Info
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Course 2019-2020 a.y.

20543 - SOCIAL MEDIA MARKETING

Department of Marketing

Course taught in English

Go to class group/s: 31 - 32

CLMG (6 credits - I/II sem. - OP  |  SECS-P/08) - M (6 credits - I/II sem. - OP  |  SECS-P/08) - IM (6 credits - I/II sem. - OP  |  SECS-P/08) - MM (6 credits - I/II sem. - OP  |  SECS-P/08) - AFC (6 credits - I/II sem. - OP  |  SECS-P/08) - CLELI (6 credits - I/II sem. - OP  |  SECS-P/08) - ACME (6 credits - I/II sem. - OP  |  SECS-P/08) - DES-ESS (6 credits - I/II sem. - OP  |  SECS-P/08) - EMIT (6 credits - I/II sem. - OP  |  SECS-P/08) - GIO (6 credits - I/II sem. - OP  |  SECS-P/08) - PPA (6 credits - I/II sem. - OP  |  SECS-P/08) - FIN (6 credits - I/II sem. - OP  |  SECS-P/08)
Course Director:
GAIA RUBERA

Classes: 31 (II sem.) - 32 (I sem.)
Instructors:
Class 31: DIRK HOVY, Class 32: GAIA RUBERA


Mission & Content Summary
MISSION

Nowadays, Big Data freely available on social networks enables managers to perform traditional marketing analyses much more efficiently, rapidly, and pervasively than in the past. However, the recent cases of Cambridge Analytica and social bots also clearly show the drawbacks of combining Big Data with Machine Learning algorithms. Given this new scenario, and in order to prepare students to face the new challenges of the Data Economy, this course introduces students to Python, one of the main programming language currently used in the Computer Science field. It also takes an in-depth look at social networks, with a specific emphasis on Twitter. Students learn how to collect real-time Twitter data through the use of an Application Program Interface (API) and how to conduct traditional marketing research and text analyses with this data. Besides providing students with practical skills on how to collect and analyze data, the course discusses the risks of the Data Economy, with a particular emphasis on how Machine Learning algorithms can be used to influence individuals' decisions.

CONTENT SUMMARY

The course is divided into 4 main blocks that cover the following topics:

  1. Introduction to Python syntax.
  2. Twitter APIs and Marketing Analytics with Python.
  3. Database management and introduction to text analysis with Python.
  4. Machine Learning applications to marketing topics.

Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Summarize and describe any dataset with Python.
  • Describe the strenght and weakeness of any brand / organization using Twitter data.
  • Explain how Machine Learning algorithms can be used to influence individuals' decisions.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Express basic programming commands in Python language.
  • Collect any type of freely data available on Twitter.
  • Estimate the competitive positioning of different brands according to Twitter data.
  • Summarize and describe any dataset with Python.

Teaching methods
  • Face-to-face lectures
  • Guest speaker's talks (in class or in distance)
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Group assignments
DETAILS

During the course, in addition to face-to-face lectures, the following activities are completed:

  • Guest speakers in class by managers working in the social media marketing area. These talks allow students to understand how multinationals are using data from social networks, and in particular Twitter, to conduct marketing analyses.
  • Practice sessions to apply the knowledge acquired in class to specific marketing problems.
  • Weekly, individual assignments to review the main Python codes learned in class.
  • Final group project jointly with a partner company (in the a.y. 2017-2018, this company was Nielsen).

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •   x  
  • Individual assignment (report, exercise, presentation, project work etc.)
  •   x  
  • Group assignment (report, exercise, presentation, project work etc.)
  •     x
  • Active class participation (virtual, attendance)
  • x    
    ATTENDING STUDENTS
    • Team Project: Paper: 45%
    • Short-case presentation: 5%
    • Final exam: open-ended + multiple choice questions: 50%
    • Extra-credit opportunity: Twitter participation: 1 point.
    NOT ATTENDING STUDENTS

    Exam: open-ended + multiple choice questions (You can consult the Tweepy documentation during the exam).


    Teaching materials
    ATTENDING STUDENTS

     

    NOT ATTENDING STUDENTS
    • TUTEN, SOLOMON, Social Media Marketing, second edition.
    • RUBERA, GROSSETTI, Python for non-Pythonians, Egea, (entire book).
    • Twitter API documentation available at

    https://media.readthedocs.org/pdf/tweepy/v3.2.0/tweepy.pdf

    Last change 16/06/2019 20:08