Social Media Mining (Wintersemester 2017/2018)
Dozent:
Prof. Dr. Christoph Meinel
(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, Modulgruppen & Module
- IT-Systems Engineering
- IT-Systems Engineering
- IT-Systems Engineering
- IT-Systems Engineering
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-S Spezialisierung
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-T Techniken und Werkzeuge
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
Beschreibung
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
Voraussetzungen
Good knowledge in
- Operating Systems and Software Engineering
- Internet Basics
- Data Mining Techniques
Literatur
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
Writing
Leistungserfassung
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)
Termine
Regular Meetings: Tuesdays, 9.15-10:45. Room A-1.2
First meetings:
17/24.10.2017 - Topic Presentation
31.10.2017 - Topic Assignment
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