skip to main content
10.1145/3267305.3277825acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
abstract

Detecting Emotions using Smartphone Sensors: Technique to Raise Self-Awareness for Social Media Users

Published:08 October 2018Publication History

ABSTRACT

Emotions are the driving force for posting content on a social media platform. Although modeling and predicting in emotion with social media have been proposed, they often require post-investigation such as sentiment analysis. This often limits users to be aware of negative emotional states while publishing content on social media. In this project, we will propose an emotion detection technique by extracting the physiological responses of social media users from smartphone commodity sensors. We aim to provide design opportunities for social media platforms to raise emotional self-awareness of the users while using social media on the smartphone.

References

  1. Ying Chen, Yilu Zhou, Sencun Zhu, and Heng Xu. 2012. Detecting offensive language in social media to protect adolescent online safety. In Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom). IEEE, 71--80. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Rowena Cormack. Mobile Marketing - Using Social Media On Mobile Phones. (????). Retrieved June 25, 2018 from http://www.socialsongbird.com/2013/09/mobile-marketing-using-social-media-on.html#.U2sUSlcUUZEGoogle ScholarGoogle Scholar
  3. Munmun De Choudhury, Scott Counts, and Michael Gamon. 2012. Not all moods are created equal! exploring human emotional states in social media. In Sixth international AAAI conference on weblogs and social media.Google ScholarGoogle Scholar
  4. Munmun De Choudhury, Scott Counts, and Eric Horvitz. 2013a. Predicting Postpartum Changes in Emotion and Behavior via Social Media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). ACM, New York, NY, USA, 3267--3276. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Munmun De Choudhury, Scott Counts, and Eric Horvitz. 2013b. Social Media As a Measurement Tool of Depression in Populations. In Proceedings of the 5th Annual ACM Web Science Conference (WebSci '13). ACM, New York, NY, USA, 47--56. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013c. Predicting depression via social media. ICWSM 13 (2013), 1--10.Google ScholarGoogle Scholar
  7. Jérémy Frey, May Grabli, Ronit Slyper, and Jessica R. Cauchard. 2018. Breeze: Sharing Biofeedback Through Wearable Technologies. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Article 645, 12 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Harri Jalonen. 2014. Negative emotions in social media as a managerial challenge. In European Conference on Management, Leadership & Governance. Academic Conferences International Limited, 128.Google ScholarGoogle Scholar
  9. Funda Kivran-Swaine, Sam Brody, Nicholas Diakopoulos, and Mor Naaman. 2012. Of Joy and Gender: Emotional Expression in Online Social Networks. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work Companion (CSCW '12). ACM, New York, NY, USA, 139--142. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Adam D.I. Kramer. 2012. The Spread of Emotion via Facebook. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). ACM, New York, NY, USA, 767--770. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Adam DI Kramer, Jamie E Guillory, and Jeffrey T Hancock. 2014. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences 111, 24 (2014), 8788--8790.Google ScholarGoogle ScholarCross RefCross Ref
  12. Saif M Mohammad. 2016. Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In Emotion measurement. Elsevier, 201--237.Google ScholarGoogle Scholar
  13. Aske Mottelson and Kasper Hornbæk. 2016. An Affect Detection Technique Using Mobile Commodity Sensors in the Wild. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '16). ACM, New York, NY, USA, 781--792. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Gregory Park, H Andrew Schwartz, Johannes C Eichstaedt, Margaret L Kern, Michal Kosinski, David J Stillwell, Lyle H Ungar, and Martin EP Seligman. 2015. Automatic personality assessment through social media language. Journal of personality and social psychology 108, 6 (2015), 934.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Detecting Emotions using Smartphone Sensors: Technique to Raise Self-Awareness for Social Media Users

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
      October 2018
      1881 pages
      ISBN:9781450359665
      DOI:10.1145/3267305

      Copyright © 2018 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 8 October 2018

      Check for updates

      Qualifiers

      • abstract
      • Research
      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate764of2,912submissions,26%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader