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Course Syllabus (Fall 2015)

Social Networks

Sociology/Communications 375 at the University of Maine at Augusta, Fall 2015
Prerequisites: SOC 101 (Introduction to Sociology) or COM 205/PSY 205 (Forms of Social Influence)
Course website: http://umasocialmedia.com/socialnetworks
Course Blackboard page for registered students: http://courses.maine.edu


Course Description Course Outcomes Course Expectations Schedule of Assignments & Readings Academic Integrity

Faculty Contact and Office Hours

Assistant Professor of Social Science James Cook
Phone: 207-621-3190
E-mail: james.m.cook@maine.edu
Social Media: Google+ | Facebook | Twitter
Drop-In Office Hours: Mondays 8-10 am by phone and video chat. Thursdays 8-9 am and 11:45 am – 1:45 pm at University College Rockland.

Alternative Contact in case of Emergency: College of Arts and Sciences Administrative Assistant LeeAnn Trask, 621-3272.

Course Description

Sociology is the scientific study of social organization — patterns in how humans behave in affiliations (ways of belonging to categories, groups and institutions) and interactions (various kinds of communication between humans). Sociologists in the social network tradition track in affiliations and interaction by building and studying patterns they find in lists, graphs, and matrices. You’ll learn those skills here.

Networks can be depicted as Lists, Graphs and Matrices

You have already encountered social networks in your own life. You probably already engage in “social networking” online when you post an update via Facebook, “follow” people on Twitter or share a YouTube video. But social networks exist offline as well. Every time you join an organization, pass on a rumor, collect information, ask someone you know for a favor, or try to find a job, social networks play a part. We rely on these aspects of our world that exist above the level of the individual person. The spread of culture, the transmission of disease, the competition of organizations and the development of nations are profoundly affected by the structures of interaction represented in social networks. Using a simple yet flexible model, social network analysts have been able to gain insights in subjects across the sociological spectrum.  You’ll gain insight relevant to your life here.

This course is organized into four sections:

  1. We begin with an introduction to the foundations of the social network perspective.
  2. Next, we master the techniques for measuring aspects of the structure of various kinds of networks: 1-mode networks, 2-mode networks, complete networks, ego networks, semantic networks, dyads, triads and groups.
  3. Third, we turn to the practical steps necessary for you to conduct social network research and use social network software.
  4. Fourth, we look at examples of social networking in politics, disease, online networks and surveillance.

Throughout the course you will apply the research tools of social network analysis to understand your personal connections and the connections between the organizations that are most influential in our society. This course can offer but a brief introduction to the broad terrain of social network theory and research; I hope that your exploration of the field of social networks will continue after this course is complete.

Course Learning Outcomes

The learning goals of this course fit in three categories, consonant with General Education and Social Science program learning outcomes described by the University of Maine at Augusta. A student who successfully completes this course will:

Outcome 1. Demonstrate an understanding of the theory of social networks by:

  • Connecting contemporary social network theory to 20th Century psychology, anthropology and sociology and to 19th Century social thought by Emile Durkheim and Georg Simmel.
  • Engaging in critical thinking regarding the applicability or inapplicability of social network theory to various sociological phenomena.

Outcome 2. Develop a command of the vocabulary and characterization of social networks by:

  • Using network terminology to identify nodes, arcs, edges and affiliations in graphical form through sociograms and in mathematical form through matrices.
  • Appropriately distinguishing between a complete network, network samples, ego networks, one-mode networks and two-mode networks.
  • Calculating measures of connection at the level of the actor, the position/role, the tie, the dyad, the triad, the group and the network as a whole.
  • Utilizing NodeXL software for social network analysis to assemble, organize and transform network data.

Outcome 3. Demonstrate competence in social network research by:

  • Gathering one-mode social network data on interaction and two-mode social network data on affiliation from online and offline sources.
  • Utilizing network analysis software to characterize social network structure in list, matrix and graph form.
  • Analyzing the impact of network structure on patterns through network statistics.
  • Applying social network analysis to understand socially meaningful outcomes in political action and online interaction.

Who Should Take This Course? Who Shouldn’t Take This Course?

You must be a registered student in order to gain official course credit in this class, to submit assignments for review, and to gain access to the course Blackboard page (available through my.uma.edu). However, anyone is welcome review the course materials available at the course’s public web page.

You don’t need to have any prior experience with social networks to take this course. We’ll take every step together, right from the beginning.

Students who are curious, self-directed, and interested in one of the newest fields of social science should consider taking this course. Social network analysis is one of the hottest fields of research today, having generated dozens of books, thousands of research articles and many new professional specializations.

Students who would like to avoid weekly discussion should think twice before taking this course. I will ask for your reflections and participation during nearly every week, and you will be graded for the extent of your participation in online lectures during each week. This is not a course designed for students who tend to drop in and out of focus.

Students who are uncomfortable with using new computer programs should think twice before taking this course. You’ll need to use various applications on the Internet, a word processor, and a template for Microsoft Excel called NodeXL.

Students who do not have access to a Windows computer should think twice before taking this course. A computer program required for this course, NodeXL, only runs on Windows computers with full versions of Microsoft Office installed. On the other hand, the good news is that these computer programs are also available for your free use in student computer labs in Bangor, Augusta and all of the University College centers located across Maine.  In addition, because you are a UMA student, Microsoft Office is now available for you to install on your own computer for absolutely free.  To get yourself a free copy of Microsoft Office, visit this page and follow the instructions posted there.

Course Expectations

Lectures and Reading Assignments
All readings assignments are listed below, in the section of this syllabus titled “Schedule of Assignments & Readings.” You will need to acquire two printed books for this course: Analyzing Social Media Networks with NodeXL by David Hansen, Ben Shneiderman and Marc Smith and Social Network Analysis by Christina Prell. Both are available through the UMA Bookstore and in most online bookstores as well.

Other readings will be accessible in this syllabus as hyperlinks to web pages and online academic journals. Unless the syllabus specifically notes otherwise, all reading assignments for this class are required, and should be completed by the week of the class under which they are listed.

Lectures incorporate text, images and videos and discussion. They will be listed in this course syllabus and in the course’s Blackboard page under the link “Weekly Lectures.” You’re responsible for reviewing and being familiar with all parts of these lectures, not just the main text. Lectures will be made available on the first day of the week under which they are listed.

Technical Requirements
Internet Use
You are required to use the internet in this course to:

  • access the syllabus, readings, lectures, and grading materials for the course;
  • obtain social network data;
  • transform social network information;
  • participate in course discussion;
  • submit assignments for grading;
  • use library resources; and
  • communicate with me.

I expect you to regularly check the course Blackboard page at courses.maine.edu, the course website at umasocialmedia.com/socialnetworks, and your University of Maine @maine.edu e-mail account for messages, announcements and other useful course materials. The internet is the primary means by which you should use this course syllabus, since it contains multiple links to important online resources. If you need help obtaining your Blackboard and e-mail privileges, call the UMA Help Desk at 1-800-696-4357.

Software
To succeed in this class, you will need to use NodeXL, an addition to the Microsoft Excel program in Microsoft Office. NodeXL is available absolutely free of charge at nodexl.codeplex.com (click the large “download” button and follow installation instructions), but you must have a Windows computer with Microsoft Office installed if you want to run NodeXL at home.  Microsoft Office is available for free through the university — To get yourself a free copy of Microsoft Office, visit this page and follow the instructions posted there.

If you do not have a Windows computer at home, then you have an alternative: for your convenience NodeXL have been installed in the student computer labs on the UMA Augusta campus, the UMA Bangor campus, and all University College centers across the state of Maine.

Library Access
Online access to the library of the University of Maine at Augusta is required for this course in order for you to find and read journal articles and conduct reviews of the academic literature.  UMA library resources may be accessed easily using your University of Maine system username and password. For technical help of any kind while using the UMA Library website and library services, contact Off-Campus Library Services through this website, by phone at 1-800-339-7323 in Maine, by phone at 1-888-266-4950 outside of Maine, or by e-mail at ocls@maine.edu.

Workload Standard
In alignment with the accreditation standards of the New England Association of Schools and Colleges, the University of Maine at Augusta has defined “the appropriate workload” for a three credit hour online course like this one at a minimum of nine hours per week in order to succeed. If you find yourself consistently working more than nine hours per week and are still not succeeding in your classwork, please let me know so that we can identify the source of your trouble and some strategies for efficient study.

Office Hours and Staying In Touch
At the top of this syllabus, I’ve indicated when my “office hours” are. These are times when you don’t have to make an appointment to come in to see me. Just pop in to University College Rockland on Thursdays from 8 AM to 9 AM and 11:45 AM to 2:45 pm. I also will be available by phone from 8 AM to 10 AM on Mondays.  If you’d like to set up a text or video chat session, please let me know of your technology preference (Google Chat, Google Hangout, Skype or some other medium) and I’ll be happy to meet virtually with you that way as well.

I’d love to hear from you at other times, too; office hours are just when I’m guaranteed to be available to students.  If I don’t pick up the phone right away, please leave a message and I’ll return your call as soon as possible. You can also e-mail me (james.m.cook@maine.edu) at any time, and I usually respond to e-mails within a day.

Due Dates, Withdrawals and Incompletes
Due Dates
All graded work in class must be completed by the due dates listed below in the Course Schedule section of this syllabus. If you know you will be busy on the day an assignment is due, turn it in early. Since you are able to complete your exams within an entire allotted week as best fits your schedule (see “Exams” below), there will be no makeup examinations.

Policy on Withdrawals and Incompletes
The UMA Student Handbook notes that students who submit drop cards before November 2015 will receive W grades for the courses they drop. After that date, faculty must assign either a W or WF (withdrew failing). W grades do not count against your grade point average, but WF grades count as a F. If students drop after the drop date and are not passing the class, I will assign a WF grade.

The UMA Student Handbook notes that “an incomplete (‘I’) grade is a temporary grade assigned to a course when a student has obtained permission of the instructor to complete course requirements at a later date. Incomplete grades will remain so for one semester. At the end of that period, the incomplete grade will be converted to an ‘F’ unless the instructor has authorized an extension.” I will authorize incomplete grades for students who:

  • have already completed a majority of the work required in the course
  • are currently passing the course
  • are unable to complete work for unavoidable circumstances that they document, and
  • meet with me before the end of the semester in office hours to negotiate and agree to complete their work by a mutually agreed upon date

The awarding of an incomplete grade is at my discretion.

Accommodations for Students with Learning Disabilities

If you have a disability which may affect your ability to participate fully in this course, it is your responsibility to request accommodations promptly and well in advance of any assignments or exam.  If you work through the accommodations office, I’ll be glad to follow their recommendations. Here’s how the process works:

  1. Promptly contact the UMA Learning Support Services Office (phone 207-621-3066, email donald.osier@maine.edu) or the Coordinator of Student Support Services at your campus or center to discuss possible learning accommodations. It is the responsibility of these professionals to determine whether you are eligible for accommodations.
  2. If the professionals in Learning Support Services or Student Support Services determine you are eligible for accommodations, I will be provided with a letter from the office notifying me confidentially that you are eligible for accommodations.
  3. After you receive approval for accommodations, it is your responsibility to contact me to make specific arrangements to fit your accommodations to the work in class. I will be happy to provide the accommodations mentioned in your letter from Learning Support Services or Student Support Services.
  4. Accommodations are not provided retroactively, and the process can take some time. This means that you should start this process well before accommodations are needed.

Grading

Participation in Online Lectures
This social networks course is designed to build skill, and an essential part of that skill-building is practicing and questioning. Your participation during the class lectures, and your reading of other students’ participatory questions and trials, is therefore an essential element of learning. In the weekly schedule for our class contained at the bottom of this syllabus, you’ll notice that I ask you to participate by answering questions and posting information during each lecture. To gain credit for that participation, you should make your contributions during the week that a lecture is introduced: the specific due date for participation is listed in each week’s schedule.

Informed, prepared, thoughtful, active participation in class activities and discussion, in a manner that is respectful of and responsive to your peers, will result in a high class participation grade. Carelessness, lack of preparation, inactivity, unresponsiveness and disrespect toward peers will lead to a lower class participation grade. You must positively engage to earn a score. Scores will range from 100 (Outstanding) to 90 (Excellent) to 80 (Good) to 70 (Acceptable) to 60 (Unacceptable) to 0 (None). Participation in lecture after the due date listed for a particular week does not contribute to our collective understanding as a class, since your fellow students will have already moved on; therefore, a lack of participation during the week in question will lead to a grade of 0 — with no make-ups or credit for late work. The average of class participation across all weeks is worth 25% of your final grade.

Because our online lectures take place on a public website, I’d like you to use a pseudonym, not your actual name, when engaging in lecture activities.  I’ll send you an e-mail with an assigned pseudonym in the week before class begins. Only you and I will know that the pseudonym refers to you, and I’ll grade you based on participation in which you use your pseudonym.

Exams
There will be two exams in this class that must be completed and turned in by October 11 and December 19. Taken together, the exams will counts for 50% of your final grade. The exams will test you on core social network concepts, ask you to gather networks data and calculate network characteristics, and will include multiple-choice, definition, skill-based and short answer questions. The exam is “open-book” in nature, meaning that you may refer to course lectures, texts and notes in the preparation of your answers. This allowance also means that you should be able to achieve a higher standard of performance; I’ll grade strictly, so be sure to answer thoroughly. You may not solicit or receive the assistance of anyone else in the completion of your exam.

If you turn in an exam up to 24 hours after its due date, I will take 10 points off your exam grade.  If you turn in an exam more than a day but less than a week after its due date, I will take 25 points off your exam grade. If you turn in your exam more than a week after the due date, you will be assigned half the points you would have otherwise received.  If you think you may be inconvenienced on or near the exam due date, be sure to complete and turn in your exam early.

Homework
During the semester, there will be 8 pieces of homework for you to turn in. You should complete each piece of homework on your own, and the work you turn in should be wholly your own work. If you refer to another piece of writing or to a piece of data in your work, you should cite the source of that writing or data; follow the ASA format for citations as described here.

If you turn in a piece of homework on time and correctly complete all required tasks, you will receive 100 points. Homework that does not correctly complete all tasks will receive partial credit based on the portion of your work that is correct. Homework that I can’t understand will also receive only partial credit, so be sure to pay attention to issues of spelling, grammar and clarity of expression. Each piece of homework is due by a specific date and time, as listed on the course schedule below. Homework that is turned in less than 1 day late will lose 10 points. Homework turned in more than a day but less than a week after its due date will lose 25 points. If you turn in your exam more than a week after the due date, you will be assigned half the points you would have otherwise received.

Your final homework grade for the course will be the average of your weekly homework grades, but in order to encourage persistence and in recognition that everyone has hard weeks now and then, I will drop your two lowest homework scores before calculating that average. This is a chance for you to erase your worst scores and focus on your best performances, and also can be thought of as a form of extra credit. Save those extra droppable homeworks for your worst weeks of studying and for actual emergencies — there will be no other way to make up for late homework penalties. Your final homework average will count for 25% of your final grade.

Each piece of homework should be turned in in on the course Blackboard page (available through my.uma.edu). Click the “Homework” link on the course Blackboard page, then click the name of the homework assignment you’re turning in. You’ll be taken to a page on which you may upload your homework document (look for the section of the page that reads “Attach File”). Be sure to click “Submit” at the bottom of your page to turn in your work.

Extra Credit:

There are two opportunities for extra credit in this course.

  • The first opportunity for extra credit is up to four points added to your final grade for a three-to-five-page review of a piece of published, peer-reviewed academic research in social network analysis that has not been read or discussed during the semester.  The research should appear in an academic journal; reviews of popular writing are not acceptable.  The review should both summarize the substance of the reviewed research and indicate the importance of the research for understanding social networks.  The quality of your submission will determine the number of points of extra credit you receive.  To gain this kind of extra credit, be sure to send me your completed review by Sunday, December 6.
  • The second opportunity for extra credit is four points added to your final grade for participation in a research study that is trying to understand why some students do better in college courses so that we can help everyone do better. Participation is voluntary and confidential; it involves you taking a few minutes to answer a few survey questions about how you learn, to create a “concept map” describing your current thoughts about social networks, and to give me permission to follow your grades over the course of the semester to see how you do in class. To gain extra credit for this study, simply log on to our course Blackboard site at courses.maine.edu, head to the online section marked “Study,” and complete all items in that section. To gain extra credit for the study, you must complete the items in the “Study” section by Sunday, September 8.

How to Calculate your Grade:

Midterm Exam (0-100 points) _____ * .25 = _____
Final Exam (0-100 points) _____ * .25 = _____
Class Participation (0-100 points) _____ * .25 = _____
Homework (0-100 points) _____ * .25 = _____
Subtotal: = _____
Plus extra credit points (0-7 points) _____ = _____
Final Grade: = _____

Final Grade Range
A: 93 – 100
A-: 90 – 92.99
B+: 87 – 89.99
B: 83 – 86.99
B-: 80 – 82.99
C+: 77 – 79.99
C: 73 – 76.99
C-: 70 – 72.99
D+: 67 – 69.99
D: 63 – 66.99
D-: 60 – 62.99
F: 0 – 59.99

If you are determined to earn a high grade in this class, good for you! If you put in enough careful work you can do it. I encourage you to ask questions in class when you are unsure and to see me in office hours if you need help understanding a lecture or reading. Don’t wait until late in the semester to seek help: get in touch as soon as problems begin to accumulate.

Course Schedule of Assignments and Readings

All readings, lectures, homework and other assignments should be completed by the end of the week under which they are listed.


Week 1 (August 31 – September 6): Introduction to Social Networks and the Social Networks Course


Summary: This week serves as an introduction to the expectations for this social networks course. I'll lay out just what you need to do attain the objectives of the course, learn how to conduct a social network analysis, and earn a good grade to boot. A good start to the course will help you end well.

We'll also take an initial look at the incredibly simple basis of even the most complicated social network analysis. The idea of the social network is perhaps the simplest idea in all of sociology, since a network consists of nothing more than:

  1. A node, something that communicates with other nodes, and
  2. A tie, the path of communication between two nodes.

Everything having to do with social networks can be boiled down to descriptions of patterns in nodes and ties. Social network analysts have the audacity to claim that if you can describe patterns of nodes and ties in communication, you can understand our social world.

Work for Week 1:


Week 2 (September 7-13): Fundamental Concepts and History of Social Network Analysis


Summary: We dive into the meat of the course this week by considering the essential elements of social network analysis, nodes and ties, in the context of their historical origin of ideas about nodes and ties in psychology, sociology, anthropology and mathematics -- all thanks to a man in 18th century Prussia who didn't want to cross a bridge twice.

Work for Week 2:

  • Read Prell, Chapter 2
  • Read Hansen et al., Chapter 3
  • Read and Participate in Lecture #2 by September 13: Click this link to access Lecture #2
  • Homework #1, due by 11:59 pm Eastern time on Sunday, September 13:
    1. Read the entire course syllabus.
    2. Read the entire UMA Student Academic Integrity Code.
    3. Send on any questions or concerns you may have regarding the syllabus and the Student Academic Integrity Code by e-mail to james.m.cook@maine.edu; I’ll respond as promptly as possible.
    4. Complete a quiz about the syllabus and academic integrity code by Sunday September 13 at 11:59 pm. The quiz is listed as “Quiz #1” in the “Homework Assignments” section of our course Blackboard page (look for the “Homework Assignments” link on the left-hand side of our Blackboard page — accessible on the web via http://courses.maine.edu.

Week 3 (September 14-20): Characterizing Complete Networks


Summary: A "complete network" is a network in which all possible nodes within a specified boundary are accounted for and all possible ties between those nodes are measured. For actors in a complete network, network analysts calculate various characteristics that express ideas of "centrality," from betweenness to gregariousness to popularity. But the network itself also has characteristics that can be measured, including size, density, centralization and diameter. Your job this week is to become familiar with these characteristics.

Work for Week 3:

  • Read Prell, Chapter 4 and Chapter 8
  • Read Hansen, pp. 34-36
  • Read and Participate in Lecture #3 by September 20: (click this link to access Lecture 3)
  • Homework #2 (adapted shamelessly from Prof. James Moody’s excellent social networks course), due by 11:59 pm Eastern time on Sunday, September 20:
    1. Consider the network representing all family ties within a network walk of two steps from you (that is, at a network distance of two from you). The only two kinds of family ties you should include and represent in your network are 1) the tie between a parent and a child and 2) the tie between two spouses or romantic partners. Be sure to include those parents and children who may no longer be living. Include only current spouses or romantic partners. Here are the steps to follow:
      • Guess how many people will be contained in your network. Include your guess as part of your homework assignment.
      • Create an edge list of all nodes and ties through a method called snowball sampling: Start with yourself and name all the people who are your parent, child, spouse or partner. These are the nodes one step removed from you. Then list all the people who are the parent, child, spouse or partner of your parents, children, and spouse/partner. Some of these people will already have been listed in the prior step; the rest of them are nodes two steps removed from you. Be sure to give each person some kind of a name: it may be a first name only, a pseudonym, or descriptions like “Agatha’s Father” if you don’t know the person’s name.
      • Convert this edge list into a matrix. Be sure to label each row and column.
      • Convert this edge list into a sociogram (also called a network graph). Draw the sociogram in Microsoft Word using the (Insert->Shapes) command. Be sure to label each node.
      • How many people are actually included? Compare that number to your guess.
      • Post all your work in a single word processing file to the appropriate Blackboard assignments section labeled “Homework #2.”

Week 4 (September 21-27): Conducting Social Network Research and using NodeXL


Summary: Enough with the highfalutin; this week we get practical, jumping into an easy-to-use and potentially powerful computer program called NodeXL. This week you'll set up NodeXL and you'll run your very first social network computer procedure with original data. Readings in the Hansen book will guide you through some exercises to help you get a handle on controlling the NodeXL template, and this week's homework will guide you through the process of entering information and visualizing a network in NodeXL. If you follow the exercises and the homework step by step, you should do fine. The more you practice these techniques, more sense they'll make.

Work for Week 4:

  • Read Hansen et al., Chapters 4-6
  • Read and participate in Lecture 4 by September 27: (click this link to access Lecture 4)
  • Homework #3, due by 11:59 pm Eastern time on Sunday, September 27:
    1. Gain access to NodeXL software.
      • Option 1: Download the free NodeXL template for Microsoft Excel from the Social Media Research Foundation and install it onto your Windows computer.
      • Option 2: Find and load NodeXL software at the Augusta Randall Student Center computer lab, the Bangor Eastport Hall computer lab, or a University College center.
    2. Following the example of pp. 54-55 in the Hansen text, enter an edge list in the “Edges” tab in NodeXL to represent the family network you created in Homework #2.
    3. Follow the examples in Chapter 4 of the Hansen et al. text to generate a sociogram using NodeXL. Make sure to use different colors and shapes of nodes to refer to different kinds of people in your family network. Label all nodes as well. Arrange the location of nodes, either manually or through one of the automatic options discussed in Chapter 4, so that you feel they best represent the structure of your family network.
    4. Following the instructions on Hansen et al. p. 85, copy the image of your sociogram and paste it into a word processing document.
    5. Using the “Graph Metrics” command, determine the degree centrality, betweenness centrality, eigenvector centrality and closeness centrality of each node. Also determine the density and diameter of the network. Report these in the same word processing document.
    6. Post that word processing document to the appropriate Blackboard assignments section labeled “Homework #3.”

Week 5 (September 28 – October 4): Ego Networks


Summary: This week's lecture is self-centered. Ego networks are literally egocentric creations, starting from a single person who looks at the networked world from her or his unique vantage point. In lecture and reading, we'll begin by considering ego alone, then coax the egoist into charting ties she or he may not see. Although ego networks are not complete networks, they are perhaps more accurate representations of the way the social world looks to us; as we go about living our lives, our range of vision is limited, not global.

This is also a week to continue moving beyond pencil and paper to representing social networks using computer programs. This week, you'll use NodeXL to create sociograms of ego networks at three levels.

Work for Week 5:


Week 6 (October 5-11): Midterm Exam



Week 7 (October 12-18): Groups and Affiliations


Summary:

This week, we consider Ronald Breiger's insight that persons and groups have an importance in social networks that is "dual": while shared group memberships lead to the formation of interpersonal ties, the people who are members of two groups also create ties between groups. As Scott Feld puts it more broadly, groups "focus" interaction, and that means we can describe groups using network analysis. Christina Prell's chapter also discusses distinctions people make within groups, from components to cliques to cores and beyond.

Work for Week 7:

  • Read Prell, Chapter 7
  • Read Scott Feld: “The Focused Organization of Social Ties
  • Read and Participate in Lecture 7 by October 18: (click here to access Lecture 7)
  • Homework #4, due by 11:59 pm Eastern Time on Sunday, October 18:
    • Visit the web page of the Fortune 500, the five hundred corporations registered with and operating in the USA with the largest revenues. Choose 6 of these companies.
    • For each company, find a list of the people sitting on its board of directors.  The Fortune 500 web page features links to company web pages, which almost always contains a list of the members of its board of directors.
    • Using Microsoft Excel, enter the membership data for these boards of directors as a 2-mode affiliation matrix in which columns are companies and rows are members of boards.  Enter board members’ names as row titles (format “Last Name, First Name“) and enter companies’ names as column titles.
    • Still using Microsoft Excel, convert the 2-mode matrix into a 1-mode persons-by-person matrix and a 1-mode board-by-board matrix.
    • Upload your Excel document to the appropriate Blackboard assignments section labeled “Homework #4.”

Week 8 (October 19-25): Similarities and Differences in Networks


Summary:

2-mode matrices aren't just for describing group memberships. They can also be used to describe closeness or distance, similarity or difference. Do the matrices that result describe networks or something else? Consider that question as you work through this week's readings and lecture.

Work for Week 8:


Week 9 (October 26 – November 1): Patterns in Social Networks


Summary:

This week is organized around the idea that social networks don't just happen randomly. There are patterns in social networks which have a strong impact on human existence even if individual humans have little choice in the matter. Unless we are very unusual, our friends will have more friends than we do. Although a catastrophic event may seem to strike a small number of people, the nature of networks generates a ripple effect that quickly touches us all.

Work for Week 9:


Week 10 (November 2-8): Networks Matter: A Question of Politics


Summary:

Politics is a collective act in which people deliberate and answer the question, "What is to be done?" Lives and livelihoods lie in the balance. But although political scientists have traditionally considered politics to be the realm of individuals thinking very hard and making rational decisions, social network analysis has revolutionized our understanding of political behavior. For example, what if the Congress is not a unique set of exceptional leaders, but just another group of people building and maintaining ties to one another like any other? What if businesses and political action groups trying to influence the Congress can be thought of as networks too? We're going to try to find out.

Work for Week 10:

  • Homework #6, due by 11:59 pm Eastern time on Sunday, November 8:
    1. Visit the Open Maine Politics database at openmepolitics.com. Using the search box in the upper-right corner of the page, find a small set of 4-6 bills before the 127th Maine State Legislature that have to do with a subject of interest to you. Bills are numbered as LDs [“Legislative Documents”].
    2. For each bill, you may find the list of sponsors (legislators who have signed on in support of the bill) by clicking on the name of the bill in your search results.  Using Microsoft Excel, enter the sponsorship data for these bills as a 2-mode affiliation matrix in which columns are bills and rows are sponsorsing legislators.  Enter sponsors’ names as row titles (format “Last Name, First Name“) and enter bill numbers as column titles (format “LD Number”“).
    3. Still using Microsoft Excel, convert the 2-mode matrix into a 1-mode legislator-by-legislator matrix and a 1-mode bill-by-bill matrix.
    4. Represent the results of your work above as an edge list, as a matrix, and as a sociogram (network graph).  When you create your sociogram, use NodeXL.  Represent tie strength by labeling your edges.
    5. Create a word processing document containing your edge list, your matrix, your sociogram, and some reflections on the patterns you notice in your political network.  Post this word processing document to the appropriate Blackboard assignments section labeled “Homework #6.”

Week 11 (November 9-15): Networks Online


Summary:

When you hear the phrase "social network," does your mind jump to thoughts of Facebook, Twitter and Pinterest? Social networks exist offline, to be sure, but in a very short amount of time a very large number of people have become users of online social networks. These networks track your behavior patterns; how can you in turn learn to track patterns of the networks you use? Follow the path set out in this week's and next week's work and you'll be on your way.

Work for Week 11:

  • Read Hansen et al., Chapter 1
  • Read “Do Not Sell Your Friends,” Chapter 7 in Douglas Rushkoff’s Program Or Be Programmed. This reading is available to officially registered students on Blackboard. Log on this course’s Blackboard page and look for the “Rushkoff” link on the left-hand side of the main page.
  • Watch the “What is Statusboom?” video:
  • Read and Participate in Lecture 11: Networks Online by November 15 (click here).
  • Homework #7, due by 11:59 pm Eastern time on Sunday, November 15:
    1. In this homework assignment, you will characterize a social network on Twitter using the NodeXL Twitter Import feature. To use this feature, you must first sign up for an account at Twitter.com. You are not required to actually use the Twitter service yourself in order to carry out this research; you only have to sign up for an account.  You may do this using any e-mail address; Twitter accounts are free of charge.
    2. Open NodeXL, be sure that you have the NodeXL tab selected on top, and select Import->From Twitter Search Network…
    3. The first time you use NodeXL Twitter search, a pop-up window should appear. In order to carry out Twitter searches, you must authorize NodeXL to be used by your Twitter account. Select the option in the pop-up window that reads “I have a Twitter account, but I have not yet authorized NodeXL to use my account to import Twitter networks. Take me to Twitter’s authorization page.” After you authorize NodeXL, you’ll be given a verifying number to enter back within the NodeXL program.
    4. Now, back in the Twitter Search import window, make sure the option “Limit to ____ Tweets” is set to 500. Check the box that reads “Expand URLs in Tweets.” “Tweets” is just another word for posts on Twitter.
    5. Enter “#mepolitics” (without quotes) in the top-most box, right above the words “How to use advanced search operators.” Then click “OK” to start collecting tweets.
    6. Visualize the network as you deem fit to most suitable express the patterns you find in how people are talking about Maine politics over Twitter. Copy and paste the resulting sociogram into a Microsoft Word document.
    7. Click the “Graph Metrics” option at the top ribbon of NodeXL and, following the examples of your Hansen text, describe the network using appropriate network measurements.  Also be sure to look through the actual text of the “Tweets” that result to get an idea what posters are talking about.  In the same Microsoft Word document, report who is talking in what social circles using the #mepolitics hashtag.
    8. Post a word processing document containing the above information to the appropriate Blackboard section labeled “Homework #7.”

Week 12 (November 16-22): Finding and Tracking Networks Online, Part II


Work for Week 12:

  1. Last week, you engaged in a guided use of NodeXL’s Twitter Search function to generate a Twitter network for the hashtag #mepolitics. This week, I’d like you to conduct and report on your own search, generating your own Twitter network. Click here to learn more about how to search Twitter.
  2. Choose a hashtag, a user, a list or a snippet of text to search through and try your own search, limiting the results to either 500 or 1000 “Tweets.” Take some time to experiment with different searches until you find a Twitter network interesting to you. Use the visualization options in NodeXL to look at the structure of conversation and decide what’s interesting.
  3. Having found an interesting Twitter network, create a meaningful sociogram in which you use space, node design and edge design to depict patterns in who is having Twitter conversations with whom. Include this sociogram in your assignment.
  4. Referring to Chapter 5 of the Hansen text and your work earlier this semester in measuring network characteristics, use the Graph Metrics command in NodeXL to measure as many network characteristics regarding your Twitter network as possible. Be sure to report these network characteristics in your assignment.
  5. Referring to the sociogram and network characteristics where appropriate, discuss your choice of a Twitter search and patterns in the resulting network that you obtained. What of importance do these results indicate?
  6. Post a word processing document containing the above information to the appropriate Blackboard section labeled “Homework #8.”

November 23-29: Thanksgiving Break (no assignments)



Week 13 (November 30 – December 6): Networks Matter: A Question of Surveillance


Summary:

The tools of social network analysis are not only interesting from an abstract academic point of view; they have practical use as well. Those who can track our associations can influence our fate. In the last year, new information about the nature and extent of government surveillance of our communications and associations has been revealed. At the highest levels of government, the tools of social network analysis are being used -- to what end?

Work for Week 13:


Week 14 (December 7-13): Conclusion and Review


Summary:

In this course we have deviated from the American myth of the independent individual to learn how networks of relations and affiliations shape the fate of individuals, groups, communities and societies. In this week's lecture, I'll try to draw together some of the broadest themes of our semester's work. This need not be the end; I'll finish the lecture by considering some paths forward for you in your undergraduate and professional careers.

Work for Week 14:


Week 14 (December 14-19): FINAL EXAM, due by 11:59 pm Eastern Time, December 19

Final exam will be posted on Saturday, December 12.


Academic Integrity

The following is a verbatim quote of the Student Academic Integrity Code for all students at the University of Maine at Augusta. The words below not only describe the general expectations of UMA for all students — that your work must be your own — but my particular expectations for your conduct in this class. You are responsible for learning the standards of academic integrity and ensuring that your work meets these standards. Failure to do so may result in appropriate sanctions — and nobody wants you to end up in that circumstance. If you have any questions about whether you might be violating standards of academic integrity, do two things: First, stop. Second, if you’re in doubt, consult with me to find out what the right course of action would be.

“Plagiarism: the representation of others’ words or ideas as one’s own. For example,

  • Submitting as one’s own work an examination, paper, homework assignment, or other project (laboratory report, artistic work, computer program, etc.) that was created entirely or partially by someone else.
  • Failure to use quotation marks to signal that one is using another person’s precise words. Even brief phrases must be enclosed in quotation marks.
  • Failure to identify the source of quotations and paraphrases. Of course one must cite the source of quotations; one must also cite the source of ideas and information that is not common knowledge even when paraphrased (presented in one’s own words). Sources include unpublished as well as published items — for example, books, articles, material on the Internet, television programs, instructors’ lectures, and people, including other students, friends, and relatives.
  • Creating an academically dishonest paraphrase. When paraphrasing the author must find their own way of expressing the original meaning. Simply inserting synonyms into the source’s sentence structures is plagiarism.
  • Failure to identify the source of the elements of a nonverbal work (for example, a painting, dance, musical composition, or mathematical proof) that are derived from the work of others.

“Cheating: the use or attempted use of unauthorized assistance in an examination, paper, homework assignment, or other project. For example,

  • Copying answers from another student’s examination.
  • Communicating in any way with another student or a third party during an examination without the permission of the instructor.
  • Using unauthorized materials or devices (e.g. notes, textbooks, calculators, electronic devices) during an examination without the permission of the instructor.
  • Obtaining and/or reading a copy of an examination before its administration without the permission of the instructor.
  • Collaborating with other students or third parties on a take-home examination, paper, homework assignment, or other project without the permission of the instructor.

“Additional violations of academic integrity include:

“Duplicate Work: Submitting a paper or other project in more than one course without the permission of the instructors. Students are expected to produce original work for each course. A student should not submit identical or substantially similar papers or projects in two different courses (in the same or different semesters) unless both instructors have given their permission.

“Facilitating Academic Dishonesty: assisting another student’s academic dishonesty. For example,

  • Writing a paper or other project for another student.
  • Permitting another student to copy from one’s examination, paper, homework assignment, or other project.
  • Assisting another student on a take-home examination, paper, homework assignment, or other project if one knows or suspects such assistance is not authorized by the instructor.

“Fabrication: For example,

  • Fabrication of data: Inventing or falsifying the data of a laboratory experiment, field project, or other project.
  • Fabrication of a citation: Inventing a citation for a research paper or other project.
  • Alteration of an assignment: Altering a graded examination, paper, homework assignment, or other project and resubmitting it to the instructor in order to claim an error in grading.”

In this class, I encourage you to share lecture notes with other students and to study together for the exam. But you may not collaborate with other students on graded assignments, and you may not rely on anything other than your pen and your brain during an examination.

Changes to this Syllabus

I do not anticipate the need to make any changes to this syllabus, but I reserve the right to do so under extenuating circumstances or as unforeseen events may warrant. Should I make any changes to the syllabus, I will communicate these in advance and through multiple means — by changing the syllabus itself, by making an announcement in class and by posting message to the course Blackboard website.

CyberChimps