CS-6471: Computational Social Science

MW 3:30-4:45pm, East Architecture 207

Course Information
Schedule
Grading
Prerequisites
FAQs
External Resources
Final Presentations

Course Information

This course is about using a variety of techniques from NLP, ML, and social science to develop a broad understanding of the emerging cross-disciplinary field of Computational Social Science. Example topics include methods of text analyses, and applications to social science fields, such as political science, sociolinguistics, sociology, and economics.

Certain slides, and materials for this course are borrowed from Jacob Eisenstein, David Bamman at UC Berkeley, and Robert Kraut at CMU.

Instructor

Diyi Yang

Teaching Assistants

Caleb Ziems

Shang-Ling Hsu

Piazza:: piazza.com/gatech/spring2022/cs6471
Office Hours (Eastern Time):: Caleb Ziems:
Mondays 4:45PM - 5:45PM (Right After Class)
Location: West Architecture Classroom 260 (and BlueJeans)

Shang-Ling Hsu
Thursdays 11AM-12PM
Location: College of Computing Building, First Floor Commons (and BlueJeans)

Prof. Diyi Yang
By Appointment

Schedule

Note: tentative schedule is subject to change.

[🚀] indicates optional reading.

Date	Topic	Readings
Jan 10	Introduction to CSS Slides	Lazer, David, Alex Pentland, Lada Adamic, Sinan Aral, Albert-Laszlo Barabasi, Devon Brewer, Nicholas Christakis et al. "Social science. Computational social science." Science (New York, NY) 323, no. 5915 (2009): 721-723.
Jan 12	Lecture: NLP and Machine Learning Basics Slides	Boyd, Danah, and Kate Crawford. "Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon." Information, communication & society 15, no. 5 (2012): 662-679. Jurafsky, Dan. Speech & language processing. Pearson Education India, 2021.
Jan 17	No Class: MLK Day
Jan 19	Bias, Fairness, and Ethics 1 Slides	Guo, Wei, and Aylin Caliskan. "Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases." In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pp. 122-133. 2021. Dutt, Kuheli, Danielle L. Pfaff, Ariel F. Bernstein, Joseph S. Dillard, and Caryn J. Block. "Gender differences in recommendation letters for postdoctoral fellowships in geoscience." Nature Geoscience 9, no. 11 (2016): 805-808.
Jan 24	Bias, Fairness, and Ethics 2 Slides	Shmueli, Boaz, Jan Fell, Soumya Ray, and Lun-Wei Ku. "Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing." In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3758-3769. 2021. Reid Pryzant, Richard Diehl Martinez, Nathan Dass, Sadao Kurohashi, Dan Jurafsky, Diyi Yang. 2020. "Automatically Neutralizing Subjective Bias in Text." AAAI 2020
Jan 26	Introduction to Project Ideas Slides
Jan 31	Language Civility 1 Slides	ElSherief, Mai, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, and Diyi Yang. "Latent Hatred: A Benchmark for Understanding Implicit Hate Speech." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 345-363. 2021. Breitfeller, Luke, Emily Ahn, David Jurgens, and Yulia Tsvetkov. "Finding microaggressions in the wild: A case for locating elusive phenomena in social media posts." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 1664-1674. 2019.
Feb 2	Language Civility 2 Slides	Danescu-Niculescu-Mizil, Cristian, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, and Christopher Potts. "A computational approach to politeness with application to social factors." In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 250-259. 2013. Zhou, Naitian, and David Jurgens. "Condolences and empathy in online communities." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 609-626. 2020. Brown, Penelope, Stephen C. Levinson, and Stephen C. Levinson. Politeness: Some universals in language usage. Vol. 4. Cambridge university press, 1987.
Feb 7	Guest Lecture from Su Lin Blodgett on: Language, Language Technologies, and Justice (hover for abstract)	Blodgett, Su Lin, Lisa Green, and Brendan O’Connor. "Demographic Dialectal Variation in Social Media: A Case Study of African-American English." In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1119-1130. 2016. Blodgett, Su Lin, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, and Hanna Wallach. "Stereotyping Norwegian salmon: an inventory of pitfalls in fairness benchmark datasets." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1004-1015. 2021.
Feb 9	Lecture: Social Science Theories Slides
Feb 14	Computational cognitive modeling 1 Slides	Kao, Justine T., Jean Y. Wu, Leon Bergen, and Noah D. Goodman. "Nonliteral understanding of number words." Proceedings of the National Academy of Sciences 111, no. 33 (2014): 12002-12007. Fried, Daniel, Jacob Andreas, and Dan Klein. "Unified Pragmatic Models for Generating and Following Instructions." Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018. Potts, Chris. "Linguistics 130A - Introduction to the Rational Speech Acts Model." YouTube, February 12, 2019.
Feb 16	Computational cognitive modeling 2 Slides	Shum, Michael, Max Kleiman-Weiner, Michael L. Littman, and Joshua B. Tenenbaum. "Theory of minds: Understanding behavior in groups through inverse planning." In Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, pp. 6163-6170. 2019. Zhu, Hao, Graham Neubig, and Yonatan Bisk. "Few-shot language coordination by modeling theory of mind." In International Conference on Machine Learning, pp. 12901-12911. PMLR, 2021.
Feb 21	Language Influence 1: Persuasion / Neogiation Slides	Rosenthal, Sara, and Kathleen Mckeown. "Detecting influencers in multiple online genres." ACM Transactions on Internet Technology (TOIT) 17, no. 2 (2017): 1-22. Yang, Diyi, and Robert E. Kraut. "Persuading teammates to give: Systematic versus heuristic cues for soliciting loans." Proceedings of the ACM on Human-Computer Interaction 1, no. CSCW (2017): 1-21.
Feb 23	Language Influence 2: Deception Slides	Peskov, Denis, Benny Cheng, Ahmed Elgohary, Joe Barrow, Cristian Danescu-Niculescu-Mizil, and Jordan Boyd-Graber. "It Takes Two to Lie: One to Lie, and One to Listen." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3811-3854. 2020. Pérez-Rosas, Verónica, Mohamed Abouelenien, Rada Mihalcea, and Mihai Burzo. "Deception detection using real-life trial data." In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 59-66. 2015.
Feb 28	Project Consultation (in-class)
March 2	Information Diffusion 1 Slides	Vosoughi, Soroush, Deb Roy, and Sinan Aral. "The spread of true and false news online." Science 359, no. 6380 (2018): 1146-1151. Zhu, Jian, and David Jurgens. "The structure of online social networks modulates the rate of lexical change." In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2201-2218. 2021. Hofman, Jake M., Amit Sharma, and Duncan J. Watts. "Prediction and explanation in social systems." Science 355, no. 6324 (2017): 486-488.
March 7	Information Diffusion 2 Slides	Leskovec, Jure, Lars Backstrom, and Jon Kleinberg. "Meme-tracking and the dynamics of the news cycle." In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 497-506. 2009. Cheng, Justin, Lada A. Adamic, Jon M. Kleinberg, and Jure Leskovec. "Do cascades recur?." In Proceedings of the 25th international conference on world wide web, pp. 671-681. 2016. Guille, Adrien, Hakim Hacid, Cecile Favre, and Djamel A. Zighed. "Information diffusion in online social networks: A survey." ACM Sigmod Record 42, no. 2 (2013): 17-28.
March 9	Mid-way Project Presentations
March 14	Cross-Cultural CSS 1 Slides	Rajadesingan, Ashwin, Ramaswami Mahalingam, and David Jurgens. "Smart, Responsible, and Upper Caste Only: Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles." In Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 393-404. 2019. Jackson, Joshua Conrad, Joseph Watts, Teague R. Henry, Johann-Mattis List, Robert Forkel, Peter J. Mucha, Simon J. Greenhill, Russell D. Gray, and Kristen A. Lindquist. "Emotion semantics show both cultural variation and universal structure." Science 366, no. 6472 (2019): 1517-1522.
March 16	Cross-Cultural CSS 2 Slides	Mehr, Samuel A., Manvir Singh, Dean Knox, Daniel M. Ketter, Daniel Pickens-Jones, Stephanie Atwood, Christopher Lucas et al. "Universality and diversity in human song." Science 366, no. 6468 (2019). Wilson, Steven, Rada Mihalcea, Ryan Boyd, and James Pennebaker. "Disentangling topic models: A cross-cultural analysis of personal values through words." In Proceedings of the first workshop on NLP and computational social science, pp. 143-152. 2016.
March 21	No Class: Spring Break
March 23	No Class: Spring Break
March 28	Guest Lecture from Katherine Keith Text and Causal Estimation: Text as Causal Confounders and Mediators (hover for abstract)	Keith, Katherine, David Jensen, and Brendan O’Connor. "Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates."In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5332-5344. 2020. Keith, Katherine, Douglas Rice, and Brendan O’Connor."Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects." In Proceedings of the First Workshop on Causal Inference and NLP, pp. 21-32. 2021.
March 30	Lecture: Statistics & Casual Inference Basics Slides
April 4	Multimodal CSS 1 Slides	Khani, Nikzad, Isidora Tourni, Mohammad Sadegh Rasooli, Chris Callison-Burch, and Derry Tanti Wijaya. "Cultural and Geographical Influences on Image Translatability of Words across Languages." NAACL 2021 Hussain, Zaeem, Mingda Zhang, Xiaozhong Zhang, Keren Ye, Christopher Thomas, Zuha Agha, Nathan Ong, and Adriana Kovashka. "Automatic understanding of image and video advertisements." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1705-1715. 2017.
April 6	Multimodal CSS 2 Slides	Kruk, Julia, Jonah Lubin, Karan Sikka, Xiao Lin, Dan Jurafsky, and Ajay Divakaran. "Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4622-4632. 2019. Wang, Xingyao, and David Jurgens. "An animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialog." In Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 3228-3257. 2021.
April 11	Social Roles and Teamwork Slides	Yang, Diyi, Robert E. Kraut, Tenbroeck Smith, Elijah Mayfield, and Dan Jurafsky. "Seekers, providers, welcomers, and storytellers: Modeling social roles in online health communities." In Proceedings of the 2019 CHI conference on human factors in computing systems, pp. 1-14. 2019. Valentine, Melissa A., Daniela Retelny, Alexandra To, Negar Rahmati, Tulsee Doshi, and Michael S. Bernstein. "Flash organizations: Crowdsourcing complex work by structuring crowds as organizations." In Proceedings of the 2017 CHI conference on human factors in computing systems, pp. 3523-3537. 2017. Gleave, Eric, Howard T. Welser, Thomas M. Lento, and Marc A. Smith. "A conceptual and operational definition of'social role'in online community." In 2009 42nd hawaii international conference on system sciences, pp. 1-11. IEEE, 2009.
April 13	Human AI Interaction Slides	Clark, Elizabeth, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A. Smith. "All That’s ‘Human’Is Not Gold: Evaluating Human Evaluation of Generated Text." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7282-7296. 2021. Wang, Dakuo, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. "Human-ai collaboration in data science: Exploring data scientists' perceptions of automated ai." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-24. Lubars, Brian, and Chenhao Tan. "Ask not what AI can do, but what AI should do: towards a framework of task delegability." In Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 57-67. 2019.
April 18	Social Movements Slides	Ziems, Caleb, and Diyi Yang. "To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence." In Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 957-976. 2021. Olteanu, Alexandra, Carlos Castillo, Nicholas Diakopoulos, and Karl Aberer. "Comparing events coverage in online news and social media: The case of climate change." In Ninth International AAAI Conference on Web and Social Media. 2015.
April 20	Final Project Presentation 1	Final Presentations
Apr 25	Final Project Presentation 2	Final Presentations

Grading

50% Project
- Project Proposal and Literature Review (10%)
- Midterm Report (15%)
- Final Report (20%)
- Project Presentation (5%)
25% Reading Responses
15% In-Class Presentations
5% Attendance Quiz
5% In-Class Discussion

Prerequisites

The course is designed for graduate students who are interested in natural language processing and computational social science. Prerequisites: a course in artificial intelligence or any relevant field (e.g., NLP, ML); proficiency with using ML/NLP tools.

Furthermore, this course assumes:

Good coding ability, corresponding to at least a third or fourth-year undergraduate CS major.
Background in basic probability, linear algebra, and calculus.
Familiarity with machine learning is helpful but not assumed. Of particular relevance are linear classifiers: naive Bayes, and logistic regression.

FAQs

The class is full. Can I still get in?

Sorry. The course admins in CoC control this process. Please talk to them.
I am graduating this Fall and I need this class to complete my degree requirements. What should I do?

Talk to the advisor or graduate coordinator for your academic program. They are keeping track of your degree requirements and will work with you if you need a specific course.
I have a question. What is the best way to reach the course staff?

Registered students – your first point of contact is Piazza (so that other students may benefit from your questions and our answers). If you have a personal matter, please email us.

Computational Social Science