605.633.81 - Social Media Analytics

Course Structure

The course materials are divided into modules which can be accessed by clicking Course Modules on the course menu. A module will have several sections including the overview, video lectures and content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.

Course Topics

• Data and Application Program Interface (API) for social media platforms
• Graph Theory
• Centrality Measures
• Social Theory
• Relational Algebra
• Natural Language Processing
• Machine Learning
• Fake News and Disinformation
• Sentiment Analysis
• Topic Modeling
• Statistical Analysis of Network Data
• Network Interventions
  
• Network Visualization

Course Goals

To understand an end-to-end approach to analyze social media, online media campaigns, and measure the behavior of personas interacting with social media technology. Apply this understanding to capture social media data using open source tools and provide mature analysis to inform and advise executives on social media policy and engagement.

Course Learning Outcomes (CLOs)

• 1. Describe the end-to-end process for performing social media analytics, to include terminology, common tools, and applications.
• 2. Ingest social media data and render network visualizations.
• 3. Describe and distinguish between accuracy, validity, repeatability, and error in social media data collection and representation.
• 4. Perform basic social network analysis of social networks to include centrality, subgroup analysis, and statistical analysis of networks.
• 5. Explain and differentiate between network-based social theories for understanding how populations engage with social media as defined in the course readings and lectures.
• 6. Characterize social media clusters and discourse using machine learning.
• 7. Generate network visualizations to convey maximum information and then critique (assess) in terms of the principles for conveying maximal information to the intended audience.
• 8. Explain the counter-intuitive nature of social media influence.

Textbooks

No textbook is required for this course.  There is an optional textbook that you may find helpful:

McCulloh, I., Armstrong, H., & Johnson, A. (2013). Social network analysis with applications. John Wiley & Sons. ISBN: 9781118169476

Required Software

Basic proficiency in R, Python, or other scripting language. The course will use R and Python, however, students will be provided with an easy to follow tutorials. Students should install the free open source version of RStudio on their computer prior during the first week of class. This software can be located at https://www.rstudio.com/products/RStudio/.

Prior to Module 2 students should install a Python IDE on their computers. PyCharm is used in guest lectures and can be downloaded at: https://www.jetbrains.com/pycharm/download/. It is also acceptable to use RStudio as your Python IDE for consistency.

You should refer to Help & Support on the left menu for a general listing of all the course technical requirements.

Student Coursework Requirements

A grade of A indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week. A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.

Final grades will be determined by the following weighting: 

Homework 25%

Mid-Term Exam 25%

Final Project/Paper 40%

Discussion Participation 10%

Each week, students will apply lesson concepts on a real-world data set of their choosing. Each assignment will build on the previous week assignment. Students will complete a mid-term exam at the end of week 7 and a final project/paper at the end of the course. The intent is for students to leave the course with an empowered, integrated understanding of social media analysis and a finalized project to begin a portfolio of their capability. Students will also be expected to participate in an open discussion forum to further explore topics and challenges in social media analytics. The field of social media analytics continues to evolve and there are many technical challenges in this field.

I highly recommend that you collect a social media data set that interests you during your Module 3 assignment and use that data set for subsequent module assignments.  There is no intent to create busy work in this course.  All assignments are intended to build the needed skills to enable you to publish a novel research finding by the end of the semester.

Grading Policy

Homework is expected to be turned in on the website as indicated in the assignment tool; it will be considered late if it is received after that time. Special circumstances (e.g., temporary lack of internet access) can be cheerfully accommodated if the student informs us in advance. Homework that is unjustifiably late will have the grade reduced for lateness.

Assignments will mostly quantitative problem sets, and programming code. Include a cover sheet with your name and assignment identifier. Also include your name and a page number indicator on each page of your submissions. Hand written submissions that are scanned as a pdf or jpeg are perfectly acceptable as long as hand writing is clear and legible.  For the homework on network centrality, we recommend a hand written submission for calculations.

I generally do not directly grade spelling and grammar. However, egregious violations of the rules of the English language will be noted without comment. Consistently poor performance in either spelling or grammar is taken as an indication of poor written communication ability that may detract from your grade.

If, after submitting a written assignment a student is not satisfied with the grade received, the student is encouraged to redo the assignment and resubmit it. If the resubmission is correct with all deficiencies corrected, the students will receive 25% of the lost points.  For example, student X receives an 80%.  He resubmits the assignment with all portions correct.  His revised score will be 80 + .25*20 = 85%.  This must be completed within two weeks of receiving the grade for that assignment.  (you can’t wait until the last week of the course).

Quantitative assignments (to include midterm) are graded as follows:

100–90 = A—All parts of question are addressed; All intermediate derivations and calculations are provided and correct; Answer is technically correct and is clearly indicated; Answer precision and units are appropriate.

89–80 = B—All parts of question are addressed; Intermediate derivations and calculations are provided, but some mistakes are present; Generally student demonstrates an understanding of the correct solution; Answer is technically correct and is indicated; Answer precision and units are appropriate.

79–70=C—Most parts of question are addressed; Some intermediate derivations and calculations are provided or multiple mistakes are present;  Generally student demonstrates a weak understanding of the correct solution; Answer is not technically correct but is indicated; Answer precision and units are indicated.

<70=F—Some parts of the question are addressed; Intermediate derivations and calculations are not provided; The answer is incorrect or missing; The answer precision and units are inappropriate or missing. 

Note: Correct answers can be obtained and checked with software for most problems.  Therefore, we assume you will have the correct answer.  The homework evaluates your ability to work out the problems (or code) by reviewing your intermediate derivations and calculations.  Failure to provide these steps may result in a failing grade.

FINAL PAPER

A course project will be assigned several weeks into the course. The last week will be devoted to the course project. The final project will be evaluated by the following grading elements:

1. Writing quality and technical accuracy (30%). Writing is expected to meet or exceed accepted graduate-level English and scholarship standards. That is, all assignments will be graded on grammar and style as well as content.
2. Data collection (20%). Students are expected to successfully collect a relevant data set for analysis.  Data will be provided to the instructors in a format suitable for review (JSON, CSV) with an explanation of the data.
3. Meaningful Analysis (30%). Students will apply appropriate analytic methods taught throughout the course to provide a novel or interesting insight within their chosen data.
4. Formatted Correctly (10%). Students will identify a target conference proceeding or journal publication, review instructions to authors, and format their final paper appropriately.  Students will provide the instructors a link to the instructions to authors for grading.
5. References (10%). Students adequately cite previous relevant research to meet academic publishing standards.

100–90 = A—Paper is rich in content; full of thought, insight, and analysis; well-written; correctly formatted for target venue.

89–80 = B—Paper contains substantial information; thought, insight, and analysis has taken place; paper is written logically and easy to follow; correctly formatted for target venue.

79–70 = C—Paper is generally competent; information is thin and commonplace; poor writing; not formatted for target venue.

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.