MDIA 5900 – Social Media Analytics
School of Media Arts & Studies
Scripps College of Communication

Ohio University, Athens, Ohio, United States

 Professor: Laeeq Khan, Ph.D., M.B.A.

Office: 307, Schoonover Center, Athens, Ohio 45701

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

Class Meeting Times, Location 12:00 – 1:20 PM; Tuesday & Thursday, SMART Lab

[Office Hours: Tues, Thurs 1:30 – 3:00 PM]


  1. Course Description

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.

2. 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.

3. 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

4. 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.

5. Expectations

Students are expected to complete all assigned readings and 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.

6. Grading Scale

There is no required textbook for this course. All readings will be available on the course 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


7. Course Evaluation and Grading:

  • Analytics Assignments

Students will participate individually writing 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.

  • Research Article Analysis

Each student will participate in individual research article analysis and discussion. Students will be assigned articles in the realm of social media analytics that will be presented in class on a chosen date. Student presenting an article will lead the in-class discussion.

  • 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).

  • Final Project Report & Presentation

Students will individually work on a final project for the course. The final project will comprise 20 – 25 pages (12-point font, double-spaced, Times New Roman) based on a data analytics research paper, in addition to a 10-minute presentation (presentation slides). The purpose of the final project is to apply the knowledge learned throughout the semester. 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
Analytics Assignments (100+100) 200 20%
Research Article Analysis and Forum Posting 200 20%
Final Project Report & Presentation (500+100) 600 60%
TOTAL 1000 100%

Course Schedule