Prof. Dr. Felix Naumann

Maximilian Jenders

für Softwaresystemtechnik
Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam

Phone: +49 331 5509 289
Fax: +49 331 5509 287
Room: E-2.01-2
Email:  Maximilian Jenders


Research Interests

  • Web Mining
  • Opinion Mining
  • Text Mining
  • Newspaper Text Analysis
  • Text Recommendation
  • Social Media Analysis
  • Machine Learning
  • Data Mining
  • MOOC courses


Co-Supervised Master's Theses:

  • Lukas Schulze: Profiling Log Messages For Unknown Error Detection (finished, 2015)
  • Tobias Schubotz: Online Temporal Summarization of News Articles (finished, 2014)
  • Mandy Roick: A Topic-Based Search for Microblog Posts (finished, 2014)
  • Thorben Lindhauer: A Content-Based Serendipity Model for News Recommendation (finished, 2014)




Analyzing and Predicting Viral Tweets

Jenders, Maximilian; Kasneci, Gjergji; Naumann, Felix in Proceedings of the WWW '13 Companion: 22nd International World Wide Web Conference Rio de Janeiro, Brazil , 2013 .

Twitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of opinion formation and many other opinion mining tasks. This paper addresses important questions concerning the spread of information on Twitter. What makes Twitter users retweet a tweet? Is it possible to predict whether a tweet will become "viral", i.e., will be frequently retweeted? To answer these questions we provide an extensive analysis of a wide range of tweet and user features regarding their influence on the spread of tweets. The most impactful features are chosen to build a learning model that predicts viral tweets with high accuracy. All experiments are performed on a real-world dataset, extracted through a public Twitter API based on user IDs from the TREC 2011 microblog corpus.
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