Social Computing Syllabus
This Social Computing course focuses on understanding and modeling social behavior through its digital traces. What kind of insight can we gain about social dynamics through the massive online data? The course will also focus on how social dynamics, interactions, and social networks get shaped by various online platforms and environments. Students will read relevant academic papers in social computing, as well as being introduced to some foundational methods and techniques in social media data analysis, such as network analysis, topic modeling, and sentiment analysis. They will express their learning through written (reflections) and in-class discussion of reading, individual homework assignments, and a final group research project. With their final projects, they will investigate specific questions about social behavior and analyze real world online data sets, including Twitter, OpenStreetMap, and likely Wikipedia data.
Students are expected to:
- attend all class meetings
- participate in in-class discussions
- submit four individual homeworks
- submit and present a group final project
Final grades are determined using the following weights:
Undergraduate Grade Points
- Weekly Reflections - 30%
- Homeworks - 20%
- Class Participation - 15%
- Final Project - 35%
Graduate Grade Points
- Weekly Reflections - 25%
- Homeworks - 15%
- Class Participation - 15%
- Grad-student led discussion - 15%
- Final Project - 30%
Your weekly reflection should include the following aspects:
- Your impressions of the reading: what seemed interesting, surprising, most important
- What aspects you are critical of (disagree, see problems with methods)
- Two questions about the readings for class discussion
- What kind of study would you design based on these ideas
Observation of social behavior in public spaces
- Observe a public space for at least half an hour
- Take notes about what you observe
- Specifically focus on who interacts with whom, with respect to race, class, gender and other salient social distinctions
- Note the spatial aspects of these interactions (how different people occupy or make use of space differently)
- Note the temporal aspects of the interaction (how fast or slow things happened)
- Write a short (no more than a page) reflection on the above, including the time and place of your observations.
Find a publically available data set and form a hypothesis
- Search online for publically available data sets
- A few potential places to start: SNAP data sets, Icon data sets, data.world
In your one-page write up:
- Describe the data set — what types of data does it include?
- Describe a hypothesis you could test or a question you would like to answer with this data set
- Describe the advantages and the limitations of this data set for answering your question (what variables would you use, what variables are missing, sampling issues)
Basic statistical analysis of chosen dataset
- Select 4-5 variables that relate to your hypothesis or research question (you are free to explore a different research question or a different data set than in the prior homework)
- Check the selected variables for missing values and address them in some way (see lecture in WEEK 4)
- Visualize the distribution of the data (histogram)
- Statistically describe the selected variables (mean, variable, standard deviation, or non-parametric equivalents like median, range, quartiles if the data is not Normally-distributed)
- Check for correlations between some subset of the selected variables (select the subset based on your hypothesis)
In you 1-2 page write-up:
- Very briefly describe your chosen data set (old or new)
- Explain why you have chosen these particular variables and how they relate to your (old or new) hypothesis
- Describe your approach to addressing the missing values
- Include the statistical descriptors of the data and the histogram for each variable
- Discuss the correlations between a subset of the variables
- Include any other plots or analyses (optional)
Final project proposal
In you 2-page proposal:
- Describe the research question you are hoping to answer with this final project. What is novel and meaningful about this research question? Describe why you are interested in this research question
- Briefly describe your chosen data set and why it is suitable for answering your research question. Be specific about what types of data are contained in the data set and how they are relevant. Report if you decided to switch data sets in the process and why.
- Explain which particular variables you plan to focus on and how they relate to your hypothesis or research question. How do you plan to operationalize the concepts you are hoping to measure with those variable? (i.e. follower count as proxy for popularity).
- Describe the methods you plan to use for this final project. Are you planning to do a quantitative or qualitative analysis? If you are using digital trace data (most of you are), what computational techniques are you planning to use to answer your research question?
- Discuss the limitations of your approach. What are the sampling issues with how your data set has been collected? Are the variables available to operationalize your concepts of interest good proxies? Reflect on the limitations of the methods you plan to use in answering your research question.
This course’s philosophy on academic honesty is best stated as “be reasonable.” The course recognizes that interactions with classmates and others can facilitate mastery of the course’s material. However, there remains a line between enlisting the help of another and submitting the work of another.
The essence of all the individual work that you submit to this course must be your own. Collaboration on homeworks and reflections is not permitted except to the extent that you may ask classmates and others for help so long as that help does not reduce to another doing your work for you. Generally speaking, when asking for help, you may show your code to others, but you may not view theirs, so long as you and they respect this policy’s other constraints. Collaboration on the course’s final project is encouraged within the project group.
Americans with Disabilities Act (ADA) Policy Statement
The Americans with Disabilities Act (ADA) is a federal antidiscrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact the [Accessibility Resource Center] (http://arc.unm.edu/).
Title IX Sexual Harassment Policy Statement
No form of discrimination, sexual harassment, or sexual misconduct will be tolerated in this class or at UNM in general. I strongly encourage you to report any problems you have in this regard to the appropriate person at UNM. As described below, I must report any such incidents of which I become aware to the university. UNM also has confidential counselors available through UNM Student Health and Counseling (SHAC), UNM Counseling and Referral Services (CARS), and UNM LoboRespect.
In an effort to meet obligations under Title IX, UNM faculty are considered “responsible employees” by the Department of Education (see pg.15). This designation requires that any report of gender discrimination which includes sexual harassment, sexual misconduct and sexual violence made to a faculty member, TA, or GA must be reported to the Title IX Coordinator at the Office of Equal Opportunity. For more information on the campus policy regarding sexual misconduct, see this.