{"title":"“An image hurts more than 1000 words?”","authors":"Franziska Oehmer-Pedrazzi, Stefano Pedrazzi","doi":"10.1515/commun-2023-0117","DOIUrl":null,"url":null,"abstract":"Visual content captures attention, is easy to understand, and is more likely to be remembered. However, it is not limited to conveying informative content; it can also be used to propagate hate. While existing research has predominantly focused on textual hate speech, this study aims to address a research gap by analyzing the characteristics of visual hate, including its channels, intensity, sources, and targets, through a standardized manual content analysis. The hate images were collected through the citizen science approach of data donation. Findings highlight that transgender individuals and migrants are the primary targets of visual hate. It reveals a presence of hate images not only on communication platforms but also in various intermediaries and journalistic media. Half of these images use factual or humorous methods to discriminate against individuals or groups, while an equal number adopt a highly aggressive tone. The study suggests governance measures to combat this issue effectively.","PeriodicalId":501361,"journal":{"name":"Communications","volume":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/commun-2023-0117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Visual content captures attention, is easy to understand, and is more likely to be remembered. However, it is not limited to conveying informative content; it can also be used to propagate hate. While existing research has predominantly focused on textual hate speech, this study aims to address a research gap by analyzing the characteristics of visual hate, including its channels, intensity, sources, and targets, through a standardized manual content analysis. The hate images were collected through the citizen science approach of data donation. Findings highlight that transgender individuals and migrants are the primary targets of visual hate. It reveals a presence of hate images not only on communication platforms but also in various intermediaries and journalistic media. Half of these images use factual or humorous methods to discriminate against individuals or groups, while an equal number adopt a highly aggressive tone. The study suggests governance measures to combat this issue effectively.