Unrealistic Images Research Articles

Social Media and Nutritional Guidelines in Pregnancy.

Nutrition impacts health outcomes of pregnant people and their fetuses. Discussing nutrition with patients may be challenging for health care providers. In this vacuum, patients use social media for health information during pregnancy. Little is known about the type and quality of nutrition information that is available on social media. The aim of this study was to qualitatively explore the pregnancy nutrition messages contained in social media videos for content and evaluate the information's concordance with evidence-based guidelines. A review of current social media video content for nutrition in pregnancy was conducted across 4 platforms: YouTube, Instagram, Facebook, and TikTok. Content analysis was used to descriptively and categorically analyze the social media video content. In the 62 social media videos reviewed, most were not consistent with established guidelines, were inaccurate, or presented a homogenous and unrealistic image of the pregnant person. Four categories of nutrition in pregnancy social media information emerged: what to eat, what not to eat, what I ate: aspirational, and what I ate: rebellion. Few videos were produced by health care organizations. Some were produced by anonymous organizations, with the majority made by pregnant or postpartum people. Many were testimonial or confessional in style. Despite regulations requiring identification of paid posts and advertisements, it was difficult to discern if videos were authentic sharing of patient experiences or advertisements. There is incorrect and incomplete information on social media surrounding nutrition in pregnancy. Professional guidelines for nutrition in pregnancy are available but are not as accessible to patients as social media videos. Social media use is associated with disordered eating and increased risk of anxiety and depression. However, targeted interventions by health care providers that use social media platforms have been successful in the distribution of credible health information. Midwives and other health care providers should share evidence-based nutrition recommendation with patients to facilitate meaningful conversations around nutrition in pregnancy during in person and online interactions.

Against revolutions

Abstract The history of science in public discussion is dominated by large-scale narratives of revolution. These locate epistemic violence within specialist communities, obscuring the role of science in environmental destruction and in silencing other ways of engaging with the world. At the same time, the language of revolution has fostered an unrealistic image of science, giving too much prominence to crisis, heroic challenges to authority and the wholescale abandonment of established theory. Revolutionary narratives in history of science were consolidated in the decades around 1900, as the genealogy for an emerging union of science, industry and imperial power. Even when explicitly rejected, they function as ‘ghost narratives’ within teaching and research. Relocating epistemic violence not only involves changing the geography and chronology of established narratives, a project that is well under way. It also requires understanding that revolution is the wrong category of event for communicating science and its history.

Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks

Continuous Conditional Generative Adversarial Networks (CcGANs) enable generative modeling conditional on continuous scalar variables (termed regression labels). However, they can produce subpar fake images due to limited training data. Although Negative Data Augmentation (NDA) effectively enhances unconditional and class-conditional GANs by introducing anomalies into real training images, guiding the GANs away from low-quality outputs, its impact on CcGANs is limited, as it fails to replicate negative samples that may occur during the CcGAN sampling. We present a novel NDA approach called Dual-NDA specifically tailored for CcGANs to address this problem. Dual-NDA employs two types of negative samples: visually unrealistic images generated from a pre-trained CcGAN and label-inconsistent images created by manipulating real images' labels. Leveraging these negative samples, we introduce a novel discriminator objective alongside a modified CcGAN training algorithm. Empirical analysis on UTKFace and Steering Angle reveals that Dual-NDA consistently enhances the visual fidelity and label consistency of fake images generated by CcGANs, exhibiting a substantial performance gain over the vanilla NDA. Moreover, by applying Dual-NDA, CcGANs demonstrate a remarkable advancement beyond the capabilities of state-of-the-art conditional GANs and diffusion models, establishing a new pinnacle of performance. Our codes can be found at https://github.com/UBCDingXin/Dual-NDA.

Strike a pose: Immediate effects of a model-created social media literacy video on body image and mood

Exposure to idealized social media imagery has been shown to be detrimental to body image among young women, and social media literacy may be protective. This mixed methods study aimed to evaluate the effects of an online video narrated by a high-profile, plus-size model on the body image and negative mood of young women and predictors of the effects. A sample of 304 young women, average age 20.95 (SD = 2.09) years, completed an online survey including pre- and post-exposure measures of state body image and negative mood, and trait measures of upward social media appearance comparison, thin-ideal internalization, and selfie-investment. A subset (n = 148) responded to open-ended questions to obtain feedback on the video coded categorically and through thematic analysis. Findings revealed immediate improvements in state body satisfaction and negative mood, with higher levels of trait appearance comparison and selfie investment associated with smaller effects of the video. Participants endorsed the video as raising awareness of unrealistic images but limited in terms of mitigating appearance comparison and selfie-investment. Furthermore, participants described that Iskra's status as a professional model also influenced their reactions. These findings highlight the importance of assessing for unintended paradoxical effects of social media literacy efforts and ensuring that such interventions are empirically based.

CalAttnMix: An instance-attention based data augmentation method for calcification detection in mammograms

Data augmentation is widely used as a pre-processing step to solve limited training data and class imbalance problems in deep-learning based calcification detection methods. However, current augmentation methods in breast mammograms fail to guarantee the class balance and diversity of generated calcifications, especially that randomly fusing instances with specified regions results in unrealistic images, further degrading the performances. Here, we propose an instance-attention based data augmentation method, named Calcification-Attention-Mix (CalAttnMix), which adaptively transfers multiple source calcification instances into diverse breast tissue backgrounds, aiding breast calcification detection. First, to seek feasible breast tissue candidate backgrounds, an Augmentation Region Proposal Mechanism (ARPM) is devised based on morphology constraints of calcifications. Then, without segmentation masks of calcification foregrounds, we design a transformer-based Foreground Prediction Module (FPM), along with a multi-task learning strategy, to localize instances accurately in a weakly-supervised manner. Finally, to generate diverse instances, an attention mix module is introduced to dynamically mix instances with background candidates, simulating various breast tissue surroundings. By focusing on the minor class, CalAttnMix generates diverse and balanced datasets for calcification detection. Experiments on the public and clinical dataset show that CalAttnMix achieves the lowest FID 22.152 and cosine similarity score 0.304, respectively, confirming the realism and diversity of generated images. CalAttnMix outperforms SOTA augmentation methods by 3.40% / 2.30% and 1.40% / 5.00% in the average recall/mAP, demonstrating the best detection results.

Monumentalism as an Essential Feature of V. Hegamyan's Work

The purpose of the work is to investigate monumentalism in V. Hegamyan’s artistic works as a characteristic feature of the artist's creative manner. The research methodology combines a set of such methods as analytical, semiotic, art history, biographical, which in the end made it possible to achieve objective research results. The scientific novelty of the work consists in the systematisation and generalisation of the artist V. Hegamyan’s work, in particular, the disclosure of the essential features of monumentalism as a defining feature of the majority of his works. Conclusions. The name of the Odesa artist V. Hegamyan, who was the founder of the graphic faculty of the Odesa pedagogical institute, is little known to the general public of researchers of Ukrainian fine art. It has been found that the peak of the artist's creative search and revelation falls in the 1960s and 1970s, when V. Hegamyan painted numerous works, combining elements of monumental style and traditions with the colour energy of modern painting, moving away from socialist realist (academic) painting and creating unrealistic images. The monumentality of his works was manifested in the fact that he experimented a lot and fruitfully with the form, which was always dynamic. Accordingly, with all the conventionality of the figures, V. Hegamyan achieved real similarity, verisimilitude and expressiveness, creating a semantic multi-layeredness and endowing them with symbolism and ambiguity. He constantly worked on improving the technique and created his own artistic style in art, which consisted of a combination of graphic, pictorial and monumental techniques. The deepest level is archetypal symbolism, which transcends the boundaries of cultural regions and eras. In this aspect, V. Hegamyan consistently avoids straight-line allegoricality: symbolic subtexts naturally emerge from the subjects of the paintings and the means of expression chosen by the artist. This is what makes his works multifaceted: additional meanings are superimposed on specific images. The peak of the artist's creative activity during the Soviet period coincides with the emergence and development of such an artistic phenomenon as nonconformism, to which V. Hegamyan, although he did not belong, became a symbolic figure for many artists of that time. Keywords: Velery Hegamyan, monumentalism, works of art, Odesa period of creativity, nonconformism, drawing, symbols, symbolism, artistic composition.

ORRN: An ODE-Based Recursive Registration Network for Deformable Respiratory Motion Estimation With Lung 4DCT Images.

Deformable Image Registration (DIR) plays a significant role in quantifying deformation in medical data. Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images. However, in 4D (3D + time) medical data, organ motion, such as respiratory motion and heart beating, can not be effectively modeled by pair-wise methods as they were optimized for image pairs but did not consider the organ motion patterns necessary when considering 4D data. This article presents ORRN, an Ordinary Differential Equations (ODE)-based recursive image registration network. Our network learns to estimate time-varying voxel velocities for an ODE that models deformation in 4D image data. It adopts a recursive registration strategy to progressively estimate a deformation field through ODE integration of voxel velocities. We evaluate the proposed method on two publicly available lung 4DCT datasets, DIRLab and CREATIS, for two tasks: 1) registering all images to the extreme inhale image for 3D+t deformation tracking and 2) registering extreme exhale to inhale phase images. Our method outperforms other learning-based methods in both tasks, producing the smallest Target Registration Error of 1.24 mm and 1.26 mm, respectively. Additionally, it produces less than 0.001% unrealistic image folding, and the computation speed is less than 1 s for each CT volume. ORRN demonstrates promising registration accuracy, deformation plausibility, and computation efficiency on group-wise and pair-wise registration tasks. It has significant implications in enabling fast and accurate respiratory motion estimation for treatment planning in radiation therapy or robot motion planning in thoracic needle insertion.

Read the female values from the movie Barbie

Barbie has been a source of controversy and fascination.The article "Barbie Has Never Been a Great Symbol, but She’s an Excellent Mirror" suggests that Barbie embodies both an unrealistic image of femininity and the aspirations of women.Her beautiful image has exacerbated women's anxiety about their physique and appearance, and movies and brands related to Barbie may have invisibly spread the stereotype of women in their era; But at the same time, Barbie is a product of innovative significance, emphasizing the independence of women as subjects. Barbie has attracted attention to women's issues, and Barbie has also become an important cultural idol. Overall, Barbie represents a continuous struggle for women to gain freedom from stereotypes and expectationsy.

A Denoising Method for Multi-Noise on Steel Surface Detection

In order to solve the problem that steel surface defects are easily covered or submerged by other objects or noise, this paper proposes an open–closed transformation algorithm which can eliminate or weaken multiple noises. In the case of a small number of samples, this paper establishes a super-resolution generative adversarial neural network to achieve the enhancement of sample data. For avoiding unrealistic image defects caused by cuts or brightness variations, an enhancement method is given which incorporates the original defective high-frequency information into classical image fusion methods, such as rotation and error slicing. Experimental results show that the accuracy of the proposed denoising method reaches over 90%, which is more than 2.6% of that of the most primitive classification network. To compare with existing denoising methods, the denoising method proposed in this paper not only has higher accuracy, faster denoising speed, and stronger anti-interference ability, but also has better adaptation to the environment. This research will provide a new solution method for the denoising of multi-noise phenomena in multiple different environments.

Less Is More: Unsupervised Mask-Guided Annotated CT Image Synthesis With Minimum Manual Segmentations.

As a pragmatic data augmentation tool, data synthesis has generally returned dividends in performance for deep learning based medical image analysis. However, generating corresponding segmentation masks for synthetic medical images is laborious and subjective. To obtain paired synthetic medical images and segmentations, conditional generative models that use segmentation masks as synthesis conditions were proposed. However, these segmentation mask-conditioned generative models still relied on large, varied, and labeled training datasets, and they could only provide limited constraints on human anatomical structures, leading to unrealistic image features. Moreover, the invariant pixel-level conditions could reduce the variety of synthetic lesions and thus reduce the efficacy of data augmentation. To address these issues, in this work, we propose a novel strategy for medical image synthesis, namely Unsupervised Mask (UM)-guided synthesis, to obtain both synthetic images and segmentations using limited manual segmentation labels. We first develop a superpixel based algorithm to generate unsupervised structural guidance and then design a conditional generative model to synthesize images and annotations simultaneously from those unsupervised masks in a semi-supervised multi-task setting. In addition, we devise a multi-scale multi-task Fréchet Inception Distance (MM-FID) and multi-scale multi-task standard deviation (MM-STD) to harness both fidelity and variety evaluations of synthetic CT images. With multiple analyses on different scales, we could produce stable image quality measurements with high reproducibility. Compared with the segmentation mask guided synthesis, our UM-guided synthesis provided high-quality synthetic images with significantly higher fidelity, variety, and utility ( by Wilcoxon Signed Ranked test).

Carnet de voyage, Bagdad 2019

This contribution is a commented portfolio of images gathered during fieldwork in Baghdad (February 2020). It aims to provide an overview of the murals which have been concentrated on Tahrir Square since the revolution of October 2019. These murals share some iconographic topics in common with the thawra (revolution) in Syria, Tunisia, Egypt and Beirut. Nevertheless, some semantic and iconographic registers are specific to Iraq, and divided into two main categories. The first one recreates “a city within the city” (ville en abyme) through composite unrealistic images of real places known to all; the second relates to the martyrology of Shiism, with portraits of historical imams, but also of “martyrs” killed during the repression in Autumn 2019. Contrary to the well-known official iconography since the 1958 Iraqi revolution, this new figurative expression is that of a young population predominantly Shiite (due to demography in Iraq), and it stems from a cultural and political substrate prone to rebellion—that of Hussein, long repressed by power. The style is often naive, at the antipodes of the concomitant Beiruti thawra street art, although crossing with other globalized graphic codes. It constitutes a vibrant popular culture and also signals a “Shiitization” of public space. Beyond traditional divisions, it finally proves to federate, in reaction to the iconoclasm of Daesh but also to the corruption that strikes the daily life and the future of millions of citizens.

Context-Aware, Reference-Free Local Motion Metric for CBCT Deformable Motion Compensation.

Deformable motion is one of the main challenges to image quality in interventional cone beam CT (CBCT). Autofocus methods have been successfully applied for deformable motion compensation in CBCT, using multi-region joint optimization approaches that leverage the moderately smooth spatial variation motion of the deformable motion field with a local neighborhood. However, conventional autofocus metrics enforce images featuring sharp image-appearance, but do not guarantee the preservation of anatomical structures. Our previous work (DL-VIF) showed that deep convolutional neural networks (CNNs) can reproduce metrics of structural similarity (visual information fidelity - VIF), removing the need for a matched motion-free reference, and providing quantification of motion degradation and structural integrity. Application of DL-VIF within local neighborhoods is challenged by the large variability of local image content across a CBCT volume, and requires global context information for successful evaluation of motion effects. In this work, we propose a novel deep autofocus metric, based on a context-aware, multi-resolution, deep CNN design. In addition to the inclusion of contextual information, the resulting metric generates a voxel-wise distribution of reference-free VIF values. The new metric, denoted CADL-VIF, was trained on simulated CBCT abdomen scans with deformable motion at random locations and with amplitude up to 30 mm. The CADL-VIF achieved good correlation with the ground truth VIF map across all test cases with R2 = 0.843 and slope = 0.941. When integrated into a multi-ROI deformable motion compensation method, CADL-VIF consistently reduced motion artifacts, yielding an average increase in SSIM of 0.129 in regions with severe motion and 0.113 in regions with mild motion. This work demonstrated the capability of CADL-VIF to recognize anatomical structures and penalize unrealistic images, which is a key step in developing reliable autofocus for complex deformable motion compensation in CBCT.

Taking and Sharing Photographs of Virtual Court Proceedings to Social Media: A Critical Appraisal on the Law of Contempt in Malaysia

In response to the spread of the Covid-19 pandemic, virtual Court hearings in Malaysia are now conducted in a way that enables proceedings to continue, ensuring its accessibility to the public. However, the attendees of virtual Court hearings remain bound to the same prohibitions as those enforced in conventional hearings. In Malaysia, the Court bans photography of any proceedings in the Courtroom, in which, disregarding the prohibition, can cite one for contempt of Court. The excessive use of digital photography may trigger photo manipulation by creating unrealistic images, which causes a public misconception. However, Zoom Court hearings have remained a permanent feature of the Malaysian justice system post-Covid-19. This research aims to examine whether photography in Court proceedings amounts to contempt by disrupting the ongoing process. The methodology used is qualitative approach based on library and internet research. This research found that photography of Court proceedings and sharing the photographs on social media platforms give rise to the dangers of witness intimidation and prejudice to the accused person’s right to a fair hearing. It suggests a need to envision a detailed guideline, clearer signage, and develop special measures for the stakeholders to use mobile phones with cameras in Court proceedings.

Reference-free learning-based similarity metric for motion compensation in cone-beam CT

Purpose. Patient motion artifacts present a prevalent challenge to image quality in interventional cone-beam CT (CBCT). We propose a novel reference-free similarity metric (DL-VIF) that leverages the capability of deep convolutional neural networks (CNN) to learn features associated with motion artifacts within realistic anatomical features. DL-VIF aims to address shortcomings of conventional metrics of motion-induced image quality degradation that favor characteristics associated with motion-free images, such as sharpness or piecewise constancy, but lack any awareness of the underlying anatomy, potentially promoting images depicting unrealistic image content. DL-VIF was integrated in an autofocus motion compensation framework to test its performance for motion estimation in interventional CBCT. Methods. DL-VIF is a reference-free surrogate for the previously reported visual image fidelity (VIF) metric, computed against a motion-free reference, generated using a CNN trained using simulated motion-corrupted and motion-free CBCT data. Relatively shallow (2-ResBlock) and deep (3-Resblock) CNN architectures were trained and tested to assess sensitivity to motion artifacts and generalizability to unseen anatomy and motion patterns. DL-VIF was integrated into an autofocus framework for rigid motion compensation in head/brain CBCT and assessed in simulation and cadaver studies in comparison to a conventional gradient entropy metric. Results. The 2-ResBlock architecture better reflected motion severity and extrapolated to unseen data, whereas 3-ResBlock was found more susceptible to overfitting, limiting its generalizability to unseen scenarios. DL-VIF outperformed gradient entropy in simulation studies yielding average multi-resolution structural similarity index (SSIM) improvement over uncompensated image of 0.068 and 0.034, respectively, referenced to motion-free images. DL-VIF was also more robust in motion compensation, evidenced by reduced variance in SSIM for various motion patterns (σ DL-VIF = 0.008 versus σ gradient entropy = 0.019). Similarly, in cadaver studies, DL-VIF demonstrated superior motion compensation compared to gradient entropy (an average SSIM improvement of 0.043 (5%) versus little improvement and even degradation in SSIM, respectively) and visually improved image quality even in severely motion-corrupted images.Conclusion: The studies demonstrated the feasibility of building reference-free similarity metrics for quantification of motion-induced image quality degradation and distortion of anatomical structures in CBCT. DL-VIF provides a reliable surrogate for motion severity, penalizes unrealistic distortions, and presents a valuable new objective function for autofocus motion compensation in CBCT.

Increase in body dysmorphia and eating disorders among adolescents due to social media

In this review, analysis of the studies pertaining to the role of social media in body dissatisfaction eventually leading to body dysmorphia and eating disorders was conducted.Objective:The aim was to review how social media creates the urge of attaining unrealistic body images which is impossible in real life but possible in real-life.Methods:Different search mediums including PubMed, Sciencedirect, and Google Scholar were used in order to find out the effects of social media in influencing body dissatisfaction. The inclusion criteria were based on the selection of research articles from 2014-2021 which correlated social media with eating disorder & body dysmorphia. Additionally, the articles that were not original researches, abstract only and not in English were excluded. After exclusion of irrelevant sources, duplicated article, abstract only& articles published before 2014, Total 25 articles were selected for the purpose of systematic review. Result:As per this systematic review, body dysmorphia & eating disorders were related to high levels of social media addiction. Moreover, how much social media has negatively impacted both the physical health of young adults especially in regards to disordered eating patterns as well as mental health. Social comparison in order to attain unrealistic images on social media resulting in body dysmorphia and body dissatisfaction.Conclusion:By reviewing different studies and finding out the effects of social media in body dysmorphia it is revealed that social media can highly influence various types of eating disorders like anorexia nervosa, bulimia nervosa and binge eating disorders.

Increase in body dysmorphia and eating disorders among adolescents due to social media

In this review, analysis of the studies pertaining to the role of social media in body dissatisfaction eventually leading to body dysmorphia and eating disorders was conducted.Objective:The aim was to review how social media creates the urge of attaining unrealistic body images which is impossible in real life but possible in real-life.Methods:Different search mediums including PubMed, Sciencedirect, and Google Scholar were used in order to find out the effects of social media in influencing body dissatisfaction. The inclusion criteria were based on the selection of research articles from 2014-2021 which correlated social media with eating disorder & body dysmorphia. Additionally, the articles that were not original researches, abstract only and not in English were excluded. After exclusion of irrelevant sources, duplicated article, abstract only& articles published before 2014, Total 25 articles were selected for the purpose of systematic review. Result:As per this systematic review, body dysmorphia & eating disorders were related to high levels of social media addiction. Moreover, how much social media has negatively impacted both the physical health of young adults especially in regards to disordered eating patterns as well as mental health. Social comparison in order to attain unrealistic images on social media resulting in body dysmorphia and body dissatisfaction.Conclusion:By reviewing different studies and finding out the effects of social media in body dysmorphia it is revealed that social media can highly influence various types of eating disorders like anorexia nervosa, bulimia nervosa and binge eating disorders.

DECONSTRUCTING MASCULINITY: CHANGING PORTRAYAL OF INDIAN MEN ON OTT PLATFORMS

Over the years, media has been invoking 'hegemonic masculinity’ and producing a one-tone representation of men, who are linear, flat, and in line with the traditional definition of manliness. Such unrealistic images of men lead to the reinforcement of prejudice against other genders, which also has damaging repercussions on men. However, there is a noticeable silver lining with the entry of Over the Top (OTT) platforms as they are challenging such representations. These platforms chart out the basic premise of the theory of social construction of reality that helps construct new realities of masculinity, defying 'hegemonic masculinity.' This paper aims to critically examine and evaluate masculinity's representation in various Indian OTT platforms' content and understand how the Indian OTT content is blurring masculinity images instead of what the cultural products are portraying. The paper also intends to analyse the male lead characters and their positioning, which does not compartmentalise men's attributes into the existing patriarchal male image, representing them as real and relatable. This paper substantiates the method of discourse analysis. Three content from Indian OTT platforms are selected with the purposive sampling method - 'Little Things' – Season 1 & 2 on Netflix, 'Made in Heaven' on Amazon Prime, and 'Yeh Meri Family' on TVF Play, to analyse along variables formulated to justify the hypothesis – 'Indian content on OTT platforms is blurring the one-tone representation of masculinity. The paper proposes a revised study of men and masculinity, challenging the patriarchal framework of gender and power for future development in the study. The study posits that OTT platforms have taken a step towards bringing equality among and within genders through their representation to make a difference and build acceptance in society for heterogeneity within masculinity.

NEGATIVE/DELETERIOUS EFFECTS OF FASHION ON HEALTH

People are surrounded with unrealistic images that are altered and manipulated to appear "perfect" and when they compare themselves with such images, they develop feelings of Self-doubt and believe they should change certain physical aspects to be considered attractive. Therefore, we aimed to evaluate the influence of unrealistic beauty standards set up by the Fashion industry and its effects on a person's physical and mental Wellbeing. We conducted a survey questionnaire targeted at approximately 224 young adults with a mean age of 20-30 years. As a result, 95.6% of our survey respondents agreed that the fashion industry has set up unrealistic beauty standards that deleteriously affect a person's general health and Wellbeing.

Cross Data Set Generalization of Ultrasound Image Augmentation using Representation Learning: A Case Study

Abstract Data augmentation is a common method to make deep learning assessible on limited data sets. However, classical image augmentation methods result in highly unrealistic images on ultrasound data. Another approach is to utilize learning-based augmentation methods, e.g. based on variational autoencoders or generative adversarial networks. However, a large amount of data is necessary to train these models, which is typically not available in scenarios where data augmentation is needed. One solution for this problem could be a transfer of augmentation models between different medical imaging data sets. In this work, we present a qualitative study of the cross data set generalization performance of different learning-based augmentation methods for ultrasound image data. We could show that knowledge transfer is possible in ultrasound image augmentation and that the augmentation partially results in semantically meaningful transfers of structures, e.g. vessels, across domains.

SELF-PRESENTATION ON SOCIAL NETWORK SITES, BIG FIVE AND DARK TRIAD PERSONALITY TRAITS

The spread of social networks as a new environment allows representing self-image and psychological characteristics more or less different from reality. The studies of social network sites (SNS) confirm that self-presentation on the SNS is characterized by using specific strategies to display and regulate information about oneself. Self-monitoring and perfectionistic self-presentation can be considered essential characteristics of self-presentation on a social network site profile. Various connections between personality traits and characteristics of self-presentation on the SNS are revealed. Many studies replicate the relations between extraversion, neuroticism, and narcissism with social network activity and self-image representation. In the current study, we examined the relationship between the characteristics of self-presentation on a social network (realistic demonstrative self and fake deceiving self) with the Big Five and the Dark Triad traits. The characteristics of self-presentation and personality traits were assessed on a sample of 478 undergraduate students. Generally, a realistic demonstrative self is associated with the desire to show the best side of self (self-monitoring), avoidance of self-discussion, while a fake deceiving self reveals connections focusing on others' reactions to self-presentation. Consciousness, neuroticism, openness, and narcissism are predictors for realistic, demonstrative self-presentation, and conscientiousness and Machiavellianism are predictors for false, deceptive self-presentation. Thus, realistic demonstrative self-presentation is associated with controlling the information posted in the network and the desire to arouse admiration, and false self-presentation manifesting as a purposeful strategy for creating an unrealistic image.