Being tough doesn’t always pay off: The culture of honor vs dignity in negotiation

Early work on cross-cultural negotiation has focused on East-West differences. In the current study we investigate the negotiation scripts employed by Middle Eastern negotiators, more specifically Iranian negotiators, in an intracultural interaction, compared to North American negotiators. We examine how the Iranian worldviews, beliefs, norms, and social behavior influence their goals and aspirations, negotiation tactics, and ultimately final outcome. We formulated our hypotheses based on the theory of honor-dignity cultures and illustrate how the importance of preserving and maintaining honor influences the Iranian negotiation strategies in business dealings. Our results illustrate that consistent with the culture of honor, Iranian negotiators are more likely to be competitive, express emotions, and employ distributive tactics compared to Canadian negotiators. Moreover, this competitive mindset leaves Iranian negotiators at a disadvantage as the overall joint gain is significantly lower than Canadian negotiators

Relationally Aggressive Media Exposure and Children’s Normative Beliefs: Does Parental Mediation Matter?

Research indicates that relationally aggressive media exposure is positively associated with relational aggression in children. Theories of media effects suggest that these associations may be mediated by aggressive cognitions. Although parental mediation can attenuate the effects of violent media, it is unknown whether there are similar benefits of parental mediation of relationally aggressive media. The current study examined concurrent and longitudinal associations between relationally aggressive television and movie exposure and normative beliefs about relational aggression, and whether parental mediation moderates these associations. Participants were 103 children (50% female) in grades 3-6 and their parents. The following year, 48 children (52% female) were again assessed. Relationally aggressive media exposure predicted concurrent relational aggression norms, even after controlling for physically aggressive media exposure and physical aggression norms. Relationally aggressive television and movie exposure predicted greater subsequent approval of relational aggression only among children whose parents engaged in low levels of active mediation

Is this Harmful? Learning to Predict Harmfulness Ratings from Video

Automatically identifying harmful content in video is an important task with a wide range of applications. However, due to the difficulty of collecting high-quality labels as well as demanding computational requirements, the task has not had a satisfying general approach. Typically, only small subsets of the problem are considered, such as identifying violent content. In cases where the general problem is tackled, rough approximations and simplifications are made to deal with the lack of labels and computational complexity. In this work, we identify and tackle the two main obstacles. First, we create a dataset of approximately 4000 video clips, annotated by professionals in the field. Secondly, we demonstrate that advances in video recognition enable training models on our dataset that consider the full context of the scene. We conduct an in-depth study on our modeling choices and find that we greatly benefit from combining the visual and audio modality and that pretraining on large-scale video recognition datasets and class balanced sampling further improves performance. We additionally perform a qualitative study that reveals the heavily multi-modal nature of our dataset. Our dataset will be made available upon publication.Comment: 11 pages, 15 figure

Deep Architectures for Content Moderation and Movie Content Rating

Rating a video based on its content is an important step for classifying video age categories. Movie content rating and TV show rating are the two most common rating systems established by professional committees. However, manually reviewing and evaluating scene/film content by a committee is a tedious work and it becomes increasingly difficult with the ever-growing amount of online video content. As such, a desirable solution is to use computer vision based video content analysis techniques to automate the evaluation process. In this paper, related works are summarized for action recognition, multi-modal learning, movie genre classification, and sensitive content detection in the context of content moderation and movie content rating. The project page is available at https://github.com/fcakyon/content-moderation-deep-learning

Combining text and images for film age appropriateness classification

© 2021 The Authors. Published by Elsevier. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1016/j.procs.2021.05.087We combine textual information from a corpus of film scripts and the images of important scenes from IMDB that correspond to these films to create a bimodal dataset (the dataset and scripts can be obtained from https://tinyurl.com/se9tlmr) for film age appropriateness classification with the objective of improving the prediction of age appropriateness for parents and children. We use state-of-the art Deep Learning image feature extraction, including DENSENet, ResNet, Inception, and NASNet. We have tested several Machine learning algorithms and have found xgboost to yield the best results. Previously reported classification accuracy, using only textual features, were 79.1% and 65.3% for American MPAA and British BBFC classification respectively. Using images alone, we achieve 64.8% and 56.7% classification accuracy. The most consistent combination of textual features and images’ features achieves 81.1% and 66.8%, both statistically significant improvements over the use of text only

Great Sexpectations: The Application of Sexual Social Exchange Theory to Date Rape

In a two-part study, dating sexual expectations will be evaluated and the sexual social exchange theory will be investigated in a date rape trial. In Part 1, participants (N = 100) will be presented with one of two fictional date scenarios that will differ only on the cost of the date (i.e., $30 or$175). Participants will then indicate what behaviors (sexual and not sexual) are appropriate at the end of a first date and then a fifth date. It is predicted that all participants will expect sexual intercourse more on the fifth date than the first, and that participants in the expensive date scenario will expect sexual intercourse more than participants in the inexpensive date condition. Part II will use the information gathered in Part I to investigate how sexual expectations in a dating scenario may manifest themselves as feelings of reciprocity in the sexual social exchange theory. In Part II participants (N = 160) will be presented with one of four trial summaries that differ depending on the cost of the date (i.e., $30 or$175) and the date number (i.e., first or fifth). Participants will render a verdict and then rate the defendant and alleged victim on various rating factors (e.g., credibility), in addition to completing the Illinois Rape Myth Acceptance scale, short form. It is predicted that there will be fewer guilty verdicts and lower pro-victim judgments for both men and women when the cost of the date was high and when the couple was on their fifth date. It is also predicted that men will render fewer guilty verdicts and report lower pro-victim attitudes than women. Juror rating subscales (e.g., victim credibility) and rape myth acceptance scores are predicted to mediate the effects of the cost of date and date number on verdict. The results will be discussed in terms of how the sexual social exchange theory can explain juror perceptions in a date rape trial