Determining modified versions of social media images

This study delves into the profound impact of social media on body image perceptions and overall dissatisfaction among young adults at Quaid-i-Azam University, Islamabad. In today's digital age, social media pervades the lives of young people, shaping their self-perceptions and influencing their social interactions. This research aims to understand how the constant exposure to curated and often idealized images on social media platforms contributes to the development of unrealistic body standards, fostering feelings of inadequacy and pressure to conform to societal beauty ideals. Employing a quantitative research approach, the study focused on a sample of 200 undergraduate and postgraduate students aged 18 to 30 years. Data collection utilized systematic sampling techniques and involved the administration of questionnaires via Google Forms. The study drew upon Social Comparison Theory to understand how individuals evaluate their own appearance by comparing themselves to the seemingly flawless images and physiques presented on social media platforms. Data analysis was conducted using IBM SPSS Statistics. The findings revealed a significant correlation between social media usage and body image dissatisfaction among young adults. Frequent comparisons with idealized images of influencers and celebrities on social media platforms were found to be a major contributor to negative body image perceptions, leading to a range of negative emotional and psychological outcomes, including anxiety, depression, and low self-esteem. These findings underscore the urgent need for interventions that address the detrimental effects of social media on young adults' mental health. 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How do the organization and presentation of large-scale social media images recondition the process by which visual knowledge, value, and meaning are made in contemporary conditions? Analyzing fundamental elements in the changing syntax of existing visual software ontology—the ways current social media platforms and aggregators organize and categorize social media images—this article relates how visual materials created within social media platforms manifest distinct modes of knowledge production and acquisition. First, I analyze the structure of social media images within data streams as opposed to previous information organization in a structured database. While the database has no pre-defined notions of time and thus challenges traditional linear forms, the data stream re-emphasizes the linearity of a particular data sequence and activates a set of new relations to contemporary temporalities. Next, I show how these visual arrangements and temporal principles are manifested and discussed in three artworks: “Untitled” (Perfect Lovers) by Felix Gonzalez-Torres (1991), The Clock by Christian Marclay (2011), and Last Clock by Jussi Ängeslevä and Ross Cooper (2002). By emphasizing the technical and poetic ways in which social media situate the present as a “thick” historical unit that embodies multiple and synchronous temporalities, this article illuminates some of the conditions, challenges, and tensions between former visual structures and current ones, and unfolds the cultural significations of contemporary big visual data.

IntroductionInstagram and TikTok, video-based social media platforms popular among adolescents, contain tobacco-related content despite the platforms’ policies prohibiting substance-related posts. Prior research identified themes in e-cigarette-related social media posts using qualitative or text-based machine learning methods. We developed an image-based computer vision model to identify e-cigarette products in social media images and videos.Aims and MethodsWe created a data set of 6999 Instagram images labeled for 8 object classes: mod or pod devices, e-juice containers, packaging boxes, nicotine warning labels, e-juice flavors, e-cigarette brand names, and smoke clouds. We trained a DyHead object detection model using a Swin-Large backbone, evaluated the model’s performance on 20 Instagram and TikTok videos, and applied the model to 14 072 e-cigarette-related promotional TikTok videos (2019–2022; 10 276 485 frames).ResultsThe model achieved the following mean average precision scores on the image test set: e-juice container: 0.89; pod device: 0.67; mod device: 0.54; packaging box: 0.84; nicotine warning label: 0.86; e-cigarette brand name: 0.71; e-juice flavor name: 0.89; and smoke cloud: 0.46. The prevalence of pod devices in promotional TikTok videos increased by 15% from 2019 to 2022. The prevalence of e-juices increased by 33% from 2021 to 2022. The prevalence of e-juice flavor names and e-cigarette brand names increased by about 100% from 2019 to 2022.ConclusionsDeep learning-based object detection technology enables automated analysis of visual posts on social media. Our computer vision model can detect the presence of e-cigarettes products in images and videos, providing valuable surveillance data for tobacco regulatory science (TRS).ImplicationsPrior research identified themes in e-cigarette-related social media posts using qualitative or text-based machine learning methods. We developed an image-based computer vision model to identify e-cigarette products in social media images and videos. We trained a DyHead object detection model using a Swin-Large backbone, evaluated the model’s performance on 20 Instagram and TikTok videos featuring at least two e-cigarette objects, and applied the model to 14 072 e-cigarette-related promotional TikTok videos (2019–2022; 10 276 485 frames). The deep learning model can be used for automated, scalable surveillance of image- and video-based e-cigarette-related promotional content on social media, providing valuable data for TRS. Social media platforms could use computer vision to identify tobacco-related imagery and remove it promptly, which could reduce adolescents’ exposure to tobacco content online.

A review of the studies on social media images from the perspective of information interaction

This paper presents a novel model for understanding social image content through text localization. For text localization, we explore maximally stable extremal regions (MSER) for detecting components that work by clustering pixels with similar properties. The output of component detection includes several non‐text components due to the degradations of social media images. To select the best components among many, we explore the genetic algorithm by convolving different kernels with components, which results in a feature matrix that is further fed to EfficientNet for choosing actual text components. Therefore, the proposed model is called genetic algorithm based network for text localization in degraded social media images (TLDSMI). For evaluating text localization, we consider the images of the standard dataset of natural scenes by uploading and downloading from different social media platforms, namely, WhatsApp, Telegram, and Instagram. The effectiveness of our method is shown by testing on original and degraded standard datasets. For example, for the degraded images of different complexities including degradations caused by social media platforms, the proposed method performs well in almost all situations. In addition, the proposed model achieves the best F1‐Score, 0.76, 0.77, 0.70, and 0.78 for the degraded images of CUTE, ICDAR 2013, Total‐Text, and CTW1500, respectively, compared to the state‐of‐the‐art methods.

The 21st century might be considered the "boom" period for social networking due to the fast expansion of social media use. In terms of user privacy and security regulations, a plethora of new requirements, issues, and concerns have arisen due to the proliferation of social media. With the increase in social media use, images on social media are often modified or fabricated for certain purposes. Therefore, this work implements and evaluates the SPIRAL-LSB algorithm for common attacks for social media images. Image compression was also discussed as images published to social media platforms was often compressed. An analysis was performed to assess the algorithm's output on social media images. The experiments were carried out prior to and after uploading to the Instagram platform. The dataset was subjected to image splicing, copy-move, cut-and-paste, text insertion, and 3D-sticker insertion attacks. The outcome of SPIRAL-LSB was effective for text insertion attacks solely. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) were selected as the experiment's metrics. The average PSNR value is 63.25, and the SSIM value is 0.99964, both of which are regarded high. This indicates that the watermark has not degraded the quality of the images. This work was designed for usage on social media for intellectual property reasons and may be used to validate the validity of social media images and prevent issues with image integrity, such as image manipulation.

The analysis for social networks, such as the socially connected Internet of Things, has shown a deep influence of intelligent information processing technology on industrial systems for Smart Cities. The goal of social media representation learning is to learn dense, low-dimensional, and continuous representations for multimodal data within social networks, facilitating many real-world applications. Since social media images are usually accompanied by rich metadata (e.g., textual descriptions, tags, groups, and submitted users), simply modeling the image is not effective to learn the comprehensive information from social media images. In this work, we treat the image and its textual description as multimodal content, and transform other metainformation into the links between contents (such as two images marked by the same tag or submitted by the same user). Based on the multimodal content and social links, we propose a Deep Attentive Multimodal Graph Embedding model named DAMGE for more effective social image representation learning. We introduce both small- and large-scale datasets to conduct extensive experiments, of which the results confirm the superiority of the proposal on the tasks of social image classification and link prediction.

City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd management purposes. Previous works proposed a large number of methods to count the crowd using different data in various contexts, but no methods proposed using social media images in city events and no datasets exist to evaluate the effectiveness of these methods. In this study we investigate how social media images can be used to estimate the crowd size in city events. We construct a social media dataset, compare the effectiveness of face recognition, object recognition, and cascaded methods for crowd size estimation, and investigate the impact of image characteristics on the performance of selected methods. Results show that object recognition based methods, reach the highest accuracy in estimating the crowd size using social media images in city events. We also found that face recognition and object recognition methods are more suitable to estimate the crowd size for social media images which are taken in parallel view, with selfies covering people in full face and in which the persons in the background have the same distance to the camera. However, cascaded methods are more suitable for images taken from top view with gatherings distributed in gradient. The created social media dataset is essential for selecting image characteristics and evaluating the accuracy of people counting methods in an urban event context.

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.

Social media is a routine part of every-day life for millions of people worldwide. How does engaging with social media shape enduring memories for that experience? This question is important given the popularity of certain types of content on social media platforms, such as content widely known as “fitspiration”. Two experiments involving 510 US adults (mean age = 36.82) examined memory for food and fitness-related social media images that individuals write comments about, as well as memory for other images in the context. We demonstrate that commenting on social media images boosts memory for them and weakly affects memory for conceptually related images in the same context. Exploratory analyses revealed correlations between self-reported disordered eating symptomology and effects of commenting on memory. These findings demonstrate that how people engage with social media has implications for the enduring memories of that content and may relate to behaviors and attitudes in offline lives, such as eating and body image.

Primary objective An emerging body of research examines the role of computer-mediated communication in supporting social connection in persons with traumatic brain injury (TBI). We examine the cognitive impacts of engaging with images posted to social media for persons with moderate-severe TBI. Research design Prior work shows that after viewing social media posts, adults have better memory for posts when they generate a comment about the post. We examined if persons with TBI experience a memory benefit for commented-upon social media images similar to non-injured comparison participants. Methods and procedures 53 persons with moderate-to-severe TBI and 52 non-injured comparison participants viewed arrays of real social media images and were prompted to comment on some of them. After a brief delay, a surprise two-alternative forced choice recognition memory test measured memory for these images. Main outcomes and results Persons with TBI remembered social media images at above-chance levels and experienced a commenting-related memory boost much like non-injured comparison participants. Conclusions These findings add to a growing literature on the potential benefits of social media use in individuals with TBI and point to the benefits of active engagement for memory in social media contexts in TBI.

Social Media and Body Image Concerns: Current Research and Future Directions

The use of manipulated images on social media by brands has garnered criticism from various interest groups worldwide. Some brands have started endorsing pro-social campaigns that challenge traditional stereotypes of manipulated images. This study investigates the impact of manipulated and unedited social media images of models on brand equity and the role of consumer-perceived ethicality in mediating these relationships. The research involved two experiments using an inter-study experimental design for two fashion brands, with two social media images (manipulated vs. unedited). The findings suggest that using manipulated images of models in social media posts significantly lowers brand equity and consumers’ perceived ethicality. Furthermore, the results indicate that consumers’ perceived ethicality fully mediates the influence. The study provides clear recommendations for brand owners on conveying meaningful messages and images of models on social media platforms.

Due to the adverse effect of quality caused by different social media and arbitrary languages in natural scenes, detecting text from social media images and transferring its style is challenging. This paper presents a novel end-to-end model for text detection and text style transfer in social media images. The key notion of the proposed work is to find dominant information, such as fine details in the degraded images (social media images), and then restore the structure of character information. Therefore, we first introduce a novel idea of extracting gradients from the frequency domain of the input image to reduce the adverse effect of different social media, which outputs text candidate points. The text candidates are further connected into components and used for text detection via a UNet++ like network with an EfficientNet backbone (EffiUNet++). Then, to deal with the style transfer issue, we devise a generative model, which comprises a target encoder and style parameter networks (TESP-Net) to generate the target characters by leveraging the recognition results from the first stage. Specifically, a series of residual mapping and a position attention module are devised to improve the shape and structure of generated characters. The whole model is trained end-to-end so as to optimize the performance. Experiments on our social media dataset, benchmark datasets of natural scene text detection and text style transfer show that the proposed model outperforms the existing text detection and style transfer methods in multilingual and cross-language scenario.

The study concluded that social media in particular has taken a large and important space in the most prominent fields of contemporary life, especially open-source social media, and as long as most aspects of human, natural and cosmic activity; It was worth studying all their aspects to clarify the position of Sharia and the law on them and their rulings on them. There is no doubt about the necessity and necessity of submitting them to their controls to ensure optimal benefit from them, as they are among the services that are indispensable in our contemporary time. Accordingly, this study dealt with the issue of social media through the following images:
 - Social media images in the call and Ifta
 - Social media images in business transactions
 Topics related to the media today are among the topics worthy of study and care, as the media occupies a large and influential space in various fields of life. Perhaps social media is one of the most prominent types of media in terms of influence and importance, as it is based on transmitting news and information from the community to the community itself, to achieve one of the purposes of news, guidance or guidance through its various means such as clubs, forums, places of worship, social networks, newspapers and books. The media directed to a particular place, group, or segment of society is considered its social media, such as the media directed to the community of a particular city or a segment such as women, or a group such as the disabled. Whereas social media is of such vitality and importance; That I studied this subject as required by the requirements of the study. Then this study was based on the extrapolation of the issues in which the opinions of scholars varied according to the multiplicity of factors related to the edge, according to an applied analytical methodology.