Social Media Mining (Wintersemester 2019/2020)

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.-31.10.2019
• 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 or visualize the dynamics of social media, various data mining technologies will be used within the selected topics in this seminar. So far, our reseach group are working extensively at individuals Personality prediction from his/her posts, likes, friends network & profile picture. Also, we are working at malicious behaviour identification for humans and bots at various social platforms.

 Please find the topics presentation here (2019): owncloud.hpi.de/s/WcQKB5jkb2tkNPX

Requirements

Practical knowledge in:

• Operating Systems and Software Engineering
• Interest in Data Science and social media platforms
• Internet Basics and Conceptes
• Software development with Python or R

Literature

Selected publications: 

• 2017, BinTareaf, Raad and Berger, Philipp, and Hennig, Patrick and Koall, Sebastian and Kohstall, Jan and Meinel, Christoph
  Information Propagation Speed and Patterns in Social Networks: a Case Study Analysis of German Tweets
• 2017, BinTareaf, Raad and Berger, Philipp, and Hennig, Patrick and Jung, Jaeyoon and Meinel, Christoph
  Identifying Audience Attributes - Predicting Age, Gender and Personality for Enhanced Article Writing
• 2018, BinTareaf, Raad and Berger, Philipp, and Hennig, Patrick and Meinel, Christoph
  Malicious Behavioural Identification in Online Social Networks
• 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

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

-Seminar Meetings: Thursday 17th of Oct 2019 @ A-1.1 (9:15-10:45)

At the first meetings, we will be discussing and providing all details and materials that our students may need. Feel free to attend in order to explore how we combine data science domain with other science domains as Psychology form social science.

-First meetings:

17/Oct - Topic Presentations (Lecturers will provide a set of varied topics to be investigated during the seminar)

24-31/ Oct - Team & Topic Assignment (Final date)

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