Social Media Images Research Articles

Relationship of Social Media and Body Image Dissatisfaction among University Students

Social media has become an integral part of young adults’ lives today. It has moved well beyond simple entertainment and now can profoundly affect many areas of functioning. Social media messages promote the ideal, attractive body images that affect the university student’s attitudes toward the body. The current study examined the relationship between social media and body image dissatisfaction and examined how social media impacts body image among university students. Sample size estimation was calculated from G*power and 462 students minimum required in the current study. This cross-sectional study recruited (N=500) respondents from different universities such as the University of Sahiwal, Government college university of Faisalabad Sahiwal campus, Virtual University of Sahiwal, University of Okara, Virtual University of Okara, University of Management and Technology, and the University of Lahore by employing simple random sampling. There were two questionnaires applied the social media questionnaire and Socio-Cultural Attitudes towards Appearance Scale-3 (SATAQ-3) for data collection. Data were analyzed with SPSS version 25.0 and results showed that there was a significant negative relationship of social media with body image dissatisfaction. Moreover, male students were higher users of social media as compared to female students as well as higher body image dissatisfaction as compared to girls. Implications and limitations of the study are mentioned as well as future directions also suggested

Discovering Tampered Image in Social Media Using ELA and Deep Learning

Discovering Tampered Image in Social Media Using ELA and Deep Learning

Interactive 3D reconstruction method of fuzzy static images in social media

Abstract Because the traditional social media fuzzy static image interactive three-dimensional (3D) reconstruction method has the problem of poor reconstruction completeness and long reconstruction time, the social media fuzzy static image interactive 3D reconstruction method is proposed. For preprocessing the fuzzy static image of social media, the Harris corner detection method is used to extract the feature points of the preprocessed fuzzy static image of social media. According to the extraction results, the parameter estimation algorithm of contrast divergence is used to learn the restricted Boltzmann machine (RBM) network model, and the RBM network model is divided into input, output, and hidden layers. By combining the RBM-based joint dictionary learning method and a sparse representation model, an interactive 3D reconstruction of fuzzy static images in social media is achieved. Experimental results based on the CAD software show that the proposed method has a reconstruction completeness of above 95% and the reconstruction time is less than 15 s, improving the completeness and efficiency of the reconstruction, effectively reconstructing the fuzzy static images in social media, and increasing the sense of reality of social media images.

Rhythm Beyond the Cinematic Medium/The Pixel Beyond the Movie Theatre

In The Affect Theory Reader, Gregory J. Seigworth and Melissa Gregg write about Roland Barthes’s splendid notion of ‘shimmer’: an ‘exhaustively nuanced space’ that may be inventoried as patho-logies (by which to contemplate pathos) of bodies (human and nonhuman). In Alex Garland’s 2018 film Annihilation, a refracting effect — the Shimmer — which has appeared around a lighthouse and is slowly spreading outwards, is being studied. Military groups have entered the Shimmer never to return. A group of female scientists — of which Lena (Natalie Portman), a biologist, is one — enter the Shimmer and begin to inventory the strange organic duplicates of form within it. These organic structures, while extraordinarily nuanced, are also patho-logies of organic life as they are refracted by the Shimmer. This article will consider the ‘exhaustively nuanced space’ of cinema and its patho-logies via the conditions of the rhythm of the pixel in cinema, and beyond, in social media. While cinema, as well as social media, can be conceived as an affective experience, this essay will consider how the rhythm of the pixel as an energetic relation allows for an ethics to arise in the relation between the media text and the spectator/operator. In an examination of the rhythm of the pixel beyond the cinematic medium, I consider the energetic ‘becoming’ of the spectator/operator and the digital image (text and image in social media) as they act in relation. In an examination of the rhythm of the pixel beyond the movie theatre, I consider the infinite intensities in the aisthetic encounter of body and text/image in social media and its correlation to the politics of a mass-art. My hope is that in the ‘exhaustively nuanced space’ of rhythm beyond the cinematic medium and the pixel beyond the movie theatre, what may be found is patho-logies conjured by affective intensities and their connectives whereby digital interactions may no longer be refracted by the passions of divisive debate, but by an ethics of care, compassion, and empathy.

Depicting the Work of Police Foundations on Social Media: A Visual Communication Analysis

Visual imagery is a fundamental element of our communication systems. Unlike spoken or written words that deliver meaning one word at a time, images convey multiple messages instantly with powerful impact. This study explores the content embedded in the social media images used by nonprofit police foundations in the United States through archeological visual analysis. Findings reveal that public service organizations navigate curation choices when selecting images for social media accounts, such as how best to depict the mission of the organization. The prioritization of image curation is imperative, especially with the growth of social media as a space for promoting encounters with, not just information distribution to, citizens. In the public service, the importance of image curation lies in the potential to reinforce the work of organizations, but the risk is miscommunication with consequences for public trust.

Deep objectness hashing using large weakly tagged photos

CNN-based hashing methods have greatly boosted the performance of image retrieval, under the strong supervision of large amounts of manually annotated labels. In recent years, a large number of social media images with user tags have been generated on the Internet. These images can be regarded as weakly labeled training data, which can provide rich samples for training hash network, and greatly reduce the cost of obtaining training data. However, there are noise and visual irrelevant tags in user tags, and different tags may describe different objects in the image. In the previous CNN-based hashing method, a training image usually corresponds to a manual label and generates a hash code. These methods are difficult to use the image described by user tags with noise. For solving the above problem, we propose a CNN-based objectness hash learning method using user tags as a guide for training. First of all, the user tags are roughly filtered to remove noise tags that are not related to the visual content of images. Secondly, we quantify user tags into a unified semantic space and extract the highest-frequency words of the semantic space from similar objectness areas as their labels. Then, these objectness areas with their labels are grouped into a series of triple units as training data. So that the generated hash code can inherit the semantic similarity of the objectness areas well, that is, the Hamming distance between hash codes generated by similar objectness areas is closer, the reverse is the farther. Experimental results on NUS-WIDE and Flickr datasets show that our method can effectively extract object-level semantic information from weak user tags, and improve the accuracy of image retrieval.

Visual Gender Stereotypes (Advertisement, Social Media)

Visual Gender Stereotypes (Advertisement, Social Media)

A Longitudinal and Comparative Content Analysis of Instagram Fitness Posts.

Body dissatisfaction is among the most common mental health challenges experienced by women and has been identified as a risk factor for disordered eating. Research has found that exposure to social media images depicting thin, muscular bodies, often dubbed ‘fitspiration’, may contribute to body dissatisfaction. Image-centred social media platforms, such as Instagram, have rising popularity among adolescents and young adults. However, little is known about the content of images produced by different fitness-related sources, such as those from fitness brands compared with individual users, and how fitness content on social media is evolving over time. This study sought to determine whether Instagram content varied between female fitness influencers and brands and how this content changed between 2019 and 2021. A longitudinal content analysis was conducted on a sample of 400 Instagram images using a coding scheme developed specifically for this project. The scheme coded images for fit ideal body depiction, fitness focus, objectification, and sexualisation. Chi-square tests indicated that female fitness influencer content was more sexualised and portrayed more of the fit ideal, while fitness brands produced more Instagram content with a fitness focus. There were no significant overall longitudinal changes for any of the four key variables. However, when looking at longitudinal changes by account type, fitness-focused influencer content increased while fitness-focused brand content decreased over time. These findings highlight discernible differences in content produced by different Instagram account types. It points future research towards the consideration of potential moderating factors, such as account type, when exploring the impact of social media images on body image and mental health.

Social comparisons: A potential mechanism linking problematic social media use with depression.

Background and aimsThe majority of Australians are regular users of social media, especially young adults. Of concern, is that a minority of people appear to use social media in an addictive or problematic way which is associated with negative psychological outcomes such as depression. Social comparisons, where users compare themselves to others on social media, have also been linked with depression. Therefore, the key aim of the study was to determine whether social comparisons mediate the relationship between Problematic Social Media Use (PSMU) and depression.MethodIn a two-part study 144 participants (65 females) answered a series of self-report questions assessing factors relating to PSMU and then came into the lab to view a series of social media images, (pre-tested to be upward or downward comparisons).ResultsFemales used social media more problematically, liked more upward than downward comparison images and compared themselves more negatively to others on social media than did males. Higher PSMU scores were associated with depression and low self-esteem and comparing oneself more negatively to others on social media. Finally, focusing on upward comparisons and a tendency to make negative comparisons to others on social media partially mediated the association between PSMU and depression.Discussion and conclusionsSocial comparisons may function as a mechanism linking PSMU with negative psychological outcomes. Clinical interventions for individuals with PSMU which reduce the focus on upward social comparisons may also reduce negative psychological outcomes such as depression.

The association between social media use and mental health among adolescents and young adults

IntroductionThe associations of problematic social media use, the special use of image-based social media (photo editing, following celebrities) and mental health (body dissatisfaction, self-esteem, depression) have been established (e.g. Yurdagül et al., 2019; Gioia, Griffiths, Boursier, 2020; Lowe-Calverley and Grieve, 2021). The links may be explained with the theory of social comparison and self-objectification.ObjectivesTesting theory-oriented hypotheses related to image-based social media use and body dissatisfaction, gender specifically, among adolescents and young adults.MethodsThree surveys have been conducted with convenience sampling: (1) 117 Hungarian university students in person (mean age=22.4, SD=2.9, 79% female), (2) 383 high school students in person (mean age=16.5, SD=1.2, 58% female); (3) 124 Israeli adolescents online (mean age=16.8, SD=2.7, 68% female).Results(1): The tendency of modifying body image in social media (the frequency of modifying pictures, the use of filters) mediates the association between body shame and problematic social media use. Physical appearance social comparison mediates the association between self-related negative emotions and attitude (low self-esteem+ineffectiveness) and problematic social media use. (2): The technology-based social comparison mediate the association between muscle checking and problematic Instagram use among boys. (3) Physical appearance social comparison mediates the association between the frequency of following celebrities and body dissatisfaction among girls, but not among boys.ConclusionsDuring the use of image based social media, social comparison and the exposure to the beauty standards may lead to poorer mental health, which could result in problematic social media use as maladaptive coping.DisclosureNo significant relationships.

SMIFD-1000: Social media image forgery detection database

Image forgery/manipulation is one of the most alarming topics and becomes a major concern about different social media platforms regarding one’s privacy and safety. Therefore, the detection of the manipulated images is of immense interest to the researchers in the recent years. Despite the availability of numerous image forgery detection (IFD) datasets, very few particularly address the actual challenge by collecting the manipulated images from real-world scenario, e.g., collection of images from social media. Consequently, the contextual knowledge behind using the manipulated images remains unachieved. In order to address these issues, we propose an indigenous social media image forgery detection database, naming SMIFD-1000. This dataset provides rich annotations from several aspects: (a) image level: image regions that helps to classify pixel-level information; (b) forgery type: provide rich information about manipulation and (c) target and motif of manipulations: provide contextual rich knowledge about manipulation, which is significantly important from the perspective of social science. Finally, we would examine and benchmark the effectiveness of several publicly available algorithms on this dataset to demonstrate its usefulness. Results show that the dataset is highly challenging and will serve as an important benchmark for the existing and future IFD algorithms. Keywords: Image Manipulation, Digital Forensics, Image Dataset.

Revealing consumer insights through visual analysis of social media images

By analysing visual content on social media, marketing professionals can gain a deeper understanding of their audience than via traditional methods of market research, such as surveys. This paper outlines the unique advantages of visual analysis of online images and highlights eight case studies from the social media listening platform that demonstrate these benefits. It concludes with a forecast of possible future directions for visual analysis in marketing.

A new ontology-based multimodal classification system for social media images of personality traits

A new ontology-based multimodal classification system for social media images of personality traits

Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification on Social Networks

Advances in Artificial Intelligence (AI) have made it possible to automate human-level visual search and perception tasks on the massive sets of image data shared on social media on a daily basis. However, AI-based automated filters are highly susceptible to deliberate image attacks that can lead to content misclassification of cyberbulling, child sexual abuse material (CSAM), adult content, and deepfakes. One of the most effective methods to defend against such disturbances is adversarial training, but this comes at the cost of generalization for unseen attacks and transferability across models. In this article, we propose a robust defense against adversarial image attacks, which is model agnostic and generalizable to unseen adversaries. We begin with a baseline model, extracting the latent representations for each class and adaptively clustering the latent representations that share a semantic similarity. Next, we obtain the distributions for these clustered latent representations along with their originating images. We then learn semantic reconstruction dictionaries (SRD). We adversarially train a new model constraining the latent space representation to minimize the distance between the adversarial latent representation and the true cluster distribution. To purify the image, we decompose the input into low and high-frequency components. The high-frequency component is reconstructed based on the best SRD from the clean dataset. In order to evaluate the best SRD, we rely on the distance between the robust latent representations and semantic cluster distributions. The output is a purified image with no perturbations. Evaluations using comprehensive datasets including image benchmarks and social media images demonstrate that our proposed purification approach guards and enhances the accuracy of AI-based image filters for unlawful and harmful perturbed images considerably.

#Body goals or #Bopo? Exposure to pregnancy and post-partum related social media images: Effects on the body image and mood of women in the peri-pregnancy period

Women who are pregnant or have recently given birth use social media where they may encounter content promoting appearance ideals or offering more realistic representations of the body during this unique period. To date, little is known regarding the ways in which different social media content may impact the body image and mood of women who are pregnant or postpartum. A sample of 261 participants aged 21–44, mean (SD) age = 31.79 (4.402), were randomly allocated online to view either thin-and-toned ideal or body positive social media content specific to the pregnancy and postpartum period, or a neutral travel condition. Findings revealed that exposure to the body positive images had positive effects on body image relative to the thin-and-toned ideal condition, as well as leading to increases in some indices of positive body image, while the thin-ideal images were harmful. Partial support emerged for the protective roles of body appreciation and low social media-related rumination among women exposed to the thin-and-toned ideal images. Social media content may be harmful or helpful to the body image and mood of pregnant and postpartum women, which is important given the documented relationships between maternal body image, mood, and maternal and infant outcomes.

Effect of Mass Media on Body Image and Self-Esteem of Teenage Girls

This paper investigates whether mass media (social media) affects the body image and self-esteem of teenage girls. For most people, the beauty or covetable feminine body image of teenage girls is defined by a charming face and thin body shape commonly known as zero figures. But this definition of beauty cannot be true always because it is based on the experience of seeing which is not all-encompassing. The whole beauty of the girls can be defined by the combination of all the experiences, as experiences of seeing, listening, touching and all other which we are perceived by an individual. Now a day’s social media is the major source to share information among individuals. This paper provides the effects of social media on the body image and self-esteem of teenage girls. This study targets teenage girls because physical attractiveness is especially important to them and a thin and beautiful idealized image in social media plays a significant role in influencing their self-confidence. Further a data driven model using Artificial intelligence (AI) suggested for future challenges.

Visual Sentiment Analysis from Disaster Images in Social Media

The increasing popularity of social networks and users’ tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societally important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster-related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation and analyzing people’s sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming at a separate task. The presented analysis and the associated dataset, which is made public, will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.

The Joy of Following: Network Fascism and the Micropolitics of the Social Media Image

This article deploys Spinoza’s ethic of joy alongside Deleuze and Guattari’s exposition of micropolitics to expose how fascist desires and affects bloom and circulate through digital communications ecosystems that generally promote a diffusion or decentralisation of power. Beyond the steady barrage of alt-right content conscientiously documented by liberal journalists and progressive watchdogs, a more persistent and widespread fascist impulse permeates the very forms of some of our most banal digitally mediated acts and encounters. Rather than a sole looming authoritarian figurehead, the network itself – particularly with the new image paradigms propelled by apps such as Instagram and TikTok – has become the rallying point for the circulation of fascistic affects, burnished through the art and ethos of following: of rules, routines, protocols, accounts. I contend that a joyful passion accompanies much of this everyday experience of keeping up with one’s feeds, engaging the platforms, participating in the spectacles. This is what Sontag, interrogating the appeal of the Third Reich, calls the ‘joy of followers’, a joy in fascist belonging, which is to suggest that fascist movements thrive not only on the circulation of negative affects like hatred and fear, but also on the profusion of pleasure and affiliation. Deleuze’s Spinoza, resolutely anti-fascist, helps us parse this situation as it plays out in the social media sphere. Spinoza offers a bipartite conceptualisation of joy that allows us to diagnose the pleasures particular to fascist belonging and network belonging alike as passive, partial and indirect. Ultimately, what we today share with historical fascisms is a ubiquitous aesthetic that merges art with life and bodies with information, and a corresponding ethos that cultivates conformism, barbarises critical thought and redirects joyful impulses into reactionary social forms. As fascistic power relations spread anew through digital cultures’ newly evolving modes of visuality, hapticity, vibration and expression, one can observe something of what Deleuze calls ‘sad joy’, a sort of joy rooted in conquest and domination. The aim of this article is to root out such sad joys, to appreciate their appeal, and ultimately to reject them in all their various forms.

Social Media Language and Images Predict Psychiatric Diagnoses and Impending Hospitalization

Social Media Language and Images Predict Psychiatric Diagnoses and Impending Hospitalization

Urban Recreational Ecosystem Services Investigation based on Social Media Images

Urban Recreational Ecosystem Services Investigation based on Social Media Images