{"title":"暴力的人会微笑吗:社交媒体对他们头像的分析","authors":"Mauro Coletto, C. Lucchese, S. Orlando","doi":"10.1145/3184558.3191594","DOIUrl":null,"url":null,"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.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"144 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Do Violent People Smile: Social Media Analysis of their Profile Pictures\",\"authors\":\"Mauro Coletto, C. Lucchese, S. Orlando\",\"doi\":\"10.1145/3184558.3191594\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":235572,\"journal\":{\"name\":\"Companion Proceedings of the The Web Conference 2018\",\"volume\":\"144 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Companion Proceedings of the The Web Conference 2018\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3184558.3191594\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion Proceedings of the The Web Conference 2018","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3184558.3191594","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Do Violent People Smile: Social Media Analysis of their Profile Pictures
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.