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.
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013c. Predicting depression via social media. ICWSM 13 (2013), 1--10.Google Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- Saif M Mohammad. 2016. Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In Emotion measurement. Elsevier, 201--237.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
Index Terms
- Detecting Emotions using Smartphone Sensors: Technique to Raise Self-Awareness for Social Media Users
Recommendations
Detecting negative emotions during social media use on smartphones
AsianHCI '19: Proceedings of Asian CHI Symposium 2019: Emerging HCI Research CollectionEmotions are integral to the social media user experience; we express our feelings, react to posted content and communicate with emoji. This may lead to emotional contagion and undesirable behaviors such as cyberbullying and flaming. Nearly real-time ...
How Do You Feel Online: Exploiting Smartphone Sensors to Detect Transitory Emotions during Social Media Use
Emotions are an intrinsic part of the social media user experience that can evoke negative behaviors such as cyberbullying and trolling. Detecting the emotions of social media users may enable responding to and mitigating these problems. Prior work ...
Uses and gratifications of social networking sites for bridging and bonding social capital
Applying uses and gratifications theory (UGT) and social capital theory, our study examined users of four social networking sites (SNSs) (Facebook, Twitter, Instagram, and Snapchat), and their influence on online bridging and bonding social capital. ...
Comments