Hasso-Plattner-Institut
  
Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Maximilian Jenders

Hasso-Plattner-Institut
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

Supervisions

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)

 

 

Publications

Analyzing and Predicting Viral Tweets

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

Abstract:

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.

BibTeX file

@inproceedings{Maximilian2013a,
author = { Maximilian Jenders, Gjergji Kasneci, Felix Naumann },
title = { Analyzing and Predicting Viral Tweets },
year = { 2013 },
month = { 0 },
abstract = { 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. },
address = { Rio de Janeiro, Brazil },
booktitle = { Proceedings of the WWW '13 Companion: 22nd International World Wide Web Conference },
priority = { 0 }
}

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last change: Wed, 15 Apr 2015 10:41:10 +0200