ABSTRACT
The popularity of online social platforms has also determined the emergence of violent and abusive behaviors reflecting real life issues into the digital arena. Cyberbullying, Internet banging, pedopornography, sexting are examples of these behaviors, as witnessed in the social media environments. Several studies have shown how to approximately detect those behaviors by analyzing the social interactions and in particular the content of the exchanged messages. The features considered in the models basically include detection of o ensive language through NLP techniques and vocabularies, social network structural measures and, if available, user context information. Our goal is to investigate those users who adopt offensive language and hate speech in Twitter by analyzing their profile pictures. Results show that violent people smile less and they are dominating by anger, fear and sadness.
- Mohammed Ali Al-garadi, Kasturi Dewi Varathan, and Sri Devi Ravana. 2016. Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network. Computers in Human Behavior 63(2016), 433--443. Google ScholarDigital Library
- Saeideh Bakhshi, David A Shamma, and Eric Gilbert. 2014. Faces engage us: Photos with faces attract more likes and comments on instagram. In ACM Human factors in computing systems. 965--974. Google ScholarDigital Library
- Alessandro Bessi, Mauro Coletto, George Alexandru Davidescu, Antonio Scala, Guido Caldarelli, and Walter Quattrociocchi. 2015. Science vs conspiracy: Collective narratives in the age of misinformation. PloS one 10, 2 (2015), e0118093.Google ScholarCross Ref
- Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, and Athena Vakali. 2017. Detecting Aggressors and Bullies on Twitter. In Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 767--768. Google ScholarDigital Library
- Mauro Coletto, Claudio Lucchese, Cristina Ioana Muntean, Franco Maria Nardini, Andrea Esuli, Chiara Renso, and Raffaele Perego. 2016. Sentiment-enhanced Multidimensional Analysis of Online Social Networks: Perception of the Mediterranean Refugees Crisis. In ASONAM 2016. Google ScholarDigital Library
- M Coletto, C Lucchese, S Orlando, and R Perego. 2015. Electoral Predictions with Twitter: a Machine-Learning approach. In IIR 2015, Cagliari, Italy.Google Scholar
- Maral Dadvar, Dolf Trieschnigg, Roeland Ordelman, and Franciska de Jong. 2013. Improving cyberbullying detection with user context. In European Conference on Information Retrieval. Springer, 693--696. Google ScholarDigital Library
- Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017. Automated Hate Speech Detection and the Problem of Offensive Language. ICWSM 2017 (2017).Google Scholar
- Julia Deeb-Swihart, Christopher Polack, Eric Gilbert, and Irfan A Essa. 2017. Selfie-Presentation in Everyday Life: A Large-Scale Characterization of Selfie Contexts on Instagram. In ICWSM. 42--51.Google Scholar
- Karthik Dinakar, Roi Reichart, and Henry Lieberman. 2011. Modeling the detection of Textual Cyberbullying. The Social Mobile Web 11, 02 (2011).Google Scholar
- Qianjia Huang, Vivek Kumar Singh, and Pradeep Kumar Atrey. 2014. Cyber Bullying Detection Using Social and Textual Analysis. In Intl. Workshop on SociallyAware Multimedia, ACM SAM 2014. 3--6. Google ScholarDigital Library
- Vasileios Lampos, Tijl De Bie, and Nello Cristianini. 2010. Flu detector-tracking epidemics on Twitter. In Machine Learning and Knowledge Discovery in Databases. Springer, 599--602. Google ScholarDigital Library
- Parma Nand, Rivindu Perera, and Abhijeet Kasture. {n. d.}. --How Bullying is this Message--: A Psychometric Thermometer for Bullying. ({n. d.}).Google Scholar
- Desmond Upton Patton, Robert D Eschmann, and Dirk A Butler. 2013. Internet banging: New trends in social media, gang violence, masculinity and hip hop. Computers in Human Behavior 29, 5 (2013), A54--A59.Google ScholarCross Ref
- Anna Schmidt and Michael Wiegand. 2017. A Survey on Hate Speech Detection using Natural Language Processing. SocialNLP 2017 (2017).Google Scholar
- Vivek K Singh, Qianjia Huang, and Pradeep K Atrey. {n. d.}. Cyberbullying detection using probabilistic socio-textual information fusion. In IEEE/ACM ASOMAN 2016. 884--887. Google ScholarDigital Library
- A Squicciarini, S Rajtmajer, Y Liu, and Christopher Griffin. 2015. Identification and characterization of cyberbullying dynamics in an online social network. In IEEE/ACM ASOMAN 2015. 280--285. Google ScholarDigital Library
- Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan, and Amit P Sheth. 2014. Cursing in english on twitter. In ACM conf. on Computer supported cooperative work & social computing. ACM, 415--425. Google ScholarDigital Library
- Quanzeng You, Darío García-García, Mahohar Paluri, Jiebo Luo, and Jungseock Joo. 2017. Cultural Diffusion and Trends in Facebook Photographs. ICWSM 2017 (2017).Google Scholar
- Changtao Zhong, Hau-wen Chan, Dmytro Karamshu, Dongwon Lee, and Nishanth Sastry. 2017. Wearing Many (Social) Hats: How Different are Your Different Social Network Personae ICWSM 2017 (2017).Google Scholar
- Erjin Zhou, Zhimin Cao, and Qi Yin. 2015. Naive-deep face recognition: Touching the limit of LFW benchmark or not arXiv preprint arXiv:1501.04690 (2015).015).Google Scholar
Index Terms
- Do Violent People Smile: Social Media Analysis of their Profile Pictures
Recommendations
Predicting postpartum changes in emotion and behavior via social media
CHI '13: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsWe consider social media as a promising tool for public health, focusing on the use of Twitter posts to build predictive models about the forthcoming influence of childbirth on the behavior and mood of new mothers. Using Twitter posts, we quantify ...
Romantic motivations for social media use, social comparison, and online aggression among adolescents
This study examines whether adolescent motivations for social media use, social comparison tendencies and gender are related to online aggression victimization and/or perpetration. Results from a national cross-sectional survey of adolescents (N=340) ...
The Classification of Aggressive Dialogue in Social Media Platforms
SIGMIS-CPR'18: Proceedings of the 2018 ACM SIGMIS Conference on Computers and People ResearchThe significance of aggression for the understanding of human behavior cannot be over amplified. It associates the individual, behavior, habits, environment, and health (mental). Understanding specificity when it comes to types of aggression and ...
Comments