Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI

Social Media Mining (Wintersemester 2017/2018)

Dozent: Prof. Dr. Christoph Meinel (Internet-Technologien und -Systeme) , M.Sc. Raad BinTareaf (Internet-Technologien und -Systeme)

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 27.10.2017
  • Lehrform: Seminar / Projekt
  • Belegungsart: Wahlpflichtmodul
  • Maximale Teilnehmerzahl: 12

Studiengänge & Module

IT-Systems Engineering MA
  • ITSE-Analyse
  • ITSE-Entwurf
  • ITSE-Konstruktion
  • ITSE-Maintenance
  • ISAE-Spezialisierung
  • ISAE-Techniken und Werkzeuge
  • OSIS-Konzepte und Methoden
  • OSIS-Spezialisierung
  • OSIS-Techniken und Werkzeuge


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.

 Please find the topics presentation here: https://owncloud.hpi.de/index.php/s/JYufb0f7PjjVwjT


Good knowledge in

  • Operating Systems and Software Engineering
  • Internet Basics
  • Data Mining Techniques


Checkout our Papers:

  • 2010, Bross, Justus and Quasthoff, Matthias and Berger, Philipp and Hennig, Patrick and Meinel, Christoph
    Mapping the blogosphere with rss-feeds
  • 2010, Bross, Justus and Berger, P and Hennig, P and Meinel, Christoph
    RSS-Crawler enhancement for blogosphere-mapping
  • 2011, Berger, Philipp and Hennig, Patrick and Bross, Justus and Meinel, Christoph
    Mapping the Blogosphere--Towards a universal and scalable Blog-Crawler
  • 2013, Hennig, Patrick and Berger, Philipp and Meinel, Christoph
    Identify emergent trends based on the blogosphere
  • Hennig, Patrick and Berger, Philipp and Godde, Christian and Hoffmann, Daniel and Meinel, Christoph
    A Fuzzy, Incremental, Hierachical Approach of Clustering Huge Collections of Web Documents
  • 2013, Berger, Philipp and Hennig, Patrick and Klingbeil, Thomas and Kohnen, Matthias and Pade, Steffen and Meinel, Christoph
    Mining the Boundaries of Social Networks: Crawling Facebook and Twitter for BlogIntelligence
  • 2013, Hennig, Patrick and Berger, Philipp and Meinel, Christoph and Graber, Maria and Hildebrandt, Jens and Lehmann, Stefan and Ramson, Cathleen
    Tracking Visitor Engagement in the Blogosphere for Leveraging Rankings
  • 2013, Hennig, Patrick and Berger, Philipp and Meinel, Christoph
    Web Mining Accelerated with In-Memory and Column Store Technology
  • 2013, Berger, Philipp and Hennig, Patrick and Meinel, Christoph
    Identifying Domain Experts in the Blogosphere--Ranking Blogs Based on Topic Consistency
  • 2014, Berger, Philipp and Hennig, Patrick and Detje, Stephan
    BlogSphere-A Topical Map of the Blogosphere
  • 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


The final evaluation will be based on:

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


Regular Meetings: Tuesdays, 9.15-10:45. Room A-1.2

First meetings:

17/24.10.2017 - Topic Presentation

31.10.2017 - Topic Assignment