Mobile Social Applications Research Articles

Extracting Keywords from Texts based on Word Frequency and Association Features

With the development of information technology such as mobile Internet and social media applications, network information is growing rapidly and leads to the problem of information overload. Keywords help to filter and find interesting information for users from massive text. Automatic extraction of keywords from text as tags of text help to improve recommendation and keyword-based information retrieval. This paper proposes a novel keyword extraction approach from text that combines features such as word frequency and association. Experiment results show that the precision rate, recall rate and F-measure are all better than those of TextRank and TF-IDF.

FashionTransformation 4.0: Beyond Digitalization & Marketing in Fashion Industry

Many businesses are attempting to transform their business operations & strategies by adapting new technologies and selling trendy products on various platforms in the current climate of sustainability's newest trends. Digital marketing is an important component of the fashion industry because it operates at all levels of the fashion system. Digital fashion practises, ranging from digital communication and online reputation to e-commerce, are becoming increasingly popular. With a single click, customers can access the entire world through this digital mode. Fashion is undergoing a significant digital transformation, with garments and apparel being presented and sold online, and fashion trends and styles being launched, discussed, and negotiated primarily online. Email campaigns, website optimization, social media campaigns, online forums, television advertisements, mobile applications, and other digital marketing strategies are all used to reach out to potential customers and increase sales. This research will open the doors to enhance the efficacy of various digital tools, practices and platforms for communication.

Twitter’s impact in building reputed hospital brands in USA

Social media have become a strategic tool for hospitals interested in boosting their corporate communication and achieving several organizational objectives such as improving patient’s engagement or reinforcing their own corporate reputation. These platforms help hospitals adapt their communication strategies to a new context (new patients’ demands, increasing competition between health organizations, development of health technologies, etc.). This paper aims to analyse Twitter’s impact in branding initiatives led by hospitals. To do that, we carried out a literature review about corporate communication, branding and social media; and then, we analysed Twitter’s corporate profiles of the best US hospitals in the treatment of oncological diseases. This paper concludes that hospitals interested in effectively using Twitter as a corporate communication tool for branding initiatives need to carry out a strategic reflection before launching any initiative on this platform, improve Twitter’s integration with other social media platforms and mobile applications, and facilitate better collaborations between health professionals and communication experts.

Evolution of research topics in LIS between 1996 and 2019: an analysis based on latent Dirichlet allocation topic model

This study investigated the evolution of library and information science (LIS) by analyzing research topics in LIS journal articles. The analysis is divided into five periods covering the years 1996–2019. Latent Dirichlet allocation modeling was used to identify underlying topics based on 14,035 documents. An improved data-selection method was devised in order to generate a dynamic journal list that included influential journals for each period. Results indicate that (a) library science has become less prevalent over time, as there are no top topic clusters relevant to library issues since the period 2000–2005; (b) bibliometrics, especially citation analysis, is highly stable across periods, as reflected by the stable subclusters and consistent keywords; and (c) information retrieval has consistently been the dominant domain with interests gradually shifting to model-based text processing. Information seeking and behavior is also a stable field that tends to be dispersed among various topics rather than presented as its own subject. Information systems and organizational activities have been continuously discussed and have developed a closer relationship with e-commerce. Topics that occurred only once have undergone a change of technological context from the networks and Internet to social media and mobile applications.

What influences the “when” of eating and sleeping?A qualitative interview study

Timing of eating relative to sleep and endogenous circadian rhythm impacts weight and cardiometabolic health. We used qualitative methods to explore what influences the “when” of eating and sleeping. We conducted 37 one-on-one semi-structured interviews among participants with a body mass index (BMI) ≥ 25 kg/m2 recruited from three internal medicine clinics affiliated with an urban academic hospital. Participants (70.3% Female; 51.4% White; Age range: 21–83 years old) completed measures of social jetlag, physical activity, eating habits, and mobile application use and participated in interviews following a guide developed by the study team. Responses were recorded, transcribed and coded sequentially by two trained researchers using editing-style analysis to identify themes. We identified two main themes, each with subthemes: 1) influences on the “when” of eating and sleeping, with subthemes including social jetlag and being overscheduled, and 2) contextualizing beliefs and perceptions about the “when” of eating and sleeping, with subthemes including perceived recommendations for timing of eating and sleeping, and alignment of behaviors with perceived recommendations. Many participants noted being more flexible in their eating and sleeping times on work-free vs work days. The themes this study identified should be considered when designing interventions that influence the timing of eating and sleeping for weight management.

A Systematic Literature Review on e-Mental Health Solutions to Assist Health Care Workers During COVID-19.

Background: e-Mental health is an established field of exploiting information and communication technologies for mental health care. It offers different solutions and has shown effectiveness in managing many psychological issues. Introduction: The coronavirus disease 2019 (COVID-19) pandemic has critically influenced health care systems and health care workers (HCWs). HCWs are working under hard conditions, and are suffering from different psychological issues, including anxiety, stress, and depression. Consequently, there is an undeniable need of mental care interventions for HCWs. Under the circumstances caused by COVID-19, e-health interventions can be used as tools to assist HCWs with their mental health. These solutions can provide mental health care support remotely, respecting the recommended safety measures. Materials and Methods: This study aims to identify e-mental health interventions, reported in the literature, that are developed for HCWs during the COVID-19 pandemic. A systematic literature review was conducted following the PRISMA protocol by searching the following digital libraries: IEEE, ACM, ScienceDirect, Scopus, and PubMed. Results and Discussion: Eleven publications were selected. The identified e-mental health interventions consisted of social media platforms, e-learning content, online resources and mobile applications. Only 27% of the studies included empirical evaluation of the reported interventions, 55% listed challenges and limitations related to the adoption of the reported interventions. And 45% presented interventions developed specifically for HCWs in China. The overall feedback on the identified interventions was positive, yet a lack of empirical evaluation was identified, especially regarding qualitative evidence. Conclusions: The COVID-19 pandemic has highlighted the importance and need for e-mental health solutions for HCWs.

Examining artifacts generated by setting Facebook Messenger as a default SMS application on Android: Implication for personal data privacy

AbstractThe use of mobile devices and social media applications in organized crime is increasingly increasing. Facebook Messenger is the most popular social media applications used globally. Unprecedented time is spent by many interacting globally with known and unknown individuals using Facebook. During their interaction, personal information is uploaded. Thus, crafting a myriad of privacy trepidation to users. While there are researches performed on the forensic artifacts' extraction from Facebook, no research is conducted on setting Facebook Messenger applications as a default messaging application on Android. Two Android mobile devices were used for data generation and Facebook Messenger account was created. Disc imaging and data partition were examined and accessed to identify changes in the orca database of the application package using DB browser. The data were then generated using unique words which were used for conducting key searches. The research discovered that mqtt_log_event0.txt of the Com.Facebook.orca/Cache directory stores chat when messenger is set as a default messaging app. The research finding shows that chats are recorded under messages tab together with SMS of data/data/com.facebook.orca/databases/smstakeover_db and data/data/com.facebook.orca/databases/threads_db. This indicates that only smstakeover_db stores SMS messaging information when using messenger application. It is observed that once the user deletes a sent SMS message, the phone number and the deleted time stamp remained in the data/data/com.facebook.orca/databases/smstakeover_db database in the address_table are recoverable. The results suggest that anonymization of data is essential if Facebook chats are to be shared for further research into social media content.

Social Correlates of Recent Suicidal Ideation Among Men Who Have Sex with Men in Greater Tokyo

Suicide is a major public health problem in Japan, where mortality rates are among the highest in the world. Globally, men who have sex with men (MSM) report elevated rates of suicidal ideation and attempts compared to their heterosexual peers. However, there is a paucity of research on suicidal ideation among MSM in Japan, where MSM continue to experience significant stigma and discrimination, and where resources are limited for MSM-specific mental health support. This study examined the prevalence and correlates of recent suicidal ideation among MSM in Greater Tokyo. We recruited 1,657 MSM for an anonymous, online, cross-sectional survey posted on social mobile applications popular with MSM in Japan (e.g., Grindr, 9Monsters) from November 2015 through January 2016. Recent suicidal ideation, measured as suicidal thoughts in the preceding two weeks, was reported by almost one-third (31%) of participants. Participants who reported unemployment or part-time employment (compared to full-time employment) and knowing a heterosexual person who restricted their freedom or excluded them from a group had higher odds of suicidal ideation. Being aged 46 years or older, reporting good health, and having a medium or high level of access to social capital networks with other MSM were associated with reduced odds of suicidal ideation. Our findings indicate that suicide prevention interventions with MSM in Greater Tokyo and similar urban settings in Japan should work to enhance MSM social networks as a protective factor; attend to employment concerns, particularly in younger MSM; and augment current efforts to address stigma and discrimination in the community.

Machine learning approach for threat detection on social media posts containing Arabic text

Recently, social media has become a part of daily people’s routine. People frequently share images, text, and videos in social media (e.g., Twitter, Snapchat, Facebook, and Instagram). Consequently, there is a demand for an automated method to monitor and analyze the shared social media content. This research developed a method that aims to detect any threat in the images or comments in the shared content. Instagram has gained popularity as the most famous social media website and mobile application for media sharing. Instagram enables users to upload, view, share, and comment on a media post (image or video). There are many unwanted contents in Instagram posts, such as threats, which may cause problems for society and national security. The purpose of this research is to construct a model that can be utilized to classify Instagram content (images and Arabic comments) for threat detection. The model was built using Convolutional Neural Network, which is a deep learning algorithm. The dataset was collected utilizing the Instagram API and search engine and then labeled manually. The model used was retrained on the images and comments training set with the classes of threat and non-threat. The results show that the accuracy of the developed model is 96% for image classification and 99% for comment classification. The result of this research will be useful in tracking and monitoring social media posts for threat detection.

Viewpoint: Emerging Digital Technologies: A Sinister Presage for Privacy?

The purpose of this paper is to discuss the impact of the Internet of Things (IoT), big data and Artificial Intelligence (AI) on consumer privacy. It presents the rationale behind the implementation of such robust complementary trio in the digital age with regard to the rapidly changing consumer behavior engendered by the fast emergence of new technologies. Consequently it exposes the reasons why privacy will be controlled not by the individual but rather by the smart devices and applications used by the individual. The approach has been based upon the discussion and the remarkably fast evolution of powerful data collectors such as social media and mobile applications together with associated analytics software that are used to describe and predict trends, patterns, relationships and behaviors. It is propounded that although IoT, big data and AI are undeniably major advancements in computer technology, the true innovation will occur only when their combined use enables users to take effective decisions, maintain positive relationships, improve their performance and enhance their lives while preserving their privacy. This paper gathers some of the latest discussions about privacy in an era where technology is omnipresent and continuously persisting in complex and inconspicuous ways to occupy a larger place in every individual's life. It also acts as an eye-opener about the tremendous importance of preserving privacy in order to maintain independence, individuality and security as well as to reduce the risk of an irreversible digital invasion.

Use of Social Media Apps by Nurses for Professional Purposes in Catalonia: Cross-Sectional Study.

Over the last few years, the use of social media mobile applications or apps (SMAs) has increased exponentially. The potential advantages of using these technologies by health professionals in clinical settings have been discussed many times. Considering that the nursing profession is the largest segment of the healthcare workforce in the majority of countries in the world, the impact of using these apps by these professionals is very relevant. The objectives of this study were, firstly, to determine if nurses were using SMAs professionally and the most frequent SMAs used and secondly, to find out if, among nurses, there is a need for training in the use of these mobile applications for professional purposes. The study is a descriptive cross-sectional study based on an Internet survey of 1,293 nurses in Catalonia (Spain). The average age of the respondents who had these apps installed on their mobile phones or tablets, was 43.12 (SD ± 11.32) years old. WhatsApp was the most frequent SMAs used by nurses for professional purposes, and 79.2 % of nurses mentioned they used it several times a day. WhatsApp was the preferred SMAs for communicating with colleagues (31.2% of nurses) followed by Facebook (18.4%) and Twitter (11.3%). In contrast, the use of the SMAs was much less frequent as a means of communication with patients (7.2% in the case of WhatsApp). Nurses expressed their need for specific training in the use of these apps for professional purposes, indicating the interest and potential impact of the introduction of these technologies in clinical environments. The use of SMAs is quite common among nurses at the moment of the survey, and WhatsApp was the most popular one to support their professional activity. Based on the results of the survey, the Nursing Association of Barcelona (COIB) will consider the design of specific training activities in the use of SMAs in clinical settings.

Hybrid neighborhoods: From sandlots to social media

The rapid development of Information and Communication Technologies (ICT) along with the mass urbanization phenomenon have led to dramatic changes in the ways people create social bonds, form and understand communities and act collectively towards common goals. One important change is that locality and distance is no longer perceived as a key prerequisite for the development of social bonds. Local communities, traditionally based on social grouping by physical proximity, have been seriously affected by technological media (social media and applications). Socio-psychological research shows that the major impact of technology-based communication is the transformation of social bonds between members of local communities and the social capital they accumulate. Within this framework, the research project “GEITONIA” has a dual scope. On a theoretical level, to shed more light on the different ways and degrees local communities use social media and applications in everyday life. On an empirical level, to examine if and in what ways a local social medium mobile application, developed for neighborhoods, can help the understanding of the sense of community and re-strengthen the social cohesion among its members. The article is an attempt to provide a quick glance on the key concepts and theoretical background on which the research project is based.

Trust2Privacy: A Novel Fuzzy Trust-to-Privacy Mechanism for Mobile Social Networks

Mobile social applications have been widely used by Internet users. Users can efficiently acquire many kinds of information and share their statuses by various social platforms. However, when a user intends to share information through the user's social applications, the user can set the access permission only before the information is posted. Once the information is posted, it is completely beyond the user's control. Specifically, during the recommendation process (for friend or information) in social applications, a user cannot control who can get the recommendation and access his/her information. If one user is accessed by another malicious user, his/her privacy information can be disclosed, and even be further misused for malicious attacks. In this article, we propose Trust2Privacy, a trust-based access control mechanism to protect the personalized privacy of users after posting their information, which can effectively realize the transformation from trust to privacy. First, to represent the relations accurately among users, we define the direction of trust among users according to the user-follow status. Then we combine the similarity, correlation, and interaction among users to calculate the trust values. Considering the fuzzy relationship between the multi-dimensional features and trust levels, we propose a fuzzy comprehensive evaluation algorithm to compute the fuzzy trust. Moreover, for the sake of the high mobility of mobile social networks, we exploit so-called online-to-offline trust evidence to derive the trust value, including taking the location information (e.g., distance, semantics) into consideration. To meet the personalized privacy requirement, we design a filtering algorithm based on the trust relationships and the privacy policy of the target users or posted information, according to which the access requesters can get the list of accessible users or information. This enables achieving the goal of personalized privacy protection. The theoretical analysis and simulation experiment demonstrate that Trust2Privacy is able to achieve personalized privacy protection without bringing negative impact on the availability and usability of mobile social applications.

A comparative evaluation of aggregation methods for machine learning over vertically partitioned data

It is increasingly common applications where data are naturally generated in a distributed fashion, especially after the emergence of technologies like the Internet of Things (IoT). In sensor networks, in collaborative health or genomic projects, in credit risk analysis, among other domains, distinct features are collected from multiple sources, including the use of social media and mobile applications, and due to privacy concerns or communication costs, may not be shared among sites. This scenario of vertical data partitioning poses challenges to traditional machine learning (ML) approaches, as classical algorithms are designed to learn from the complete set of features. A common strategy is to combine predictions from local models trained at each site into a global model, and for this purpose, several aggregation methods have been proposed. In this work we tackle a gap within the related literature, performing a comparative evaluation of elementary and meta-learning-based aggregation methods to reveal their strengths and weakness for 46 datasets with varied characteristics. We show that no method outperforms its counterparts in all domains, emphasizing the need for experimental comparison to ensure a good choice in the domain of interest. Moreover, our experiments provide the first insights into the relations between datasets’ properties and aggregators’ performance. We show that for low class imbalance and a good instance-to-feature ratio, almost all aggregation methods tend to perform well. The silhouette coefficient (reflecting class separability) and class imbalance coefficient are the most influential properties on aggregators’ performance, thus we recommend their analysis in the first step of the methodological design. We found that arithmetic-based methods are not suitable for datasets with poor class separability and a large number of classes, whereas meta-learning approaches are less sensitive for datasets with silhouette coefficient close to 0. Our analyses were summarized as classification and regression trees, which have the impact to serve as practical tools for future research. Taken together, our findings give rise to interesting applications in the domain of intelligent systems, especially regarding their potential to reduce the burden of vast experimental comparisons when training ML models with feature-partitioned data.

Thematic Organization of Conversations in the Voip Application Viber

In a large number of social media and mobile applications, a new hybrid oral-writing formation is emerging, which is called from linguists' written spoken speech with similar features to both written and oral speech, but also has its own distinctive features. This study will illustrate the thematic structure of a conversation using the VoIP application Viber to highlight the most common model of a zig-zag themed organization and its specifics.

Fast head detection in arbitrary poses using depth information

Purpose This study aims to develop a real-time algorithm, which can detect people even in arbitrary poses. To cover poor and changing light conditions, it does not rely on color information. The developed method is expected to run on computers with low computational resources so that it can be deployed on autonomous mobile robots. Design/methodology/approach The method is designed to have a people detection pipeline with a series of operations. Efficient point cloud processing steps with a novel head extraction operation provide possible head clusters in the scene. Classification of these clusters using support vector machines results in high speed and robust people detector. Findings The method is implemented on an autonomous mobile robot and results show that it can detect people with a frame rate of 28 Hz and equal error rate of 92 per cent. Also, in various non-standard poses, the detector is still able to classify people effectively. Research limitations/implications The main limitation would be for point clouds similar to head shape causing false positives and disruptive accessories (like large hats) causing false negatives. Still, these can be overcome with sufficient training samples. Practical implications The method can be used in industrial and social mobile applications because of its robustness, low resource needs and low power consumption. Originality/value The paper introduces a novel and efficient technique to detect people in arbitrary poses, with poor light conditions and low computational resources. Solving all these problems in a single and lightweight method makes the study fulfill an important need for collaborative and autonomous mobile robots.

Technology usage for teaching and learning law in open and distance learning: a Sri Lankan perspective

PurposeThe Open University of Sri Lanka (OUSL) is the only institution to deliver legal education through Open and Distance Learning (ODL) in Sri Lanka. This study aims to analyze technology usage in learning and teaching law in the ODL under OUSL to evaluate the accessibility and also challenges.Design/methodology/approachA combination of qualitative and quantitative methodologies has been used for the study. This includes both interviews with teachers and surveying among students on the usage of technology in learning and teaching law at OUSL. Since the LL.B Degree Programme of OUSL delivered at six regional centres in Sri Lanka, this study also includes the comparative analysis of technology usage in teaching and learning at selected regional centres.FindingsThe findings indicate that the majority of students have access to technology through mobile phones and are aware of blended learning. Even though they prefer to integrate blended learning with learning law, they do not prefer learning entirely online. Social media and mobile applications are the most preferred modes of blended learning by students. It is also acknowledged that the internal staff has knowledge and access to the use of technology in teaching law while the external staff faces challenges and is in need of adequate training.Originality/valueThe original contribution of this article provides insightful guidelines not only to the OUSL of Sri Lanka but also to the institutions offering similar disciplines through ODL to understand lecturers, learners in the future integration of technology.

Combating Stigma Through HIV Self-Testing: New York State's HIV Home Test Giveaway Program for Sexual Minorities.

Gay, bisexual, and men who have sex with men (MSM) are disproportionately affected by the HIV/AIDS epidemic more than any other group. In New York State (NYS) outside of New York City (NYC), MSM accounted for 57% of new HIV/AIDS diagnoses in 2017. HIV/AIDS home testing initiatives have been effective at getting priority populations tested for HIV. The NYS Department of Health (NYSDOH) administered the HIV Home Test Giveaway (HHTG) program through social media and mobile applications popular among the priority populations to (1) promote HIV screening among gay and bisexual men, MSM, transgender (TG), and gender queer/gender nonconforming (GNC) individuals who have sex with men, and (2) identify individuals with undiagnosed HIV infection. The NYSDOH recruited participants from NYS (excluding NYC) through social media campaigns between November 2016 and January 2018. Interested individuals were directed to an online eligibility survey. Eligible participants received a coupon via e-mail for a free HIV self-test (HIVST) kit through the manufacturer's Web site. Eligible participants received a $20 to $25 Amazon online gift card if they completed a voluntary online follow-up survey regarding their test results, experiences with the HHTG, and HIV/AIDS risk behaviors. Participants were also able to request assistance from the NYSDOH with further testing and various prevention and supportive services. In total, 6190 individuals participated and 3197 (52%) were eligible and received a coupon. Of the eligible, 2022 (63%) redeemed coupons for HIVST. Among eligible participants, 976 (31%) reported having never been tested for HIV. On the follow-up survey, 922 (29%) participants reported having used the HIVST kit for themselves; 203 (22%) were first-time testers; and 7 (0.8%) tested HIV reactive. Of the follow-up survey participants, 761 (59%) requested assistance with various services. Media campaigns were effective in promoting HIV testing among priority populations and reaching individuals who have never been tested for HIV.

ВЕБ-АРХИВЫ В РЕКОНСТРУКЦИИ ИСТОРИИ ВИРТУАЛЬНЫХ МУЗЕЕВ: ПОТЕНЦИАЛ И ОГРАНИЧЕНИЯ

Web archives are repositories of unique sources on the history of the information society, including the cultural segment of the World Wide Web. The relevance of studying the web history of museum information resources refers to the need to understand the past and contemporary processes of the development of the museum's digital environment in order to more effectively build strategies for future advancement with a valuable impact on society. The article, for the first time, attempts to assess the information potential of web archives for studying the web history of virtual museums and discusses the limitations that prevent the reconstruction of their web history. Web archives are designed to observe web pages and web sites saved at a certain point in time; they analyze the structure and content of the museum web, interpret the visual aids and sections' titles, and track statistics of publication activity. Tracing changes in the role and significance of the digital environment in museum activities, as well as trends in the development of museums, and predicting future trajectories are possible based on the analysis of the dynamics of museums' web content. At the same time, the peculiarities of search engines in web archives, technical restrictions, incompatibility of modern software with earlier formats, limits on scanning information on the World Wide Web to save it, uneven preservation by domain zones in the Internet Archive, and the lack of specialized web preservation programs at national and regional levels restraint the possibility of a comprehensive study of the history of virtual museums. The author concludes that it is necessary to expand national web archiving programs in favour of a more detailed preservation of the cultural segment of the web as a digital cultural heritage, as well as the content of social networks and mobile applications, for future use by researchers.

Energy-aware caching and collaboration for green communication systems

Social networks and mobile applications tend to enhance the need for high-quality content access. To meet the growing demand for data services in 5G cellular networks, it is important to develop effective content caching and distribution techniques, to reduce redundant data transmission and thereby improve network efficiency significantly. It is anticipated that energy harvesting and self-powered Small Base Stations' (SBS) are the rudimentary constituents of next-generation cellular networks. However, uncertainties in harvested energy are the primary reasons to opt for energy-efficient (EE) power control schemes to reduce SBS energy consumption and ensure the quality of services for users. Using edge collaborative caching, such EE design can also be achievable via the use of the content cache, decreasing the usage of capacity limited SBSs backhaul and reducing energy utilisation. Renewable energy (RE) harvesting technologies can be leveraged to manage the huge power demands of cellular networks. To reduce carbon footprint and improve energy efficiency, we tailored a more practical approach and propose green caching mechanisms for content distribution that utilise the content caching and renewable energy concept. Simulation results and analysis provide key insights that the proposed caching scheme brings a substantial improvement regarding content availability, cache storage capacity at the edge of cellular networks, enhances energy efficiency, and increases cache collaboration time up to 24%. Furthermore, self-powered base stations and energy harvesting are an ultimate part of next-generation wireless networks, particularly in terms of optimum economic sustainability and green energy in developing countries for the evolution of mobile networks.