“Lucky Boy!”; Public perceptions of child sexual offending committed by women

This exploratory study addresses the existing gaps on the public perceptions of child sexual offending committed by women. Using thematic analysis, the study extracted, coded and analysed the comments (N=1651) made by the general public to nine Daily Mail online newspaper articles published from 2018 – 2019, reporting the sentencing decisions of female sex offenders, who have been charged and found guilty with the offence of sexual activity with a child. From those comments, 170 coded themes were identified, and this amounted to 3394 coded incidences. Unlike previous research, this study cross examines public responses to different typologies of offending behaviour; teachers, mothers, same sex offenders, co-offenders and finally those who offended for financial gain. The impact of these typologies was analysed through key descriptive case variables, which were quantitively evaluated against the prominent themes that emerged. It found that while people demand equal sentencing decisions between male and female child sex offenders, this is limited by public perception when the abuser is an attractive female and, as a result, perceived as less harmful to the child, who is not seen no longer as a victim but as a ‘Lucky Boy’. Such preconceptions fuel shame, social stigma and stereotyping towards sexual exposure and prevents victims to disclose their abuse and achieve closure and justice

What do you think of my picture? Investigating factors of influence in profile images context perception

International audienceMultimedia quality assessment has been an important research topic during the last decades. The original focus on artifact visibility has been extended during the years to aspects as image aesthetics, interestingness and memorability. More recently, Fedorovskaya proposed the concept of 'image psychology': this concept focuses on additional quality dimensions related to human content processing. While these additional dimensions are very valuable in understanding preferences, it is very hard to define, isolate and measure their effect on quality. In this paper we continue our research on face pictures investigating which image factors influence context perception. We collected perceived fit of a set of images to various content categories. These categories were selected based on current typologies in social networks. Logistic regression was adopted to model category fit based on images features. In this model we used both low level and high level features, the latter focusing on complex features related to image content. In order to extract these high level features, we relied on crowdsourcing, since computer vision algorithms are not yet sufficiently accurate for the features we needed. Our results underline the importance of some high level content features, e.g. the dress of the portrayed person and scene setting, in categorizing image