Designing Internet Research (Spring 2022)

From CommunityData
Designing Internet Research
COM528 - Department of Communication, University of Washington
Instructor: Benjamin Mako Hill (University of Washington)
Course Websites:
Course Catalog Description:
Focuses on designing Internet research, assessing the adaptation of proven methods to Internet tools and environments, and developing new methods in view of particular capacities and characteristics of Internet applications. Legal and ethical aspects of Internet research receive ongoing consideration.

Overview and Learning Objectives

What new lines of inquiry and approaches to social research are made possible and necessary by the Internet? In what ways have established research methods been affected by the Internet? How does the Internet challenge established methods of social research? How are researchers responding to these challenges?

These are some of the key questions we will explore in this course. The course will focus on assessing the incorporation of Internet tools in established and emergent methods of social research, the adaptation of social research methods to study online phenomena, and the development of new methods and tools that correspond with the particular capacities and characteristics of the Internet. The readings will include both descriptions of Internet-related research methods with an eye to introducing skills and examples of studies that use them. The legal and ethical aspects of Internet research will receive ongoing consideration throughout the course. The purpose of this course is to help prepare students to design high quality research projects that use the Internet to study online communicative, social, cultural, and political phenomena.

I will consider the course a complete success if every student is able to do all of these things at the end of the quarter:

  • Discuss and compare distinct types of Internet research including: web archiving; textual analysis; ethnography; interviews; network analyses of social and hyperlink networks; analysis of digital trace data, traditional, natural, and field experiments; design research; interviewing; survey research; and narrative and visual analyses.
  • Describe particular challenges and threats to research validity associated with each method.
  • For at least one method, be able to provide a detailed description of a research project and feel comfortable embarking on a formative study using this methodology.
  • Given a manuscript (e.g., in the context of a request for peer review), be able to evaluate an Internet-based study in terms of its use its methodological choices.

Note About This Syllabus

You should expect this syllabus to be a dynamic document. Although the core expectations for this class are fixed, the details of readings and assignments will shift based on how the class goes, guest speakers that I arrange, my own readings in this area, etc. As a result, there are three important things to keep in mind:

  • Although details on this syllabus will change, I will try to ensure that I never change readings more than six days before they are due. I will send an announcement no later than before each Wednesday evening that fixes the schedule for the next week. This means that if I don't fill in a reading marked "[To Be Decided]" six days before it's due, it is dropped. If we don't change something marked "[Tentative]" before the deadline, then it is assigned. This means that if you plan to read more than six days ahead, contact the teaching team first.
  • Because this syllabus a wiki, you will be able to track every change by clicking the history button on this page when I make changes. I will summarize these changes in the weekly an announcement on Canvas sent that will be emailed to everybody in the class. Closely monitor your email or the announcements section on the course website on Canvas to make sure you don't miss these!
  • I will ask the class for voluntary anonymous feedback frequently — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments to courses that I teach while the quarter progressed based on this feedback.

Books

This class has no textbook and I am not requiring you to buy any books for this class. That said, several required readings and many suggested readings, will come from several excellent books which you might want to consider adding to your library:

These books include:

  • Burgess, Jean, Alice Marwick, and Thomas Poell, eds. 2018. The SAGE Handbook of Social Media. London, UK: SAGE. [Available through UW libraries]
  • Foucault Welles, Brooke, and Sandra González-Bailón, eds. 2018. The Oxford Handbook of Networked Communication. London, UK: Oxford University Press. [Available through UW libraries]
  • Hargittai, Eszter, and Christian Sandvig, eds. 2015. Digital Research Confidential: The Secrets of Studying Behavior Online. MIT Press. [Available through UW libraries
  • Hesse-Biber, Sharlene Nagy, ed. 2011. The Handbook of Emergent Technologies in Social Research. Oxford, UK: Oxford University Press.
  • Hewson, Claire, Carl Vogel, and Dianna Laurent. 2016. Internet Research Methods. London, UK: SAGE. https://doi.org/10.4135/9781473920804. [Available through UW libraries]
  • Snee, Helene, Christine Hine, Yvette Morey, Steven Roberts, and Hayley Watson, eds. 2016. Digital Methods for Social Science: An Interdisciplinary Guide to Research Innovation. New York, NY: Palgrave-Macmillan. [Available through UW libraries]

Some Reflections on Technical Skills

This course will focus on teaching conceptual skills related to Internet research. These skills involve the "softer" skills of understanding, designing, and critiquing research plans. These are harder to teach, evaluate, and learn than more "hard" technical skills like programming, web scraping, and so on. But they are ultimately what will make a research project interesting, useful, or valid.

That said, I also believe that any skilled Internet researcher must be comfortable writing code to collect a dataset from the web or, at the very least, should have enough experience doing so that they know what is involved and what is possible and impossible. This is essential even if your only goals is to manage somebody else writing code and gathering data or work productively with a collaborator who is doing so.

Because students are going to come to the class with different technical skillsets, I will not be devoting time in this class to developing technical skills. That said, I strongly believe that a well rounded Internet researcher will have these skills as well. Although being successful in this class will not also require technical skills, being a successful Internet researcher will.

For example, I think most Internet researchers should have at least:

  1. Basic skills in a general purpose high-level programming language used for Internet-based data collection and analysis. I strongly recommend the Python programming language although other programming languages like Ruby and Perl are also good choices. Generally speaking, statistical programming languages like R, Stata, Matlab are not well suited for this. However, if you happen to known a statistical programming language, learning a language like Python will be much easier!
  2. Familiarity with the technologies of web APIs. In particular, students should understand what APIs are, how they work, and should be able to read, interpret, and process data in JSON.
  3. Knowledge of how to process and move data from a website or API into a format that they will be able to use for analysis. The final format will depend on the nature of the result but this might be a statistical programming environment like R, Stata, SAS, SPSS, etc or a qualitative data analysis tools like ATLAS.ti, NVivo, Dedoose, Taguette, or similar.

If you are already comfortable doing these things, great. If you are not, I'd love to work with you to help you make a plan for building these skills. To be clear: It's not part of the class and it's not part of how you will be evaluated. But it's something that I want to help you all to have.

Here are some options for building these technical skills:

  • I can help point you to find some online resources like MOOCs, online tutorials, and so on that are useful for building these skills. The details will probably vary based on what you know already.
  • I have plans to teach a class (likely in the Spring quarter of 2023) that will be a sort of companion class to this one and which will introduce Python and the skills above. I'd love to have any of you join me!
  • I also regularly have organized free workshops called the Community Data Science Workshops that teach exactly these skills. Although I've historically tried to time these workshops with this class, the ongoing pandemic has meant that it isn't in the cards this quarter. I do hope to teach them again at some point in the near future, though and happy to put you all on the announcement list.

Assignments

The assignments in this class are designed to give you an opportunity to try your hand at using the conceptual material taught in the class. There will be no exams or quizzes. Unless otherwise noted, all assignments are due at the end of the day (i.e., 11:59pm on the day they are due).

Reflections

Deliverables
(1) Post a message in the #reading-reflections channel on the course Discord server; (2) Respond to at least one of your classmates before class.
Due Date
(1) the day before class at 6pm (on any day with reading); (2) the day of class by 10:30am (on a day with reading)
Maximum length
500 words

For every day that we have readings (i.e., every day except for the consulting weeks and and the final presentation week), I'm asking everybody to reflect on the readings by the day before class and to share their reflections with everybody else.

Reflections should be no more than 500 words (equivalent about half a page single-spaced page). So everyone will have a chance to read the reflections before class, response papers should be posted to the #reading-reflections channel on the course Discord server the day before by 6pm (i.e., on Sundays and Tuesday) so that we can all read, think, and respond. Please also pose one or two open-ended discussion questions that may serve as jumping off points for our in-class conversation. Don't bother with summarizing (we've all done the reading after all) and focus on engaging with ideas.

In terms of content, response papers offer you an opportunity to engage the readings by identifying common or conflicting premises, thinking through potential implications, offering political or cultural examples, posing well-supported objections, or outlining critical extensions. In my experience, the most thought provoking reflections go beyond pointing out things that one wonders about or finds interesting and explain why you find it interesting.

Turn in your response paper to Canvas by posting a new message in the appropriate day in the course the discussion board.

I'd also like everybody read over everybody else's responses and respond to at least one person—evening things out so that not everybody response to one person would be nice, but use your judgement.

Research Project

As a demonstration of your learning in this course, you will design a plan for an internet research project and will, if possible, also collect (at least) an initial sample of a dataset that you will use to complete the project.

The genre of the paper you can produce can take one of the following three forms:

  1. A draft of a manuscript for submission to a conference or journal.
  2. A proposal for funding (e.g., for submission for the NSF for a graduate student fellowship).
  3. A draft of the methods chapter of your dissertation.

In any the three paths, I expect you take this opportunity to produce a document that will further your to academic career outside of the class.

Project Identification

Due Date
April 15
Maximum paper length
800 words (~3 pages)
Deliverables
Turn in the appropriate Canvas dropbox

Early on, I want you to identify your final project. Your proposal should be short and can be either paragraphs or bullets. It should include the following things:

  • The genre of the project and a short description of how it fits into your career trajectory.
  • A one paragraph abstract of the proposed study and research question, theory, community, and/or groups you plan to study.
  • A short description of the type of data you plan to collect as part of your final project.

Final Project

Outline Due Date
May 13
Maximum outline length
2 pages
Presentation Date
June 1
Paper Due Date
June 10
Maximum final paper length
8000 words (~27 pages)
All Deliverables
Turn in in the appropriate Canvas dropboxes

Because the emphasis in this class is on methods and because I'm not an expert in each of your areas or fields, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is so important. Instead of providing all of this details, instead feel free to start with a brief summary of the purpose and importance of this research, and an introduction of your research questions or hypotheses. If your provide more detail, that's fine, but I won't give you detailed feedback on this parts.

The final paper should include:

  • a statement of the purpose, central focus, relevance and significance of this research;
  • a description of the specific Internet application(s) and/or environment(s) and/or objects to be studied and employed in the research;
  • key research questions or hypotheses;
  • operationalization of key concepts;
  • a description and rationale of the specific method(s), (if more than one method will be used, explain how the methods will produce complementary findings);
  • a description of the step-by-step plan for data collection;
  • description and rationale of the level(s), unit(s) and process of analysis (if more than one kind of data are generated, explain how each kind will be analyzed individually and/or comparatively);
  • an explanation of how these analyses will enable you to answer the RQs
  • a sample instrument (as appropriate);
  • a sample dataset and description of a formative analysis you have completed;
  • a description of actual or anticipated results and any potential problems with their interpretation;
  • a plan for publishing/disseminating the findings from this research
  • a summary of technical, ethical, human subjects and legal issues that may be encountered in this research, and how you will address them;
  • a schedule (using specific dates) and proposed budget.

I also expect each student to begin data collection for your project (i.e., using the technical skills you learn in the class) and describe your progress in this regard this in your paper. If collecting data for a proposed project is impractical (e.g., because of IRB applications, funding, etc) I would love for you to engage in the collection of public dataset as part of a pilot or formative study. If this is not feasible or useful, we can discuss other options.

I have a strong preference for you to write this paper individually but I'm open to the idea that you may want to work with others in the class.

Participation

The course relies heavily on participation and discussion. It is important to realize that we will not summarize reading in class and I will not cover it in lecture. I expect you all to have read it and we will jump in and start discussing it. The "Participation Rubric" section of my detailed page on assessment gives the rubric I will use in evaluating participation.

Assessment

I have put together a very detailed page that describes the way I approach assessment and grading—both in general and in this course. Please read it carefully I will assign grades for each of following items on the UW 4.0 grade scale according to the weights below:

  • Participation: 30%
  • Reflection: 15%
  • Proposal identification: 5%
  • Final paper outline: 5%
  • Final Presentation: 10%
  • Final Paper: 35%

Schedule

Monday March 28: Introduction

Resources:

  • Class video recording [Available through Canvas] — It's mostly just me walking through the syllabus and doesn't include the introductions and such

Required Readings:

  • Agre, Phil. 2004. “Internet Research: For and Against.” In Internet Research Annual: Selected Papers from the Association of Internet Researchers Conferences 2000-2002, edited by Mia Consalvo, Nancy Baym, Jeremy Hunsinger, Klaus Bruhn Jensen, John Logie, Monica Muerero, and Leslie Regan Shade. Vol. 1. New York: Peter Lang. http://polaris.gseis.ucla.edu/pagre/research.html. [Available free online]
  • Lazer, David, Alex Pentland, Lada Adamic, Sinan Aral, Albert-Laszlo Barabasi, Devon Brewer, Nicholas Christakis, et al. 2009. “Computational Social Science.” Science 323 (5915): 721–23. https://doi.org/10.1126/science.1167742. [Available from UW libraries]
  • Sandvig, Christian, and Eszter Hargittai. 2015. “How to Think about Digital Research.” In Digital Research Confidential: The Secrets of Studying Behavior Online, edited by Eszter Hargittai and Christian Sandvig, 1–28. Cambridge, MA: MIT Press. [Available in Canvas]

Optional Reading:

Wednesday March 30: Ethics

Required Readings:

To frame a conversation around research ethics, lets read this piece:

And these pieces that are all vaguely in response to it:

Optional Readings:

  • Department of Health, Education, and Welfare, and National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. 2014. “The Belmont Report. Ethical Principles and Guidelines for the Protection of Human Subjects of Research.” http://www.hhs.gov/ohrp/policy/belmont.html. [Available free online]
  • Frankel, Mark S., and Sanyin Siang. 1999. “Ethical and Legal Aspects of Human Subject Research on the Internet.” Workshop Report. Washington, DC: American Association for the Advancement of Science. [Available free online]

Monday April 4: Internet Data Collection

Required Readings:

Optional Readings:

  • Ankerson, Megan Sapnar. 2015. “Read/Write the Digital Archive: Strategies for Historical Web Research.” In Digital Research Confidential: The Secrets of Studying Behavior Online, edited by Eszter Hargittai and Christian Sandvig, 29–54. Cambridge, MA: MIT Press. [Available in Canvas]
  • Spaniol, Marc, Dimitar Denev, Arturas Mazeika, Gerhard Weikum, and Pierre Senellart. 2009. “Data Quality in Web Archiving.” In Proceedings of the 3rd Workshop on Information Credibility on the Web, 19–26. WICOW ’09. New York, NY, USA: ACM. https://doi.org/10.1145/1526993.1526999. [Available from UW libraries]
  • Schneider, Steven M., and Kirsten A. Foot. 2004. “The Web as an Object of Study.” New Media & Society 6 (1): 114–22. https://doi.org/10.1177/1461444804039912. [Available from UW libraries]
  • Weber, Matthew S. 2014. “Observing the Web by Understanding the Past: Archival Internet Research.” In Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, 1031–1036. WWW Companion ’14. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/2567948.2579213. [Available from UW libraries]

Optional readings related to the ethics of data collection online:

Two useful sources of data collection:

  • Archive Team is an online community that archives websites. They are a fantastic resource and include many pieces of detailed technical documentation on the practice of engaging in web archiving. For example, here are detailed explanations of mirroring a website with GNU wget which is the piece of free software I usually use to archive websites.
  • OpenHumans is an online community where people share personal data with each other and with researchers.

Wednesday April 6: Digital & Trace Ethnography

Required Readings:

More traditional ethnographic research in online settings:

  • Hine, Christine. 2017. “Ethnographies of Online Communities and Social Media: Modes, Varieties, Affordances.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 401–15. London, UK: SAGE. [Available in Canvas]
  • [Selections] Jemielniak, Dariusz. 2014. Common Knowledge?: An Ethnography of Wikipedia. Stanford, California: Stanford University Press. ["Introduction" and "Appendix A: Methodology"] [Available in Canvas]

Material on "Trace" and "network" ethnographies:

Optional Readings:

  • Hine, Christine. 2000. Virtual Ethnography. London, UK: SAGE Publications. [Available from the Instructor]
This is the canonical book-length account and the main citation in this space.
Note: You may also be interest in reading the essay by Hine that boyd is responding to. [Available in Canvas]
  • Hjorth, Larissa, Heather Horst, Anne Galloway, and Genevieve Bell, eds. 2016. The Routledge Companion to Digital Ethnography. New York, NY: Routledge. [Available from the instructor]
  • Sinanan, Jolynna, and Tom McDonald. 2018. “Ethnography.” In The SAGE Handbook of Social Media, 179–95. 55 City Road: SAGE Publications Ltd. https://doi.org/10.4135/9781473984066. [Available from UW libraries]
  • Maxwell, Joseph A. 2002. “Understanding and Validity in Qualitative Research.” In The Qualitative Researcher’s Companion, edited by A. M. Huberman and Matthew B. Miles, 37–64. London, UK: SAGE. [Available in Canvas]
  • Champion, Kaylea, Nora McDonald, Stephanie Bankes, Joseph Zhang, Rachel Greenstadt, Andrea Forte, and Benjamin Mako Hill. 2019. “A Forensic Qualitative Analysis of Contributions to Wikipedia from Anonymity Seeking Users.” Proceedings of the ACM on Human-Computer Interaction 3 (CSCW): 53:1–53:26. https://doi.org/10.1145/3359155. [Available from UW libraries]

These are all other interesting and/or frequently cited examples of Internet-based ethnographies:

  • Geiger, R. Stuart, and David Ribes. 2010. “The Work of Sustaining Order in Wikipedia:The Banning of a Vandal.” In Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, 117–126. CSCW ’10. New York, NY, USA: ACM. https://doi.org/10.1145/1718918.1718941. [Available from UW libraries] — A trace ethnography and sort of the companion paper/substantive paper for the methods piece included in the required readings above.
  • Brotsky, Sarah R., and David Giles. 2007. “Inside the ‘Pro-Ana’ Community: A Covert Online Participant Observation.” Eating Disorders 15 (2): 93–109. https://doi.org/10.1080/10640260701190600. [Available from UW libraries]
Note: To conduct the study reported in this paper the authors created a used a fake profile in order to observe the psychological support offered to participants.
Note: Fantastic more general introduction but takeaways that are more specifically targeted toward people studying virtual reality type environments with virtual physicality.

Charlie's optional readings (virtual world ethnographies):

  • Bainbridge, William Sims. 2010. The Warcraft Civilization: Social Science in a Virtual World. Cambridge, Massachusetts: MIT. [mitpress https://mitpress.mit.edu/books/warcraft-civilization]
  • Nardi, Bonnie A. 2009. My Life as a Night Elf Priest: An Anthropological Account of World of Warcraft. Ann Arbor, Michigan: University of Michigan. [Available free online]
  • Pearce, Celia, Tom Boellstorff, and Bonnie A. Nardi. 2011. Communities of Play: Emergent Cultures in Multiplayer Games and Virtual Worlds. The MIT Press. [mitpress https://mitpress.mit.edu/books/communities-play]
  • Boellstorff, Tom, Bonnie Nardi, Celia Pearce, T. L. Taylor, and George E. Marcus. 2012. Ethnography and Virtual Worlds: A Handbook of Method. Princeton: Princeton University Press. [1]

Monday April 11: Online Interviewing

Required Readings:

  • O’Connor, Henrietta, and Clare Madge. 2017. “Internet-Based Interviewing.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 416–34. London, UK: SAGE. [Available in Canvas]
  • Abrams, Katie M ., and Ted J. Gaiser. 2017. “Online Focus Groups.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 435–50. London, UK: SAGE. [Available from UW libraries]
  • Hanna, Paul. 2012. “Using Internet Technologies (Such as Skype) as a Research Medium: A Research Note.” Qualitative Research 12 (2): 239–42. https://doi.org/10.1177/1468794111426607. [Available from UW libraries]
Note: Short article you can basically skim. Read it quickly so you can cite it later.

Optional Readings:

  • boyd, danah. 2015. “Making Sense of Teen Life: Strategies for Capturing Ethnographic Data in a Networked Era.” In Digital Research Confidential: The Secrets of Studying Behavior Online, edited by Eszter Hargittai and Christian Sandvig. Cambridge, Massachusetts: MIT Press. [Available from UW libraries]
Note: Strongly focused on ethnographic interviews with tons of very specific details. Fantastic article on interviewing, although perhaps a bit weak on Internet-specific advice.
  • Markham, Annette N. 1998. “The Shifting Project, The Shifting Self.” In Life Online: Researching Real Experience in Virtual Space, 61–83. Rowman Altamira. [Available from instructor]
Note: One of the earliest books on online life and one of the earliest attempts to do online interviewing. This is dated, but highlight some important challenge.

Alternate Accounts:

These texts are largely redundant to the required texts above but do provide a different perspective and examples:

  • Salmons, Janet. 2014. Qualitative Online Interviews: Strategies, Design, and Skills. SAGE Publications. [Preface, TOC, and Chapter 1] [Available from UW libraries]
This is a book that lays out what claims to be a comprehensive account to online interviewing. I have the book and am happy to loan my copy to anybody in the class that thinks this will be a core part of their research.

Optional readings related to the ethics of identify subjects:

Wednesday April 13: Discourse Analysis

Required Readings:

Optional Readings:

Monday April 18: Textual/content analyses

Required Readings:

Optional Readings:

I'm assuming you have at least a rough familiarity with content analysis as a methodology. If your not as comfortable with this, check out content analysis|the content analysis Wikipedia article to start. These help provide more of a background into content analysis (in general, and online):

  • Neuendorf, K. A. (2002). The content analysis guidebook. Thousand Oaks, Calif.: Sage Publications. [Available from Instructor]
  • Krippendorff, K. (2005). Content analysis: an introduction to its methodology. Thousand Oaks; London; New Delhi: Sage. [Available from Instructor]

Examples of more traditional content analysis using online content:

A few other things related to topic modeling and sentiment analysis:

Wednesday April 20: Social network analysis

Required Readings:

Optional Readings:

Network datasets:

Monday April 25: Visual Analysis

Required Readings:

Optional Readings:

Note: This is a two-part (each part is one hour) lecture and tutorial by an expert in computer vision. I strongly recommend watching Part I. I think this gives you a good sense of the nature of the kinds of challenges that were (and still are) facing the field of computer vision and anybody trying to have their computer look at images.

These five papers are all technical approaches to doing image classification using datasets from Internet-based datasets of images like Flickr, Google Image Search, Google Street View, or Instagram. Each of these describes interesting and challenges technical issues. If you're interested, it would be a great idea to read these to get a sense for the state of the art and what is and isn't possible:

Wednesday April 27: Design Research

Required Readings: [Tentative]

Optional Readings:

Monday May 2: Consulting Day

We will not meet together as a group today. Instead, I will schedule one-on-one in-person meetings of an hour with each student individually to catch up with you about your project and to work directly with you to resolve any technical issues you have run into with data collected.

Wednesday May 4: Consulting Day

We will not meet together as a group today. Instead, I will schedule one-on-one in-person meetings of an hour with each student individually to catch up with you about your project and to work directly with you to resolve any technical issues you have run into with data collected.

Monday May 9: Experiments

Required Readings:

Optional Readings:

This piece is set as the intersection of networks and experiments. It's very important but is probably too technical to assign for the whole c.ass
  • Kohavi, Ron, Alex Deng, Brian Frasca, Toby Walker, Ya Xu, and Nils Pohlmann. 2013. “Online Controlled Experiments at Large Scale.” In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1168–1176. KDD ’13. Chicago, Illinois, USA: Association for Computing Machinery. https://doi.org/10.1145/2487575.2488217. [Available from UW libraries]
  • Reinecke, Katharina, and Krzysztof Z. Gajos. 2015. “LabintheWild: Conducting Large-Scale Online Experiments With Uncompensated Samples.” In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 1364–1378. CSCW ’15. New York, NY, USA: ACM. https://doi.org/10.1145/2675133.2675246. [Available from UW libraries]
  • Zhu, Haiyi, Amy Zhang, Jiping He, Robert E. Kraut, and Aniket Kittur. 2013. “Effects of Peer Feedback on Contribution: A Field Experiment in Wikipedia.” In , 2253. ACM Press. https://doi.org/10.1145/2470654.2481311. [Available from UW libraries]
  • Zhang, Xiaoquan (Michael), and Feng Zhu. 2011. “Group Size and Incentives to Contribute: A Natural Experiment at Chinese Wikipedia.” American Economic Review 101 (4): 1601–15. https://doi.org/10.1257/aer.101.4.1601. [Available from UW libraries]
  • Weninger, Tim, Thomas James Johnston, and Maria Glenski. 2015. “Random Voting Effects in Social-Digital Spaces: A Case Study of Reddit Post Submissions.” Pp. 293–297 in Proceedings of the 26th ACM Conference on Hypertext & Social Media, HT ’15. Guzelyurt, Northern Cyprus: Association for Computing Machinery.

Wednesday May 11: Surveys

Required Readings:

  • Fricker, Jr., Ronald D., and Katja Lozar Manfreda. 2017. “Sampling Methods for Online Surveys.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 162–83. London, UK: SAGE. [Forthcoming]
  • Walejko, Gina. 2009. “Online Survey: Instant Publication, Instant Mistake, All of the Above.” In Research Confidential: Solutions to Problems Most Social Scientists Pretend They Never Have, edited by Eszter Hargittai, 101–21. Ann Arbor, MI: University of Michigan Press. [Forthcoming]
  • Konstan, Joseph A., B. R. Simon Rosser, Michael W. Ross, Jeffrey Stanton, and Weston M. Edwards. 2005. “The Story of Subject Naught: A Cautionary but Optimistic Tale of Internet Survey Research.” Journal of Computer-Mediated Communication 10 (2): 00–00. https://doi.org/10.1111/j.1083-6101.2005.tb00248.x. [Available free online]
  • Hill, Benjamin Mako, and Aaron Shaw. 2013. “The Wikipedia Gender Gap Revisited: Characterizing Survey Response Bias with Propensity Score Estimation.” PLoS ONE 8 (6): e65782. https://doi.org/10.1371/journal.pone.0065782. [Available free online]
  • Salganik, Matthew J., and Karen E. C. Levy. 2015. “Wiki Surveys: Open and Quantifiable Social Data Collection.” PLOS ONE 10 (5): e0123483. https://doi.org/10.1371/journal.pone.0123483. [Available free online]
Note: This journalistic account of the research may also be useful.
  • Alperin, Juan Pablo, Erik Warren Hanson, Kenneth Shores, and Stefanie Haustein. 2017. “Twitter Bot Surveys: A Discrete Choice Experiment to Increase Response Rates.” In Proceedings of the 8th International Conference on Social Media & Society, 1–4. #SMSociety17. Toronto, ON, Canada: Association for Computing Machinery. https://doi.org/10.1145/3097286.3097313. [Available from UW libraries]

Optional Readings:

  • Van Selm, Martine, and Nicholas W. Jankowski. 2006. “Conducting Online Surveys.” Quality and Quantity 40 (3): 435–56. https://doi.org/10.1007/s11135-005-8081-8. [Available from UW libraries]
  • Vehovar, Vasja, and Katja Lozar Manfreda. 2017. “Overview: Online Surveys.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 143–61. London, UK: SAGE. [Forthcoming]
  • Kaczmirek, Lars. 2017. “Online Survey Software.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 203–19. London, UK: SAGE. [Forthcoming]
  • Toepoel, Vera. 2017. “Online Survey Design.” In The SAGE Handbook of Online Research Methods, edited by Nigel G. Fielding, Raymond M. Lee, and Grant Blank, 2 edition, 184–202. London, UK: SAGE. [Forthcoming]
  • Mavletova, Aigul, and Mick P. Couper. 2014. “Mobile Web Survey Design: Scrolling versus Paging, SMS versus E-Mail Invitations.” Journal of Survey Statistics and Methodology 2 (4): 498–518. https://doi.org/10.1093/jssam/smu015. [Available from UW libraries]
  • Yun, Gi Woong, and Craig W. Trumbo. 2000. “Comparative Response to a Survey Executed by Post, e-Mail, & Web Form.” Journal of Computer-Mediated Communication 6 (1): 0–0. https://doi.org/10.1111/j.1083-6101.2000.tb00112.x. [Available free online]
  • Hargittai, Eszter, and Chris Karr. 2009. “WAT R U DOIN? Studying the Thumb Generation Using Text Messaging.” In Research Confidential: Solutions to Problems Most Social Scientists Pretend They Never Have, edited by Eszter Hargittai, 192–216. Ann Arbor, MI: University of Michigan Press. [Forthcoming]

If you don't have a background in survey design, these two have been recommended by our guest speaker as good basic things to read:

  • Krosnick, Jon A. 1999. “Maximizing Measurement Quality: Principles of Good Questionnaire Design.” In Measures of Political Attitudes, edited by John P. Robinson, Phillip R. Shaver, and Lawrence S. Wrightsman. New York: Academic Press.
  • Krosnick, Jon A. 1999. “Survey Research.” Annual Review of Psychology 50 (1): 537–67. https://doi.org/10.1146/annurev.psych.50.1.537. [Available from UW libraries]

Tools for doing mobile surveys:

Monday May 16: Digital Trace and Sensor Data

Required Readings:

Read any 2 of these 4 chapters from the Handbook of Emerging Technology in Social Research:

Wednesday May 18: Consulting Day

We will not meet together as a group today. Instead, I will schedule one-on-one in-person meetings of an hour with each student individually to catch up with you about your project and to work directly with you to resolve any technical issues you have run into with data collected.

Monday May 23: Consulting Day

We will not meet together as a group today. Instead, I will schedule one-on-one in-person meetings of an hour with each student individually to catch up with you about your project and to work directly with you to resolve any technical issues you have run into with data collected.

Wednesday May 25: Final Presentations

Monday May 30: NO CLASS for Memorial Day

Wednesday June 1: Final Presentations

Overviews of MTurk and issues of data quality:

Culture and work conditions for Turkers:

Systems to approve Turker experiences:

  • Salehi, Niloufar, Lilly C. Irani, Michael S. Bernstein, Ali Alkhatib, Eva Ogbe, Kristy Milland, and Clickhappier. 2015. “We Are Dynamo: Overcoming Stalling and Friction in Collective Action for Crowd Workers.” In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 1621–1630. CHI ’15. Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/2702123.2702508. [Available from UW libraries]
  • Irani, Lilly C., and M. Six Silberman. 2013. “Turkopticon: Interrupting Worker Invisibility in Amazon Mechanical Turk.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 611–620. CHI ’13. Paris, France: Association for Computing Machinery. https://doi.org/10.1145/2470654.2470742. [Available from UW libraries]

Assignments to complete before class:

The first task is to complete a task a crowd worker:

  • If you are a US citizen: Sign up as a worker on MTurk. Find and complete at least 5 "hits" as a worker on Amazon Mechanical Turk. Note that to do this you will need to create a worker account on Mturk.
  • If you are not a US citizen or if you cannot sign up on MTurk for some other reason: Complete at least 3-4 classification tasks in at least 2 different Zooniverse projects of your choice. Also, complete at least one "study" in Lab in the Wild
  • In either case: Record (write down) details and notes about your tasks: What did you do? Who was the requester? What could you was the purpose of the task (as best you could tell)? What was the experience like? What research applications can you (not) imagine for this kind of system?

The second task is to get ready to launch a task as a requestor. We will design and launch tasks in class but I want you to do the following ahead of time:

  • Create a "requester" account on Amazon Mechnical Turk. Doing so may require up top 48 hours to be approved so please do that immediately so you have it ready to go in class.
  • Put money onto your requestor account to pay workers. A $5 budget should be sufficient for our class. They should take any payment that Amazon does.
  • Think of at least one small classification or coding task (e.g., of Tweets, images, etc) and one human subjects data collection tasks like a survey, a survey experiment, etc, that you would like to run. You will have a budget of $5 to run the task!
  • If running this task will involve some data (e.g., a set of images or URLs, a set of Tweets, etc), collect that material in a spreadsheet before class. If it will involve a survey, create your survey in a Google Form and/or a Survey Monkey or Qualtrics survey before class.

Hyperlink Networks [Tentative]

Optional readings:

Tools for collecting hyperlink network data:

  • Issue Crawler — network mapping software by the Govcom.org Foundation, Amsterdam in a group run by Richard Rogers
  • Virtual Observatory for the Study of Online Networks (VOSON) — "web-based software incorporating web mining, data visualisation, and traditional empirical social science methods (e.g. social network analysis, SNA). Text analysis, dataset manipulation and visualisation, and social network analysis (SNA) are available within an integrated environment."

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Administrative Notes

Teaching and learning with COVID-19

The COVID-19 pandemic will impact this course in various ways, some of them obvious and tangible and others harder to pin down. On the obvious and tangible front, some of us will likely be wearing masks. UW has made it very clear to all of us that if anyone of us feels sick, they cannot come to campus or class. This might translate into some hybrid course sessions at some point over the quarter. Since the room we'll be meeting in is not set up for hybrid learning, there's a possibility that we might end up having to move whole sessions online. All of this will reshape our collective "classroom" experience in major ways.

On the "harder to pin down" side, many of us may experience elevated levels of exhaustion, stress, uncertainty and distraction. We may need to provide unexpected support to family, friends, or others in our communities. I have personally experienced all of these things at various times over the pandemic and I expect that some of you have too. It is a difficult time.

I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I will try to be transparent and direct with you throughout—both with respect to the course material as well as the pandemic and the university's evolving response to it. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time (directly related to the pandemic or not), please contact the teaching team.

This text is borrowed and adapted from Aaron Shaw's statistics course.

Your Presence in Class

As detailed in my detailed page on assessment, your participation in discussion is an important way that I will assess learning. Obviously, you must be in class in order to participate. In the event of an absence, you are responsible for obtaining notes, handouts, assignments, etc. If you can't come to campus due to COVID-19 related issues please be in contact as soon as you can and we'll figure this out. Don't risk the health of yourself or your classmates.

Religious Accommodations

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Student Conduct

The University of Washington Student Conduct Code (WAC 478-121) defines prohibited academic and behavioral conduct and describes how the University holds students accountable as they pursue their academic goals. Allegations of misconduct by students may be referred to the appropriate campus office for investigation and resolution. More information can be found online at https://www.washington.edu/studentconduct/ Safety

Call SafeCampus at 206-685-7233 anytime–no matter where you work or study–to anonymously discuss safety and well-being concerns for yourself or others. SafeCampus’s team of caring professionals will provide individualized support, while discussing short- and long-term solutions and connecting you with additional resources when requested.

Academic Dishonesty

This includes: cheating on assignments, plagiarizing (misrepresenting work by another author as your own, paraphrasing or quoting sources without acknowledging the original author, or using information from the internet without proper citation), and submitting the same or similar paper to meet the requirements of more than one course without instructor approval. Academic dishonesty in any part of this course is grounds for failure and further disciplinary action. The first incident of plagiarism will result in the student’s receiving a zero on the plagiarized assignment. The second incident of plagiarism will result in the student’s receiving a zero in the class.

Disability Resources

If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to uw at your earliest convenience so we can discuss your needs in this course.

If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924 or uwdrs@uw.edu or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.

Other Student Support

Any student who has difficulty affording groceries or accessing sufficient food to eat every day, or who lacks a safe and stable place to live, and believes this may affect their performance in the course, is urged to contact the graduate program advisor for support. Furthermore, please notify the professors if you are comfortable in doing so. This will enable us to provide any resources that we may possess (adapted from Sara Goldrick-Rab). Please also note the student food pantry, Any Hungry Husky at the ECC.

Credit and Notes

This will be the third time I have taught this course at UW in its current form. This syllabus draws heavily from previous versions. Syllabuses from earlier classes can be found online at:

This syllabus was inspired by and borrows with permission from, a syllabus from an earlier version of this class taught by Kirsten Foot. Professor Foot last taught the course in Spring 2014.

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