem&sig=wgLy-DG3bc7g0LO0_Ojy5Cy2Ejs#v=onepage&q=%22Sams%20Teach%20Yourself%20SQL%20in%2024%20Hours%22&f=false) Sams Publishing.
- [Google BigQuery: Legacy SQL Functions and Operators.](https://cloud.google.com/bigquery/docs/reference/legacy-sql)
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-14" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 14: 11/23. No class.</a>
</h2>
</div>
<div id="collapse14" class="panel-collapse collapse in" style="margin:12px">
No class: Thanksgiving Holiday.
</div></div></div>
<div class="panel-group">
<div class="panel panel-default">
<div class="panel-heading">
<h2 id="week-15" class="panel-title">
<a data-toggle="collapse" href="#collapse2">Week 15: 11/30. Other topics. </a>
</h2>
</div>
<div id="collapse15" class="panel-collapse collapse in" style="margin:12px">
**Topics:** Parallel programming with R. Good coding practices
[LINK TO SLIDES (.pdf)](slides/19-good-coding-revisited.pdf)
[Job market and industry careers advice.](https://docs.google.com/presentation/d/1qf_4lhe8xIfDlDaTte5T-ir9pSNTirzR13_xTHVjdFg/edit?usp=sharing)
</div></div></div>