Syllabus

School of Media Arts & Studies
Scripps College of Communication

Laeeq Khan, Ph.D., M.B.A.

Office: 307, Schoonover Center, Athens, Ohio 45701

Email: khanm1@ohio.edu; Twitter: @drlaeeqkhan

Welcome to MDIA 4130 – Social Media Analytics

Course Description

Social Media Analytics

There is an increasing realization amongst social media managers that data generated via social media can enable informed and insightful decision-making. Individuals, organizations and businesses are employing social media analytics tools to better understand human behavior in online communities. This course introduces students to concepts, tools, and best practices in social media analytics. The course will also acquaint students with the use of various software tools and techniques for analyzing social media interactions.

Emphasis is on providing a thorough understanding of social media, analytics, and measurement strategies for organizations and businesses. Based on readings, cases studies, tutorials, and analytics assignments, the course will also focus on collection, analysis, and visualization of social media data to build analytics reports as part of an overall social media plan for an organization/business.

Course Objectives and Outcomes

  • To gain an understanding of social media analytics concepts, techniques, and tools.
  • To understand how social media data is obtained, analyzed and visualized.
  • To prepare social media analytics reports to inform executives/senior managers thereby impacting social media policy.
  • To understand how managers can make better strategic decision based on social media analytics.

Teaching Philosophy
My role as an educator derives its greatest strength from the realization that I can make a positive difference in the lives of others. I can contribute by helping create a nurturing environment for students, which leads to innovation and critical thinking. My approach to teaching reflects my experiences with my own teachers and mentors, as well as my belief that learning spaces help explore emerging ideas. Students need to be engaged learners. I subscribe to the Japanese concept of Kaizen or “continuous improvement”. Students can achieve their personal and professional best if they continue to make small changes every day, ultimately leading to substantial positive impacts overtime. The process of continuous improvement demands that students reflect upon their daily routines.

Expectations

In general, students are expected to:

  • Be respectful to the instructor, teaching assistant, and others students; and,
  • Complete and submit original work by given deadlines.
  • Advance their understanding of social media analytics.

Readings & Assignments

Students are expected to complete all assigned readings. Please note that some of the course activities such as case studies and assignments test knowledge of the readings/lecture for that week. Students are required to submit their assignments according to the deadlines announced. Late assignments are not accepted. If you face any unforeseen circumstances that may inhibit your ability to submit your work on time, you must communicate with the course instructor.

Going on a family trip, attending a wedding, a conflict with work schedule, having a family vacation booked before the semester started, and any other personal matter are NOT considered legitimate reasons for missing assignments, activities, cases, or the final exam, and thereby, do not qualify for a make-up.

 

Participation and Behavior

Participation is important in this course. Every student is expected to be active in this course.

  • Missed assignment will result in a zero.
  • Disability accommodations: Bring documentation.
  • Inform me at least a week in advance regarding religious holidays.
  • Alert me ASAP if you see or experience online harassment or bigotry during this course.

If you have questions about course content or specific assignments, you should post them to the “Cafe Analytics” discussion forum on Blackboard. This will provide opportunities for other class members to benefit by receiving additional clarification on course issues and materials.

Academic Honesty

I have a very strict policy regarding academic honesty (please refer to Ohio University’s Student Code of Conduct (https://www.ohio.edu/communitystandards/academic/). Academic dishonesty “includes, but is not limited to: cheating, plagiarism, un-permitted collaboration, forged attendance (when attendance is required), fabrication (e.g., use of invented information or falsification of research or other findings), using advantages not approved by the instructor (e.g., unauthorized review of a copy of an exam ahead of time), knowingly permitting another student to plagiarize or cheat from one’s work, submitting the same assignment in different courses without consent of the instructor.”

Please ask if you are not clear about plagiarism. It is a serious offense. Failure to comply with university policies in this regard would result in a failing grade in this course. Refer to the following link for details: https://www.ohio.edu/communitystandards/academic/students.cfm

 

Course Format and Procedures:

There is no required textbook for this course. All readings will be available on Blackboard website. The grading scale is as follows:

 

Grade % Grade % Grade % Grade %
A 94-100 A- 90-93 B+ 87-89 B 84-87
B- 80-83 C+ 77-79 C 74-76 C- 74-76
D+ 67-69 D 64-66 D- 60-63 F <60
  1. Course Evaluation and Grading:
  • Analytics Assignments

Students will participate in pairs of two in writing analytics assignments to demonstrate their understanding of the course concepts and readings. These analytics assignments (a total of 2) will count toward the total grade in the course. Further detailed guidelines will be provided for the assignments on Blackboard/Course Website.

 

  • Case Study Analysis & Forum Posting

All students are expected to contribute to the discussion of the cases. For each case study, five questions will be posted in a forum on Blackboard/Course Website. Each student is required to pick two questions of their choice and answer them based on the case study. There is a total of four cases comprising 50 points each.

 

  • Project Proposal Meetings

All students are required to set up meeting times as a team to discuss the project proposal in my office (during office hours). Signup sheet will be provided to schedule time slots for this mandatory activity.

 

  • Social Media Analytics Report & Poster Presentation

Students will work in groups of 6 (or more) students on a final project for the course (groups are randomly assigned by the instructor at the beginning of the semester). The final project will entail 15 – 20 pages (12-point font, double-spaced, Times New Roman) social media analytics report for a real-world client, in addition to a 10-minute poster presentation. The purpose of the final project is to apply the knowledge learned throughout the semester to solving a practical problem using social media analytics. More details will be provided later on in the semester.

 

 

The overall course grade will be based on students’ achievements in the following areas:

Grading Rubric
ITEM POINTS %
Analytics Assignments (200+200) 400 40%
Case Studies (50 x 4) 200 20%
Social Media Analytics Report & Poster Presentation (300+100) 400 40%
TOTAL 1000 100%