Shared Content Research Articles

DETECÇÃO DE NUDEZ EM IMAGENS POR SEGMENTAÇÃO E RECONHECIMENTO DE PADRÕES

With the emergence of the INTERNET and the growth of social networks, the sharing of content, such as images, audios and videos, and access to this content through websites and social networks, has become much greater. Shared content, consisting of images, audio and/or videos, may not be appropriate for all audiences or environments, for various reasons. One of them is in relation to nudity and pornography, which is very present on the INTERNET and social networks, and can cause negative impacts when accessed in business environments, as well as it can cause problems in the development and behavior of children and adolescents. In order to control access to these types of content, it is necessary to develop resources that perform filtering. Therefore, this work seeks to contribute to the development of a tool capable of detecting nudity in images by combining existing image processing techniques, such as the detection of skin color pixels, counting of related elements, zoning techniques and nudity classifiers using machine learning algorithms. Tests carried out on showed an accuracy of 90.5% in the best case.

A framework for fake news detection based on the wisdom of crowds and the ensemble learning model

Nowadays, the rapid development of social networks has led to the proliferation of social news. However, the spreading of fake news is a critical issue. Fake news is news written to intentionally misinform or deceive readers. News on social networks is short and lacks context. This makes it difficult for detecting fake news based on shared content. In this paper, we propose an ensemble classification model to detect fake news based on exploiting the wisdom of crowds. The social interactions and the user?s credibility are mined to automatically detect fake news on Twitter without considering news content. The proposed method extracts the features from a Twitter dataset and then a voting ensemble classifier comprising three classifiers namely, Support Vector Machine (SVM), Naive Bayes, and Softmax is used to classify news into two categories which are fake and real news. The experiments on real datasets achieved the highest F1 score of 78.8% which was better than the baseline by 6.8%. The proposed method significantly improved the accuracy of fake news detection in comparison to other methods.

Gale Business: Insights

Gale Business: Insights is a comprehensive tool for business researchers looking for company, industry, and country information, supplemented by news, trade journals, academic articles, and reports. Company profiles include chronologies, brands, SWOT (strengths, weaknesses, opportunities, and threats) reports, and financial performance data that can be analyzed using the database’s comparison and visualization tools.The interface promotes discoverability with multiple ways to access in-depth information about public and private companies, industries, and countries. Academic researchers will benefit from the formatted citations and the ability to share and download content as well as highlight text and take notes. The product offers accessibility options such as text to speech, display formatting, and adjustable text.

Identifying Factors and the Relationship between Problematic Social Media Use and Anxieties in Instagram Users: A Deep Investigation-based Dual-stage SEM-ANN Analysis

This paper aims to identify the factors and the relationship between Problematic Social Media Use (Compulsion and Withdrawal) and Anxieties in Instagram users. The research is descriptive, with a quantitative approach developed through a survey (n=757). After a multi-analytical approach using Covariance-based Structural Equation Modeling (CB-SEM) validated the model, results became inputs to an Artificial Neural Network (ANN) model to predict the Anxieties. Findings pointed out that the most relevant constructs were Shared Content Anxiety and Self Evaluation Anxiety, and the neural network provided empirical evidence to support that Compulsion is the most relevant predictor for Anxieties, increasing its effects. The introduction of this new methodology and the theoretical contribution of the proposed hybrid model opens perspectives of the existing body of knowledge in the literature related to understanding phenomena such as the relationship between Problematic Social Media Use and Anxieties in Instagram users.

PERCEPTION REGARDING SELF-REFLECTION ON FACEBOOK BY YOUNG FEMALES OF LAHORE

The study in hand is designed to investigate how much SELF (Personality) is reflected and how it is perceived through shared content on Facebook. To know about the reflection and perception through Facebook use, a survey method has been opted. A sample of 200 students among the population of University of Punjab Lahore was selected using a stratified and convenient sampling technique, study adopts the quantitative research approach. It was hypothesized that people tend to use Facebook to build their ideal image among the virtual community and to gratify their pre-existing cognition. Furthermore, users of Facebook perceive their virtual friends’ images according to their own perceptions. Looking glass theory of self paved the way for a better understanding of the topic. Results certify the hypothesis that most people use Facebook to build an ideal identity. Keywords: Shared content, Virtual Personality, Self, Ideal Identity

Business Growth Prediction Using Social Media Data Analytics

Marketing has become an important aspect of the growth of any business. Without good strategies for sharing content across social media platforms, no business can flourish and will lose its customers. Many social media platforms provide various analytics tools for free. These tools provide analytics of content engagement. Many proprietary tools provide such features with enhanced capabilities. These tools require shared content of users for analysis. These tools do not exactly state the content being shared. Businesses need marketing teams for creating attractive content. However, a lot of money is needed for hiring skilled personnel. This research proposed an efficient platform that provides analysis of content shared by successful businesses on social media platforms. Firstly, it’s free and public shared data, no surveys to be done because the analysis is done on businesses who are experts in marketing, and secondly, there is no headache for searching trends because the analysis is done on real-time data.

Meta-heuristic Searched-Ensemble Learning for fake news detection with optimal weighted feature selection approach

Nowadays, fake news has turned into a major problem because of the negative impact that it creates on society. Social media allows people to spread information on the internet with slight investigations and to add fewer filters than the actual content. Nowadays, the false news in the internet community is unsure, and it creates a wrong impression among the users. Detecting false news has become a critical task based on shared content. To tackle the false news growth in social media, various automatic detection schemes were evaluated. The “Natural Language Processing (NLP)” method also gives a prominent solution for false news detection. The main intention of this paper is to design and introduce an innovative false news recognition method using Meta-heuristic Searched-Ensemble Learning (MS-EL). Further, the selected features are extracted by the “Term Frequency-Inverse Document Frequency (TF-IDF)” and also Word2vec features. Here, the extracted selected features are integrated with the Hybrid Squirrel–Dragonfly Search Optimization (HS-DSO) is used to optimize the weighted feature selection approach with the fitness function of solving data variance and correlation. The proposed MS-EL is adopted in the classification part, having three sets of classifiers, Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Deep Neural Network (DNN). Here, the ensemble classifier is enhanced by the same HS-DSO that shows the parameter tuning with a high convergence rate. From the experimental outcomes, the accuracy of HS-DSO-MS-EL is 22% higher than BMO-MS-EL, 24% higher than SP-BMO-MS-EL, 30% higher than SSA-MS-EL, and 29% higher than DA-MS-EL. Thus, the experimental analysis with standard datasets establishes that the introduced fake news detection method has gained higher accuracy than the existing models.

The Viral Water Cooler: Talking About Political Satire Promotes Further Political Discussion

ABSTRACT Much effort has been devoted into understanding the participatory outcomes of political satire. Despite the increased impact of social media in disseminating political information online, however, researchers have not yet examined the potential role of social media in the relationship between political satire consumption and political communication processes. This study uses a three-wave panel survey to test the effects of both viewing political satire (intentionally) and incidental exposure (via shared content on social media) to political satire on political discussion, mediated by the viewers’ conversation about the content of political satire. This study also examines how Affinity for Political Humor (AFPH), specifically its social cohesion dimension, moderates those relationships. Results demonstrate that regardless of whether the exposure was incidental via social media or not, exposure to political satire increased political discussion, mediated by conversation about political satire. This indirect effect differed by individuals’ level of AFPH. These results indicate that viewing political satire, even when it is incidental, can make people more likely to talk about the content of the satire programs, which in turn can promote their political discussion in general. This effect was found to be more prominent among those who score high on AFPH.

The COVID-19 Normalization Process for Turkish Nations in terms of Health Communications: The Case of Türkiye, Azerbaijan, and the Turkish Republic of Northern Cyprus

This study aims to determine how the health ministries of Türkiye, Azerbaijan, and the Turkish Republic of Northern Cyprus (TRNC) formed health communication strategies through their official Twitter accounts during the normalization process COVID-19 pandemic. Within the scope of the research, based on the date when the normalization process of Türkiye, Azerbaijan, and the TRNC started, one-month Twitter posts were analyzed by the thematic analysis method. The study was diagnosed with the 2020.03 version of the Maxqda program. The findings determined that although the three countries struggled with the COVID-19 pandemic in line with different strategies, health ministries shared the public awareness and awareness in the normalization process of this global pandemic. The most shared content in all three countries is the COVID-19 Table, including the daily number of cases. When examining the impact and transformation of Twitter content based on the epidemic, the Republic of Türkiye has the most comprehensive range about the pandemic, followed by Azerbaijan and the Turkish Republic of Northern Cyprus. As a result, the pandemic affects the official Twitter account shares of the Ministry of Health in all three countries.

Mental Health Training, Attitudes toward Support, and Screening Positive for Mental Disorders among Canadian Coast Guard and Conservation and Protection Officers

Public Safety Personnel (PSP) including members of the Canadian Coast Guard (CCG) and Conservation and Protection (C&P) officers, are regularly exposed to potentially psychologically traumatic events (PPTEs) and other occupational stressors. Several mental health training programs (e.g., critical incident stress management [CISM], critical incident stress debriefing [CISD], peer support, mental health first aid, Road to Mental Readiness [R2MR]) exist as efforts to minimize the impact of exposures. To help inform on the impact of several categories of mental health training programs (i.e., CISM, CISD, mental health first aid, Peer Support, R2MR) for improving attitudes toward support and willingness to access supports among CCG and C&P officers, the current study assessed CCG and C&P Officers perceptions of access to professional (i.e., physicians, psychologists, psychiatrists, employee assistance programs, chaplains) and non-professional (i.e., spouse, friends, colleagues, leadership) support, and associations between training and mental health. Participants (n = 341; 58.4% male) completed an online survey assessing perceptions of support, experience with mental health training and symptoms of mental health disorders. CCG and C&P Officers reported access to professional and non-professional support; however, most indicated they would first access a spouse (73.8%), a friend (64.7%), or a physician (52.9%). Many participants would never, or only as a last resort, access other professional supports (24.0% to 47.9%), a CCG or C&P colleague (67.5%), or their leadership (75.7%). Participants who received any mental health training reported a lower prevalence of positive screens for all mental health disorders compared to those who did not received training; but no statistically significant associations were observed between mental health training categories and decreased odds for screening positive for mental disorders. The current results suggest that the mental health training categories yield comparable results; nevertheless, further research is needed to assess the shared and unique content across each training program. The results highlight the need to increase willingness to access professional and non-professional support among CCG and C&P Officers. Revisions to training programs for leadership and colleagues to reduce stigma around mental health challenges and support for PSP spouses, friends, and physicians may be beneficial.

On the same page? A qualitative study of shared mental models in an interprofessional, inter-organizational team implementing goal-oriented care

ABSTRACT Goal-oriented care is an approach to care delivery that uses patient-identified goals to drive care planning. Implementing goal-oriented care requires team members to cognitively shift the focus from “what is the matter” to “what matters to patients,” and align their mental models of what it means to care for patients. Yet, no empirical studies of goal-oriented care apply evidence from the cognitive sciences, such as Shared Mental Model (SMM) theory. We conducted a qualitative case study of an interprofessional team that adopted goal-oriented care in Vermont, US (n = 18). Guided by SMM theory, we distinguished between task-related and team-related mental models. We used framework analysis and qualitative content analysis to determine mental model content and similarity. The most shared content areas were operationalizing goal-oriented care, engaging in formal and informal communication, taking a “whole-person” approach, taking a team approach, and building trusting relationships with patients and with other team members. Trust was the only construct that spanned both task and team mental model categories, highlighting the importance of both intra-team trust and provider-patient trust to the implementation of goal-oriented care. Team members developed SMMs through training, regular meetings, and interactions during care delivery. This study provides insight into the cognitive mechanisms that underlie team-based goal-oriented care delivery, which can be used to inform implementation, training content, and future research.

Bhirawa Newspaper Editorial Management Facing Media Convergence

The development of information technology (IT) with the ease of internet services has made the competition in the mass media industry even tougher. The internet has facilitated the growth of online mass media. This study wants to reveal how newspapers respond to the growth of online mass media adding pressure to the existence of conventional mass media, especially newspapers. This research specifically reveals how the editorial policy of the Bhirawa newspaper in managing the media in order to survive in the midst of pressure from online media. The choice of Bhirawa newspaper as the object of research is because Bhirawa newspaper is one of the local newspapers in East Java that still exists today with dynamic market segmentation because it follows the times. From the results of this study, it was revealed that in responding to the presence of online media, Bhirawa newspaper applied the concept of content sharing. Content sharing allows two different media types to share content in the form of repackaging or including budget sharing. Because the production of print media with Bhirawa is online in the same space. This single newsroom is very efficient for small media that has multiple platforms. Because what is being sold is one brand, namely Bhirawa, both printed and online. In maintaining the market segment among the state civil apparatus (ASN), the Bhirawa newspaper strengthens information related to the bureaucracy and government by placing its journalists to focus on reporting posts in the government.

ICOPA 2022: living with parasites, collaborating globally, and breaking a glass ceiling

ICOPA 2022: living with parasites, collaborating globally, and breaking a glass ceiling

Graph-Based Conversation Analysis in Social Media

Social media platforms offer their audience the possibility to reply to posts through comments and reactions. This allows social media users to express their ideas and opinions on shared content, thus opening virtual discussions. Most studies on social networks have focused only on user relationships or on the shared content, while ignoring the valuable information hidden in the digital conversations, in terms of structure of the discussion and relation between contents, which is essential for understanding online communication behavior. This work proposes a graph-based framework to assess the shape and structure of online conversations. The analysis was composed of two main stages: intent analysis and network generation. Users’ intention was detected using keyword-based classification, followed by the implementation of machine learning-based classification algorithms for uncategorized comments. Afterwards, human-in-the-loop was involved in improving the keyword-based classification. To extract essential information on social media communication patterns among the users, we built conversation graphs using a directed multigraph network and we show our model at work in two real-life experiments. The first experiment used data from a real social media challenge and it was able to categorize 90% of comments with 98% accuracy. The second experiment focused on COVID vaccine-related discussions in online forums and investigated the stance and sentiment to understand how the comments are affected by their parent discussion. Finally, the most popular online discussion patterns were mined and interpreted. We see that the dynamics obtained from conversation graphs are similar to traditional communication activities.

Assessment of the Medical Reliability of Videos on Social Media: Detailed Analysis of the Quality and Usability of Four Social Media Platforms (Facebook, Instagram, Twitter, and YouTube).

Introduction: In recent years, the internet and social media have become the primary source for patients to research their medical conditions. Given the billions of links that result from research, it has become increasingly important how medically high quality the priorities of the search algorithms are. This study aims to examine the medical quality of videos on social media. Material and Method: A new Gmail account was never used, and Facebook, Instagram, Twitter, and YouTube accounts were opened. The word “knee replacement” was searched via social media. The video duration, daily views, total views, number of likes, source, and shared content were recorded. The parameters were statistically evaluated using the scales JAMA, GQS, DISCERN, and VPI to measure the quality of the medical posts. Results: Correlations were found between JAMA, GQS, and DISCERN. No correlation between the VPI scales with other scales was found. It was found that the promotional content in videos other than Instagram was very high (56–70%). Academics and healthcare workers produced greater quality content than other groups. There is a clear dominance of healthcare practitioners on Instagram. The most shared content was informative, depending on the content. The most frequent users were alternative health practitioners. While YouTube had the highest JAMA, GQC, and DISCERN (2.98, 3.18, 37.5) scores, the lowest VPI (0.761) score was found. Discussion and Conclusions: It has been shown that Instagram and Twitter are not the right places to share videos with medical content. However, everyone should remember that Instagram is the best place to share short but popular videos. YouTube and Facebook are available resources to share videos of better medical quality with a higher score than others. We can say that the most reliable medical sources are Facebook and YouTube videos of physicians and medical staff.

SnapChat this, Instagram that: The interplay of motives and privacy affordances in college students’ sharing of food porn

The high use of social media among youth, coupled with smartphone ownership, has spawned the production and distribution of photos online. One particular genre of photos stands out as novel: food photos. Advancements in technologies have given users the ability to endow images with enhanced emotion and awe, turning food photos into food porn. This study uncovers—within the framework of uses and gratifications and affordance theory—whether food posting is food porn, why users share it, and how considerations of gratifications are connected to specific social media affordances. Our mixed-methods data were comprised of three sequential studies collected at an urban university in the United States using in-depth interviews (2015 and 2016) and open-ended questions from a survey (2019). Results suggested that most of the shared content served to enhance participants’ image via self-presentation. We argue that users’ perception of privacy affordances guides their choice of platform.

The determinants of Tunisian influencer-mompreneurs’ success: an exploratory study of a new form of female web entrepreneurship on Instagram

PurposeThis paper is designed with the aim to enlarge our understanding of a new form of women’s entrepreneurship in an emerging market, which is Tunisia. By exploring the role of nowadays’ “influencer-mompreneurs” on Instagram, our research provides a springboard to holistically investigate an up-to-date facet of female entrepreneurship in the digital era and to identify the determinants of its success.Design/methodology/approachThis study took place in Tunisia and examined a sample of 20 female followers of three successful influencer-mompreneurs. A qualitative exploratory study was conducted through semistructured interviews. To analyze the collected data, the authors used thematic analysis, through which the determinants of influencer-mompreneurs’ success on Instagram bearing on Tunisian followers’ perspectives and previous literature were identified.FindingsThe results suggest that the key determinants of influencer-mompreneurs’ success from their followers’ perspectives depend on their personal traits and their shared content. This is consistent with previous theories and models. In particular, source credibility theory and Ducoffe’s model of advertisement value, which to our knowledge, has not yet been wholly served with regard to influencer marketing research. Paradoxically, the findings reveal that “sharenting” details of their private lives as well as those of their children is also a determinant of influencer-mompreneurs’ success.Originality/valueResearch into influencer-mompreneurs’ success with presence of their children on Instagram can help to provide valuable insights because existing literature into the subject is ambiguous and requires further research to clarify this research gap. Furthermore, by broadening the view of a new form of women entrepreneurship in relation to mother-influencer marketing, this study will inspire future research to dig deeper into creative women owned businesses and to support such emerging economies in the African context.

JRS: A Joint Regulating Scheme for Secretly Shared Content Based on Blockchain

For the sake of privacy, encrypting the content shared on the Internet is becoming popular. However, it brings a nontrivial challenge in regulating the illicit shared content (e.g., viruses, rumors, malicious code, etc.). To deal with it, lots of schemes have been proposed, which mainly rely on a third-party—regulatory authority (RA), to perform auditing, regulating or supervising exclusively. Although these schemes mitigate the problem of illicit shared content, they still suffer from the following two issues. On one hand, relying on a single RA alone may lead to the serious problems of injustice and inadequate regulating. On the other hand, users’ privacy is violated since an independent RA unconditionally inspects all the files shared by users. Therefore, we propose a joint regulating scheme (JRS) for secretly shared content based on threshold secret sharing algorithm and blockchain technology. To make the censor activities democratic and public, they are authorized by both the RAs and blockchain miners during which two threshold secret sharing algorithms are applied. Further, only the suspicious users (or files) are censored and the whole process is recorded as blockchain transactions to balance the user privacy and censoring fairness. At last, the results of security and performance analysis show that JRS can resist the known attacks and the cost is acceptable in practice.

An Extended Ultimatum Game for Multi-Party Access Control in Social Networks

In this article, we aim to answer an important set of questions about the potential longitudinal effects of repeated sharing and privacy settings decisions over jointly managed content among users in a social network. We model user interactions through a repeated game in a network graph. We present a variation of the one-shot Ultimatum Game, wherein individuals interact with peers to make a decision on a piece of shared content. The outcome of this game is either success or failure, wherein success implies that a satisfactory decision for all parties is made and failure instead implies that the parties could not reach an agreement. Our proposed game is grounded in empirical data about individual decisions in repeated pairwise negotiations about jointly managed content in a social network. We consider both a “continuous” privacy model as well the “discrete” case of a model wherein privacy values are to be chosen among a fixed set of options. We formally demonstrate that over time, the system converges toward a “fair” state, wherein each individual’s preferences are accounted for. Our discrete model is validated by way of a user study, where participants are asked to propose privacy settings for own shared content from a small, discrete set of options.

Using Extended Technology Acceptance Model to Assess the Adopt Intention of a Proposed IoT-Based Health Management Tool.

Advancements in IoT technology contribute to the digital progress of health science. This paper proposes a cloud-centric IoT-based health management framework and develops a system prototype that integrates sensors and digital technology. The IoT-based health management tool can collect real-time health data and transmit it to the cloud, thus transforming the signals of various sensors into shared content that users can understand. This study explores whether individuals in need tend to use the proposed IoT-based technology for health management, which may lead to the new development of digital healthcare in the direction of sensors. The novelty of this research lies in extending the research perspective of sensors from the technical level to the user level and explores how individuals understand and adopt sensors based on innovatively applying the IoT to health management systems. By organically combining TAM with MOA theory, we propose a comprehensive model to explain why individuals develop perceptions of usefulness, ease of use, and risk regarding systems based on factors related to motivation, opportunity, and ability. Structural equation modeling was used to analyze the online survey data collected from respondents. The results showed that perceived usefulness and ease of use positively impacted adoption intention, Perceived ease of use positively affected perceived usefulness. Perceived risk had a negative impact on adoption intention. Readiness was only positively related to perceived usefulness, while external benefits were positively related to perceived ease of use and negatively related to perceived risk. Facilitative conditions were positively correlated with perceived ease of use and negatively correlated with perceived risk. Technical efficacy was positively related to perceived ease of use and perceived usefulness. Overall, the research model revealed the cognitive mechanism that affects the intention of individuals to use the system combining sensors and the IoT and guides the digital transformation of health science.