NEWS

Data for Refugees
Türk Telekom, TÜBİTAK and Boğaziçi University initiated the "D4R – Data for Read more...
Senior Projects Poster Session
Our senior students have completed their CMPE 491 Graduation project Read more...
EU Funding for Full-time Msc/Phd Positions in Cognitive Robotics and Robot Learning
Project name: IMAGINE: Robots Understanding Their Actions by Imagining Their Read more...
Special 6-week training course organized with Havelsan: "Introduction to Machine Learning and Data Analysis"

CmpE Events

Today

  1. TETAM PhD Seminars by Alper Alimoğlu and Cem Rıfkı Aydın
    • Start time: 10:00am, Thursday, April 26th
    • End time: 11:00am, Thursday, April 26th
    • Where: TETAM Roof Conference Hall
    • http://tetam.boun.edu.tr/sites/default/files/TetamPhDSeminarsVol6.jpg

    • View this event in Google Calendar

Wednesday, May 2nd

  1. CmpE Seminar: Predicting Unspoken Views by Kareem Darwish, PhD
    • Start time: 02:00pm, Wednesday, May 2nd
    • End time: 03:00pm, Wednesday, May 2nd
    • Where: AVS Conference Room, BM
    • Abstract: This work examines the effectof online social network interactions on future attitudes. Specifically, wefocus on how a person's online content and network dynamics can be used topredict future attitudes and stances in the aftermath of a major event. In thisstudy, we focus on the attitudes of US Twitter users towards Islam and Muslimssubsequent to the tragic Paris terrorist attacks that occurred on November 13,2015. We quantitatively analyze 44K users' network interactions and historicaltweets to predict their attitudes.  We provide a description of thequantitative results based on the content (hashtags) and network interaction(retweets, replies, and mentions). We analyze two types of data: (1) we usepost-event tweets to learn users' stated stances towards Muslims based onsampling methods and crowd-sourced annotations; and (2) we employ pre-eventinteractions on Twitter to build a classifier to predict post-event stances. Wefound that pre-event network interactions can predict someone’s attitudestowards Muslims with 82% macro F-measure, even in the absence of prior mentionsof Islam, Muslims, or related terms.  We also interrogated our classifierto ascertain the most discriminating features.  Doing so allowed us: todiscover underlying groups that may express similar attitudes on a topic, butmay differ ideologically; and to associate political views with lifestylechoices.                                                  Short Bio: Kareem Darwish is a senior scientist at the Arabic LanguageTechnologies at QCRI with interest in information retrieval, natural languageprocessing, and social computing. He was as a researcher at the Cairo MicrosoftInnovation Lab and the IBM Human Language Technologies group in Cairo. He alsotaught in the Electrical Engineering Department at the German University inCairo and the Faculty of Computer and Informatics at Cairo University.

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Contact us

Department of Computer Engineering, Boğaziçi University,
34342 Bebek, Istanbul, Turkey

  • Phone: +90 212 359 45 23/24
  • Fax: +90 212 2872461
 

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