Abstract: This study explores how a reality TV show which aired in Nigeria in 2019 (Big Brother Naija - BBN), played a role in enhancing gender-biased conversations among its viewers and social media followers. Thematic analysis is employed here to study Twitter conversations among BBN 2019 followers, which ensued after the show had stopped airing. The study reveals that the show influenced the way viewers and fans engaged with each other, as well as with the show’s participants, on Twitter, and argues that, despite having aired for a short period of time, BBN 2019 was able to draw people together and provide a community where viewers could engage with each other online. Though the show aired on TV, the viewers found a digital space where they could air their views, react to what was happening on the show, as well as simply catch up on action that they probably missed. Within these digital communities, viewers expressed their attractions, disgust and identities, most of these having a form of reference to sexuality and gender identities and roles, as were also portrayed by the show’s producers both on TV and on social media.
Abstract: Objectives: The purpose of this study was to understand and examine the association between diurnal mood variation and sexual/health-related status among men who have sex with men (MSM) using data from MSM Chinese Twitter messages. The study consists of 843,745 postings of 377,610 MSM users located in Guangdong that were culled from the MSM Chinese Twitter App. Positive affect, negative affect, sexual related behaviors, and health-related status were measured using the Simplified Chinese Linguistic Inquiry and Word Count. Emotions, including joy, sadness, anger, fear, and disgust were measured using the Weibo Basic Mood Lexicon. A positive sentiment score and a positive emotions score were also calculated. Linear regression models based on a permutation test were used to assess associations between affective states and sexual/health-related status. In the results, 5,871 active MSM users and their 477,374 postings were finally selected. MSM expressed positive affect and joy at 8 a.m. and expressed negative affect and negative emotions between 2 a.m. and 4 a.m. In addition, 25.1% of negative postings were directly related to health and 13.4% reported seeking social support during that sensitive period. MSM who were senior, educated, overweight or obese, self-identified as performing a versatile sex role, and with less followers, more followers, and less chat groups mainly expressed more negative affect and negative emotions. MSM who talked more about sexual-related behaviors had a higher positive sentiment score (β=0.29, p < 0.001) and a higher positive emotions score (β = 0.16, p < 0.001). MSM who reported more on their health status had a lower positive sentiment score (β = -0.83, p < 0.001) and a lower positive emotions score (β = -0.37, p < 0.001). The study concluded that psychological intervention based on an app for MSM should be conducted, as it may improve mental health.
Abstract: Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.
Abstract: This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.
Abstract: Face and facial expressions play essential roles in
interpersonal communication. Most of the current works on the facial
expression recognition attempt to recognize a small set of the
prototypic expressions such as happy, surprise, anger, sad, disgust
and fear. However the most of the human emotions are
communicated by changes in one or two of discrete features. In this
paper, we develop a facial expressions synthesis system, based on the
facial characteristic points (FCP's) tracking in the frontal image
sequences. Selected FCP's are automatically tracked using a crosscorrelation
based optical flow. The proposed synthesis system uses a
simple deformable facial features model with a few set of control
points that can be tracked in original facial image sequences.
Abstract: Facial expression analysis plays a significant role for
human computer interaction. Automatic analysis of human facial
expression is still a challenging problem with many applications. In
this paper, we propose neuro-fuzzy based automatic facial expression
recognition system to recognize the human facial expressions like
happy, fear, sad, angry, disgust and surprise. Initially facial image is
segmented into three regions from which the uniform Local Binary
Pattern (LBP) texture features distributions are extracted and
represented as a histogram descriptor. The facial expressions are
recognized using Multiple Adaptive Neuro Fuzzy Inference System
(MANFIS). The proposed system designed and tested with JAFFE
face database. The proposed model reports 94.29% of classification
accuracy.
Abstract: The study of the interaction between humans and
computers has been emerging during the last few years. This
interaction will be more powerful if computers are able to perceive
and respond to human nonverbal communication such as emotions. In
this study, we present the image-based approach to emotion
classification through lower facial expression. We employ a set of
feature points in the lower face image according to the particular face
model used and consider their motion across each emotive expression
of images. The vector of displacements of all feature points input to
the Adaptive Support Vector Machines (A-SVMs) classifier that
classify it into seven basic emotions scheme, namely neutral, angry,
disgust, fear, happy, sad and surprise. The system was tested on the
Japanese Female Facial Expression (JAFFE) dataset of frontal view
facial expressions [7]. Our experiments on emotion classification
through lower facial expressions demonstrate the robustness of
Adaptive SVM classifier and verify the high efficiency of our
approach.
Abstract: Recent theorizations on the cognitive process of moral
judgment have focused on the role of intuitions and emotions, marking
a departure from previous emphasis on conscious, step-by-step
reasoning. My study investigated how being in a disgusted mood state
affects moral judgment.
Participants were induced to enter a disgusted mood state through
listening to disgusting sounds and reading disgusting descriptions.
Results shows that they, when compared to control who have not been
induced to feel disgust, are more likely to endorse actions that are
emotionally aversive but maximizes utilitarian return
The result is analyzed using the 'emotion-as-information' approach
to decision making. The result is consistent with the view that
emotions play an important role in determining moral judgment.