Media Domain Research Articles

A Pun Identification Framework for Retrieving Equivocation Terms based on HLSTM Learning Model

The artificial intelligence system is made linguistically intelligent through HLSTM model to identify pun expressions from code mixed text. The text available on social media domain is written in mixed script format and from these content puns word identification is a challenging task in this scenario. The retrieval of pun and its corresponding equivocation terms is very hard to retrieve from the transliterated text. The pun retrieval and its equivocation representation are widely used to present the opinions over the network applications. The work described in the paper gives the comparative view of different learning models and techniques applied in the area of transliteration for pun word retrieval. The rule framed approach is presented which accepts the roman form text as input and as per the defined rules the system is developed to give the equivocation words available in the sentence. The evaluation measures used here to validate the hypothesis is based on statistical measures along with HLSTM learning model. Further the result is validated using the voting technique that can choose appropriate equivocation label which are not identifies by the learning model. The use of voting technique here is to provide an extra edge when the proposed approaches suggest incorrect tag against the pun word. The voting approach enhances the overall result accuracy with high precision value.

E-Professionalism: challenges of being social in social media in health profession

The Digital revolution is set to influence medicine significantly in the coming days. Healthcare professionals should embrace the positive potential of social media (SM) and social networking sites (SNSs) and take advantage of these technologies and tools for their personal and professional development but also be aware of the potential impact of their online behaviors.Moorhead et al identified six arching domains of social media use by the general public, patients, and health professionals. These include Information provision and answering questions on various conditions; facilitation of dialogue between patients, and patients and healthcare professionals; data collection on patient experiences and opinions; SM use for health intervention;health promotion and health education; reduction of stigma & online consultations (Moorhead et al., 2013). Social media has various advantages, such as reaching an extensive audience, low cost, peer/social/emotional support, helping students to keep updated the latest health trends, helping them to formally and informally learn the material, prompt communication & potential to influence health policy (Moorhead et al., 2013; Mostaghimi &Crotty, 2011). At the same time, there are various limitations of social media use in healthcare. These include lack of reliability, information overload, lack of confidentiality & privacy, risks associated with providing incorrect advice using social media, concerns about the correct application of online information to one’s personal health & adverse health consequences. Furthermore, in some cases, social media may restrict patients from visiting health professionals (Moorhead et al., 2013).

Social Perceived Value on Social Media and Online News Portal: Benefits to The Aborigines Women in Malaysia

Online news media and social media applications recorded an unprecedented success in just few of the recent years. However, online news media and social media domain are still little understood in term of usage among aborigines or Orang Asli in Malaysia. Thus, this research is to see the social perceived value on social media and online news portal among Orang Asli. A quantitative research design is used based on data will be collected via questionnaire from Orang Asli at Kampung Batu 12, Gombak, Selangor, at the outskirt of Kuala Lumpur. Questionnaires were distributed to 50 Orang Asli aged 15 and above. Factor analysis was used to examined the data. Exploratory factor analysis was used to extracts new factor, Online media usage impact. The social values perceived (16 items) factor was examined. After identifying the component based on an examination of eigenvalues and scree plot, rotation was done by varimax method. The items with 0.5 loadings or greater are considered practically significant. The findings showed that most of the Orang Asli gathered current information and news from the social media and social media is the gateway to online news portal. Furthermore, despite of financial circumstances, they willing to spend in order to have the internet access as it gave positive social values in their life, plus the applications internet access were all user friendly.

Data-driven model order reduction for granular media

We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-driven approach, we run many offline simulations in advance and train a model to predict the velocity field from the mass distribution and system control signals. Rapid model inference of particle velocities replaces the intense process of computing contact forces and velocity updates. In coupled DEM and multibody system simulation, the predictor model can be trained to output the interfacial reaction forces as well. An adaptive model order reduction technique is investigated, decomposing the media in domains of solid, liquid, and gaseous state. The model reduction is applied to solid and liquid domains where the particle motion is strongly correlated with the mean flow, while resolved DEM is used for gaseous domains. Using a ridge regression predictor, the performance is tested on simulations of a pile discharge and bulldozing. The measured accuracy is about 90% and 65%, respectively, and the speed-up range between 10 and 60.

Accuracy in trait judgments based on WeChat: Detecting who stands on the extreme levels of the big-five trait continua

Trait inferences occur not only during social interaction but also in virtual environments. Previous research suggested social media owners (targets) revealed their real but not idealized personality traits online, based on which people were to some extent able to make accurate judgments of the big-five traits of the targets. In the present research, participants made inferences of different levels of targets' big-five traits (low, meddle or high) after briefly browsing scant information of the unacquainted WeChat owners. Results demonstrated that participants were not uniformly accurate in judging each level of each of the big-five traits; they were prone to be accurate when judging a target located on the extremes (low or high) of the traits, but were less accurate when judging a target located at the average level. These findings extend previous results from real-world scenarios revealing people's ability to perceive other persons based on subtle signals in the domain of social media.

An Evaluation of the Accuracy of the Machine Translation Systems of Social Media Language

In this age of information technology, it has become possible for people all over the world to communicate in different languages through social media platforms with the help of machine translation (MT) systems. As far as the Arabic-English language pair is concerned, most studies have been conducted on evaluating the MT output for the standard varieties of Arabic, with fewer studies focusing on the vernacular or colloquial varieties. This study attempts to address this gap through presenting an evaluation of the performance of MT output for vernacular or colloquial Arabic in the social media domain. As it is currently the most widely used MT system, Google Translate (GT) has been chosen for evaluating the reliability of its output in the context of translating the Arabic colloquial language (i.e., Egyptian/Cairene Arabic variety) used in social media into English. With this goal in mind, a corpus consisting of Egyptian dialectal Arabic sentences were collected from social media networks, i.e., Facebook and Twitter, and then fed into GT system. The GT output was then evaluated by three human translators to assess their accuracy of translation in terms of adequacy and fluency. The results of the study show that several translation problems have been spotted for GT output. These problems are mainly concerned with wrong equivalents, inappropriate additions and deletions, and transliteration for out-of-vocabulary (OOV) words, which are mostly due to the literal translation of the Arabic vernacular sentences into English. This can be due to the fact that Arabic vernacular varieties are different from the standard language for which MT systems have been basically developed. This, consequently, necessitates the need to upgrade such MT systems to deal with the vernacular varieties.

NITSCHE'S TYPE STABILIZATION FOR THE FULLY MIXED NAVIER-STOKES/DARCY PROBLEM

In this paper, we present and analyze a fully mixed finite element scheme for the Navier-Stokes/Darcy problem based on the Nitsche's type interface stabilizations, in the fluid region coupled with the porous media domain. The reasonable parameter $ \delta>0 $, which is independent of mesh size $ h $, will guarantee the stability and optimal convergence of our stabilized scheme. Moreover, we explicitly derive the dependence and requirement of the stabilization parameter $ \delta $ for the optimal error estimates, while the numerical tests support the stability and efficiency of this stabilized mixed method.

Thermomechanical phase‐field fracture modeling of fluid‐saturated porous media

AbstractIn this work, the problem of brittle fracture in a fluid‐saturated porous material is extended by considering the non‐isothermal states of the sample. The temperature field will affect the problem in two aspects: 1) Temperature‐dependent material parameters, such as elasticity modulus (E) and critical energy release rate (Gc). 2) Thermal expansion due to thermo‐mechanical volume coupling. In hydraulic fracturing, we further study the effect of the temperature difference between the injected fluid and the surrounding porous media ambient on the crack behavior. The modeling of the porous media domain is based on the macroscopic theory of porous media (TPM), whereas the phase‐field method (PFM) is applied to approximate the sharp crack edges by diffusive ones. In the numerical implementation, the coupled system of partial differential equations will be solved using the FEM in order to simulate the heat transition in the crack and non‐crack regions.

‘I am perfectly imperfect because I am a media professional’: Asserting the Association Between Media Consumption Attitude, Emotional Ownership and Personal Well-being

A profession that leads to well-being is rewarding and fulfilling, as it is a vital factor in ascertaining the happiness and purpose of one’s life. Information and communications technology has become an integral part of everyone’s life in contemporary times. Media professionals are engaged in using various types of digital and online media for their work-related needs. At many times, due to extensive work requirements, they carry their professional commitments outside the office hours. Due to their obsessive media consumption attitude, they lose out on spending time with their families and themselves. The purpose of this study was to examine the role of such media consumption attitude in relation to personal well-being. For this, 429 working professionals from different domains of media across the Indian subcontinent participated in our survey. The results revealed that media usage has a significant role in influencing personal well-being. At the same time, emotional ownership stood to be a mediator in the relationship between media consumption attitude and personal well-being. The findings pave the way to understand the professional and personal challenges faced by media professionals, while suggesting a possible roadmap for achieving a happy and purposeful life.

Gender moderates the relationship between media use and sleep quality.

With high screen time and poor sleep commonly reported in adolescents, it is important to more fully understand how screen time impacts sleep. Despite similar overall screen times, male and female media preferences and usages differ, making it critical to determine if different domains of screen time differentially affect sleep quality. The present study examined whether differing amounts and domains of screen-based media vary in impact on sleep quality of 16-year-old male and female adolescents over a 3-month period. A total of 98 adolescents (mean [SD] age 16.27[0.29]years; 51% female) completed two online surveys spaced 3months apart and comprised of well-validated self-reported measures of sleep quality, media usage, and depressive symptoms. The various domains of media were categorised into screen-based media with little-to-no peer-to-peer interaction involved (video-only) and screen-based media with interaction a predominant component to the usage (peer-to-peer interaction-involved). Self-reported sleep quality decreased across the 3-month study period. Gender moderated the effect of interactive screen time on sleep quality 3months later, with interactive screen time associated with better sleep quality in males, but remaining poorer in females. Screen time competes with sleep time and may do so differentially depending on the media domain. Compared to females, interactive components of screen time may lessen worsening sleep quality over time in males. Understanding the relationships among screen time, its content, age, and gender may inform guidelines for educators, parents, and adolescents to help improve sleep quality of adolescents.

Transport of Turbulence Across Permeable Interface in a Turbulent Channel Flow: Interface-Resolved Direct Numerical Simulation

Turbulence transportation across permeable interfaces is investigated using direct numerical simulation, and the connection between the turbulent surface flow and the pore flow is explored. The porous media domain is constructed with an in-line arranged circular cylinder array. The effects of Reynolds number and porosity are also investigated by comparing cases with two Reynolds numbers (Reapprox 3000,6000) and two porosities (varphi =0.5,0.8). It was found that the change of porosity leads to the variation of flow motions near the interface region, which further affect turbulence transportation below the interface. The turbulent kinetic energy (TKE) budget shows that turbulent diffusion and pressure transportation work as energy sink and source alternatively, which suggests a possible route for turbulence transferring into porous region. Further analysis on the spectral TKE budget reveals the role of modes of different wavelengths. A major finding is that mean convection not only affects the distribution of TKE in spatial space, but also in scale space. The permeability of the wall also have an major impact on the occurrence ratio between blow and suction events as well as their corresponding flow structures, which can be related to the change of the Kármán constant of the mean velocity profile.

Rodrigo Duterte as Ideology: Academic vs. Social Media Myths and Representations and their Implications to Political Order

Abstract Rodrigo Duterte is imaged as an ideology through narratives, texts, discourses and representations which emerge in a highly contentious discursive terrain. This paper places this in two domains, namely in academic theorizing and popular culture, particularly in social media, both of which are implicated in representational politics. Academic theorizing about Duterte attempts to be objective and scholarly, but is dominated by anti-Duterte sentiments that are mainly born from liberal and critical orientations. The pro-Duterte social media is not only anti-elite but also has an anti-intellectual orientation. Social media is an effective contrapuntal in painting academic theorizing as a weapon of the anti-Duterte elites. Written using narratives drawn from an auto-ethnographic account of this author, this paper first analyzes the academic and social media domains around which myths and representations about Rodrigo Duterte are produced. It concludes by drawing from the analysis the implications to ideological and discursive bases for the maintenance of political order in Philippine society, particularly on the role of leaders in the context of the country’s communitarian political culture.

Effect of Social Media Advertisements on Consumer Purchase Intentions

The purpose for developing this research paper was to get an in-depth understanding of the mechanisms, features, domains, etc. of social media advertisements, across social media platforms and how these specific things are influencing potential consumers in their buying behavior or in other words their purchase intention. The design of methodology is a mixture of exploratory research design and causal research design, (involving a questionnaire) as the study satisfies both mentioned research design protocols. The findings in terms of the responses (of the questionnaire) collected were intriguing as it depicted that some characteristics of social media advertisements had more impact in influencing their purchase intention, than some of the other. Consumers preferred certain attributes more than some other attributes pertaining to ads on their feeds. Thus, the study of their implications will provide valuable insights to any concerned party. The potential limitations of this research paper will be mainly that the respondents, demographic were limited to a certain figure thus a larger sample size, would make more sense in the future. Also, the practicability of developed hypothesis may vary according to circumstances, which act as a potential limitation. The practical implications of this study can act as a guideline to organizations looking to understand their consumer base, preferences over the social media domain, which could result in potentially building their brand equity.

Social Media, Work and Nonwork Interface: A Qualitative Inquiry

Flourishing social media, easy access to smartphones and tablets, and ready availability of the internet in the past decade have made it possible for people to be connected to social media almost anywhere at any time. In this qualitative study, we interviewed 41 individuals in multiple professions in the United Kingdom to examine the role of social media in how they navigate their personal and professional lives. We find social media to be a virtual domain that has boundaries with nonvirtual personal and professional domains. Focusing on spatial and temporal boundaries, our findings revealed four boundary transition modes employees used to switch between the social media domain and their work and nonwork domains: boundary switch avoidance; disciplined boundary switch; integrated boundary switch; and boundary switch addiction. We also describe 15 mechanisms through which engaging with social media platforms enriches or conflicts with individuals’ personal and professional lives. Our findings extend work‐nonwork scholarship and boundary theory to include virtual as well as nonvirtual domains.

Development of E-learning System Using Felder and Silverman’s Index of Learning Styles Model

The existing online education system mostly provides users with undistinguished learning content. The existing online education system assumes that users have the same characteristics or tendencies as one another. Every user is not the same; they have different characteristics, tendencies, and learning preferences. Each user has different personal characteristics, but today's online education systems ignore them. This study use Felder Silverman's learning style model. Through characteristic learning styles,relationships, and content types result in more personalized e-learning. Researchers distributed questionnaires to Informatics Engineering students at, University of August 17, Surabaya, and managed to get 277 student respondents. This data uses to determine student learning styles using a questionnaire-based psychometric instrument called the index of learning style (ILS), which uses to determine learning styles. The questionnaire, validity, reliability, determination of learning styles, learning content, and material requirements are testing. This study concludes that the questionnaire used meets the standards for determining learning styles for users. The system that builds has five modules to facilitate construction. These modules include the media library, domain repository, student model, instruction model, adaptive engine, and user interface. It can conclude that the modules made can help lecturers to distribute material according to the material content that is suitable for student learning styles and helps students to study more effectively according to their preferences

Sentiment Analysis System for Classification of Patient-Generated Health Reviews Using Rough Set Theory and Machine Learning Technique

Advancements in social media domain have led to a prominent progress in the number of online communities. Sites, such as Twitter and Facebook, provide an avenue for the unrestricted generation, communication, and distribution of messages as well as information. In this work, we propose a sentiment classification system from patient-generated content posted by users on medical forums and social media sites. The rough set theory is a numerical rule-based technique employed for categorizing and examining doubtful, partial or indistinct data. The emphasis of this study is on the employment of the rough set theory technique for sentiment classification of patient-generated health reviews. We investigated four rough set theory-based algorithms, namely: Genetic, Learning from Examples Module version 2 (LEM2), Exhaustive and Covering, to generate rules for sentiment classification of patient-generated health reviews text. The Rough Set Exploration System (RSES 2.0) software is utilized to conduct experiments. Additionally, we applied SVM classifier to classify emotions. The experimental results show that the Genetic algorithm outperforms the comparing algorithms with an accuracy of 84.2% and Support Vector Machine outperforms other classifiers with an accuracy of 80.5%.

5V’s of Big Data Attributes and their Relevance and Importance across Domains

“Data is an ocean of Universal Facts”. Big data once an emergent technology of study is in its prime with immense potential for future technological advancements. A formal study in the attributes of data is essential to build robust systems of the future. Data scientists need a basic foot hold when studying data systems and their applications in various domains. This paper intends to be THE go-to resource for every student and professional desirous to make an entry in the field of Big Data. This paper has two focus areas. The first area of focus is the detailing of the 5 V attributes of data i.e. Volume, Variety, Velocity, Veracity and Value. Secondly, we will endeavor to present a domain wise independent as well as comparative of the correlation between the 5 V’s of Big Data. We have researched and collected information from various market watch dogs and concluded by carrying out comparatives which are highlighted in this publication. The domains we will mention are Wholesale Trade Domain, Retail Domain, Utilities Domain, Education Domain, Transportation Domain, Banking and Securities Domain, Communication and Media Domain, Manufacturing Domain, Government Domain, Healthcare Domain, etc. This is invaluable information for Big Data system designers as well as future researchers.

Spectral intensity correlations of backscattered diffuse light: the effect of scattering anisotropy

In this work, we report experiments and a theoretical scheme of photon transport in the frequency domain of rigid turbid media. We have employed spectral multi-speckle intensity correlations to estimate optical properties as the transport mean free path and the absorption length of turbid systems. We propose a scheme based on the photon diffusion model using an effective path-length distribution in the backscattering configuration and take explicitly into account the particles scattering anisotropy parameter g. By studying rigid Teflon slabs and polymer matrices doped with polystyrene particles of different degrees of scattering anisotropy, we find that the proposed model adequately describes our experimental results. Our hypothesis for the diffuse transport of backscattered photons in the weak multiple scattering regime is further validated using a numerical simulation scheme of speckle dynamics, based on the Copula method.

FRIEND RECOMMENDATION USING GRAPH MINING ON SOCIAL MEDIA

Recommendation system is an important type of machine learning algorithm that provide precise suggestions to the users. Recommendation systems are used in innumerable types of areas such as generation of playlists, music and video services like Jio savaan, wynk, amazon prime music etc., and products recommendation for users in e-commerce applications and commercial applications. The recommendations that are provided by various types of applications increases the speed for identifying and makes easier to access the products that users are interested in. For each user, the recommendation system is capable of envisaging the future predilections on a set of items and recommend the top items. In several industries, recommendation systems are very useful as they generate huge amount of income and this type of industries can stand uniquely from competitors. Due to cumbersome number of items that each user can find in the web, the impact of recommendation system has been increased in the internet. Recommendation systems are used for custom-made navigation by getting huge amount of data particularly in social media domain for recommending friends. A recommendation system act as a subclass for the information filtering system that pursue to predict the rating. The similarity measures that are calculated in this research are Jaccard distance and Otsuka-Ochiai coefficient. The feature extractions that are used in this paper are Adar index, PageRank, Katz centrality, Hits score. Now a days many research people are implementing different types of algorithms in various domains for recommendation systems.

Learning representation from multiple media domains for enhanced event discovery

In this paper, we focus on event discovery by utilizing data distributed in multiple media domains, such as news media and social media. To this end, we propose an in-domain and cross-domain Laplacian regularization (ICLR) model, which can learn effective data representation for both textual news reports contributed by journalists in news media domain, and image posts shared by amateur users in social media domain. The achieved data representation can be used by classification and clustering strategies for existing and new event discovery, respectively. More specifically, ICLR constructs respective Laplacian regularization terms considering the property of inter-domain and intra-domain label consistency, which can be optimized by employing an alternating optimization strategy with theoretical guarantee for convergence. In particular, we collect and release a multi-domain and multimodal dataset for evaluations and public use.