Social Media Mining (Wintersemester 2020/2021)

Lecturer: Prof. Dr. Christoph Meinel (Internet-Technologien und -Systeme) , M.Sc. Ali Alhosseini (Internet-Technologien und -Systeme)

General Information

• Weekly Hours: 4
• Credits: 6
• Graded: yes
• Enrolment Deadline: 01.10.-20.11.2020
• Teaching Form: Seminar / Project
• Enrolment Type: Compulsory Elective Module
• Course Language: English

Programs, Module Groups & Modules

Description

social media analytics refers to the science and discipline of deriving useful hidden insights from massive amounts of semi structured and unstructured data to enable knowledgeable and insightful decision making processes. However, it is increasingly difficult - if not impossible - for the average internet user and weblog enthusiast to grasp the blogosphere’s and social media platforms complexity as a whole, due to thousands of new weblogs and an almost uncountable number of new posts adding up to the before-mentioned collective on a daily basis. Therefore, mining, analyzing, modeling and presenting this immense data collection is of central interest. This could enable the user to detect technical trends, political atmospheric pictures or news articles about a specific topic.

In this seminar, we focus on understanding and analyzing social media streams from different platforms such as (Facebook, Twitter, Instagram, Blogs, Reddit, LinkedIn, Xing) to reveal potential relationships and to visualize the dynamics of the social platform. Several data mining technologies will be used within the selected topics in this seminar. Our reseach group are working extensively at cross domain studies as psychology in big data streams, individuals personality prediction from his/her posts, likes, friends network & profile pictures. Also, we are working at detecting and profiling bots at various social platforms using classical and advanced machine learning algorithms.

Requirements

Practical knowledge in:

• Good programming skills in object oriented languages, such as Python.
• Basic interest in Data Mining and social media platforms
• Internet Basics and Conceptes
• Software development with Python or R

Topics 2020 slides: https://owncloud.hpi.de/s/shbylvC01q4VOqb

Literature

Selected publications: 

• 2018, BinTareaf, Raad and Berger, Philipp, and Hennig, Patrick and Meinel, Christoph
  ASEDS: Towards Automatic Social Emotion Detection System Using Facebook Reactions
• 2018, BinTareaf, Raad and Berger, Philipp, and Hennig, Patrick and Meinel, Christoph
  Personality Exploration System for Online Social Networks: Facebook Brands As a Use Case
• 2019, Alhosseini, Ali and BinTareaf, Raad and Meinel, Christoph 
  Detect Me If You Can: Spam Bot Detection Using Inductive Representation Learning
• 2020, Seyed Ali Alhosseini, Raad Bin Tareaf, Christoph Meinel:
  Engaging with Tweets: The Missing Dataset On Social Media. RecSys Challenge 2020: 34-37

Examination

The final evaluation will be based on:

• Initial implementation / idea presentation, 15%
• Final presentation, 25%
• Report, 12-18p LNCS template, 30%
• Implementation, 15%
• Integration, 15%
• Participation in the seminar, paper review (bonus points)

Dates

All participating students will work in multiple groups with each group consists of a maximum of 3-4 students to work on a chosen topic. There will be weekly or bi-weekly meetings where the group will give the latest update/progress of the topic. The format of the seminar will be a mixed of on-site at HPI and online where each group can choose which option is suitable for the seminar.

The first meeting will be on-site in room A2.2, at 09.15 AM, 04 November 2020.

-First meetings:

XXX - First week : Topic Presentations (Lecturers will provide a set of varied topics to be investigated during the seminar)

XXX - Second and Third week: Team & Topic Assignment (Final date)

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