Use Of Online Data Research Articles

What makes anglers happy: A sentiment analysis of Walleye angler forums in the United States

AbstractObjectiveWe hypothesized online text can provide insight into the satisfaction of anglers who post on online forums.MethodsWe applied sentiment analysis to almost 3 years of management‐related posts on online angling forums to estimate the long‐term satisfaction of Walleye Sander vitreus anglers in nine U.S. states.ResultSentiment varied fourfold among states. Consistent with the literature, our random forest model found that sentiment increased with angler density, bag limit, and season length. Relatively unimportant factors were fishing license cost, the frequency of special Walleye regulations, the degree to which management agencies communicated with anglers through social media, and the importance of angling to the state economy.ConclusionOur results support the hypothesis that online text can provide insight into the satisfaction of anglers who post on online forums. This work contributes to a growing interest in exploring and validating the use of online data within systems of harvest management and governance.

Depressive symptoms and psychological pain experienced by Polish adults in the context of both the war in Ukraine and the COVID-19 pandemic.

This study sought to investigate the perceived influence of stressful events (i.e., the outbreak of the war in Ukraine, the political situation, and the COVID-19 pandemic) on the depressive symptoms and psychological pain experienced by Polish adults. The study also aimed to explore the association between experiential avoidance and emotional distress. 110 Polish social media users aged 18-89 completed online the Patient Health Questionnaire-9, Psychache Scale and the Multidimensional Experiential Avoidance Questionnaire-30 and questions concerning perceived influence of stressful event on their emotional functioning. More than half of the participants (52.7%) reported experiencing a subjective increase in their depressive symptoms since the outbreak of the war in Ukraine. Interestingly, a larger percentage of participants associated their emotional distress with the outbreak of the war (44.6%) and the overall political situation (39.2%) than with the COVID-19 pandemic (27.2%). In addition, experiential avoidance was found to be associated with subjective increases in the participants' depressive symptoms and emotional distress. Cross-sectional character of the study, small sample size, and exclusive use of online data are main limitation of the study. Findings of our study can help planning preventive and therapeutic interventions for at-risk individuals to reduce negative long-term outcomes of the recent stressful life events in Poland.

The Use of Online Data Sources in a Qualitative Analysis Learning Project

Our purpose in writing this article is to describe the use of online data sources (such as blogs and microblogs) in a qualitative analysis learning project for graduate occupational therapy students. The project was designed to meet the following learning objectives: (1) increase students’ understanding and appreciation for qualitative research principles and methods, (2) increase students’ ability to use thematic and narrative analysis procedures with authentic data sets, and (3) increase students’ ability to apply qualitative findings to occupational therapy practice. This article describes the project’s theoretical rationale, components, objectives, implementation, and informal outcomes, along with a discussion of strengths and limitations of this project and suggestions for future research. This project demonstrates one way in which publicly available online data sources can be used to create an effective graduate qualitative analysis learning activity. We are sharing this innovative learning project in the hopes that it may be of interest to our colleagues in higher education and may contribute to the ethical and scholarly use of online data in learning assignments.

Efektivitas Penggunaan Media Daring SIAKAD Terhadap Pemenuhan Kebutuhan Informasi Akademik di Kalangan Mahasiswa Universitas Islam Makassar

The rapid development of technology encourages Makassar Islamic University to make changes in the academic information system, which was originally done manually / traditionally to online-based which can be accessed anywhere and anytime by students to meet the need for academic information. Various problems occur in the implementation of the online-based information system, so the assumptions that arise regarding how the effectiveness of the use of SIAKAD Online Media to meet the academic information needs of Makassar Islamic University students are the challenges of this research.In this study a quantitative method is used which aims to determine the effect of using online SIAKAD in meeting academic information needs among students. Respondents in this study were drawn from 2017 class year students from 8 faculties totaling 1,194 people. The samples chosen in this study were 299 people with 5% specimens. The research data were obtained through a survey of respondents with Kearis distribution. Then processing and data analysis is done with a simple linear regression analysis using the help of SPSS version 25. The results of this study indicate that there is effectiveness of the use of online data to meet the needs of academic information of students assessed significantly adjusted Rsquare results of 0.305% variation of the dependent variable, namely the fulfillment of needs. This means that 30.5% of the variable fulfillment of needs is the contribution of the variable use of online data

Social Media, Research, and Ethics: Does Participant Willingness Matter?

Summary Objective : To summarise the state of the art published in 2019 in consumer health informatics and education, with a special emphasis on “Ethics and Health Informatics”. Methods : We conducted a systematic search of articles published in PubMed using a predefined set of queries, which identified 368 potential articles for review. These articles were screened according to topic relevance and 15 were selected for consideration of best paper candidates, which were then presented to a panel of international experts for full paper review and scoring. The top five papers according to the external reviewers’ ranking were discussed in a consensus meeting. Finally, the paper that received the highest score from four of the five experts was selected as the best paper on social media and ethics for patients and consumers of the year 2019. Results : Despite using the terms “ethics” and “ethical” in the search query, we retrieved very few articles. The bibliometric analysis identified three major clusters centred on “social”, “health”, and “study”. Among the top five papers, one was a review where the authors identified ethical issues across four areas at the intersection of social media and health: 1) the impact of social networking sites on the doctor-patient relationship; 2) the development of e-health platforms to deliver care; 3) the use of online data and algorithms to inform health research; and 4) the broader public health consequences of widespread social media use. The other papers highlighted ethical concerns in using social media to interact with patients at different phases of a clinical research protocol, such as recruitment phase, participant engagement, data linkage, and detection and monitoring of adverse events. Conclusions : Findings suggest that most users do not think that using social media for patient monitoring in clinical research, for example using Twitter for clinical trial recruitment, constitutes inappropriate surveillance or a violation of privacy. However, further research is needed to identify whether and how views on ethical concerns differed between social media platforms and across populations.

Trails of Data: Three Cases for Collecting Web Information for Social Science Research

As the availability of online data grows rapidly, researchers are confronted with a pressing question: How should social scientists collect Internet data for research? This study focuses on one of the most commonly used data collection techniques: web scraping. Going beyond canned approaches by leveraging a general framework of data communication, this study illustrates how online information can be systematically queried and fetched for reproducible research. To generalize our approaches, we additionally explore the variations in site security and architecture that analysts may encounter during the scraping process before they are given access to the desired data. The approaches we introduce do not rely on any proprietary software and can be easily implemented on any computing platform with programming languages such as Python or R. The methodological discussion in this study is meant to be applicable to current web-based research efforts. We include three examples with complete Python implementation. We also present an integrated workflow that enables researchers to produce analytical data sets that are traceable and thus verifiable for analysis or replication. Lastly, options related to the validity and efficiency of data are discussed, and we highlight the ongoing debate surrounding the ethics of online data collection, ultimately advocating for the fair use of online data.

Predicting referendum results in the Big Data Era

In addressing the challenge of Big Data Analytics, what has been of notable significance is the analysis of online search traffic data in order to analyze and predict human behavior. Over the last decade, since the establishment of the most popular such tool, Google Trends, the use of online data has been proven valuable in various research fields, including -but not limited to- medicine, economics, politics, the environment, and behavior. In the field of politics, given the inability of poll agencies to always well approximate voting intentions and results over the past years, what is imperative is to find new methods of predicting elections and referendum outcomes. This paper aims at presenting a methodology of predicting referendum results using Google Trends; a method applied and verified in six separate occasions: the 2014 Scottish Referendum, the 2015 Greek Referendum, the 2016 UK Referendum, the 2016 Hungarian Referendum, the 2016 Italian Referendum, and the 2017 Turkish Referendum. Said referendums were of importance for the respective country and the EU as well, and received wide international attention. Google Trends has been empirically verified to be a tool that can accurately measure behavioral changes as it takes into account the users’ revealed and not the stated preferences. Thus we argue that, in the time of intelligence excess, Google Trends can well address the analysis of social changes that the internet brings.

Social Media, E-Health, and Medical Ethics.

Given the profound influence of social media and emerging evidence of its effects on human behavior and health, bioethicists have an important role to play in the development of professional standards of conduct for health professionals using social media and in the design of online systems themselves. In short, social media is a bioethics issue that has serious implications for medical practice, research, and public health. Here, we inventory several ethical issues across four areas at the intersection of social media and health: the impact of social networking sites on the doctor-patient relationship, the development of e-health platforms to deliver care, the use of online data and algorithms to inform health research, and the broader public health consequences of widespread social media use. In doing so, we review discussions of these topics and emphasize the need for bioethics to focus more deeply on the ways online technology platforms are designed and implemented. We argue that bioethicists should turn their attention to the ways in which consumer engagement, bias, and profit maximization shape online content and, consequently, human behavior and health. We also offer a set of recommendations and suggest future directions for addressing ethical challenges in these domains.

What is the value of foreign work experience for young return migrants?

PurposeThe purpose of this paper is to understand the value of foreign work experience for young migrants after their return to the home country labour market and their labour market preferences relative to stayers.Design/methodology/approachThe authors analyse the labour market integration patterns of young return migrants in Slovakia. After reconstructing the life histories of young people from online CVs, a set of regression models investigates the attractiveness, salary expectations and positions of interest to returnees in comparison to stayers.FindingsPost-accession foreign work experience increases the attractiveness of job candidates. Foreign work experience changes the expectations of returnees with respect to wages and widens their perspective on the location of future work. In the underperforming labour market, migration experience signals to employers a set of skills that differentiate young returnees from young stayers in a positive way.Research limitations/implicationsWhile the web data are not representative, it allows the authors to study return migration from a perspective that large representative data sets do not allow.Social implicationsForeign work experience is, in general, an asset for (re)integration into the home labour market, but the higher salary demands of returnees might hinder the process in a less-skilled segment of the labour market.Originality/valueReturn migration is a relatively underresearched area, and knowledge about the perception of returnees among employers and the labour market preferences of returnees is relatively limited. Another contribution lies in the use of online data to analyse return migration from the perspective of both labour demand and supply.

Beyond identifying privacy issues in e-learning settings – Implications for instructional designers

Players in the digital economy increasingly rely on the large-scale collection or exchange of personal data. Because online data can be persistent and immense, e-learning researchers and educators should advise people to make constructive use of online data and to disclose the data with caution. However, few studies have indicated that data protection issues are particular concerns in e-learning practices. Drawing on a systematic literature review, this paper examines personal data protection issues related to instructional design and e-learning. We aim to understand the online data protection-related issues that instructors or instructional designers encounter rather than reiterating that users must be aware of the informative and persistent characteristics of online data. The themes that emerged from the literature review can be classified into two typologies. One typology refers to the identification of privacy issues as a particular concern in the instructional design of e-learning, and the second typology is the implementation of data protection as a subject matter in the pedagogical design for e-learning. The results of the review and their implications for further research are then discussed.

A Mixed Methods Research Approach to Exploring Teacher Participation in an Online Social Networking Website

Social networks are becoming very prominent in educational discourse. The discourse centers on the role of social media and its utility in the teaching and learning environment. Several arguments have been made to support its use because they are highly collaborative, easily accessible, and provide opportunities to embed a variety of Web 2.0 tools such as blogs, wikis, and online chats. However, there is a dearth in research on teacher use of educational social networking sites (SNS) in Trinidad and Tobago. The purpose of this study was to explore how 35 secondary school teachers in Trinidad and Tobago participated in an educational online social networking site. This study utilized a mixed methods exploratory approach and allowed for the use of online data capture together with questionnaires and interviews. Combined data analysis revealed five different levels of site participation, with most teachers adopting the role of content consumer rather than of content producer. Barriers to participation were time, motivation, technology, and usability. The designed social networking site allowed teachers to make public their discourse on their practice while connecting with colleagues from other schools. The study was qualitative dominant, with the mixed methods research approach allowing for deeper exploration of teachers’ participation on this social networking website, and was justified by the newness and transient nature of data from social networking websites.

A REVIEW ON ANONYMIZATION TECHNIQUES FOR PRIVACY PRESERVING DATA PUBLISHING

With the increased and vast use of online data, privacy in data publishing has now become very important issue. Now days, many organizations are collecting and storing huge volumes of data in large databases. Data publisher collected data from data holders then data publisher released this data to data recipient for research analysis and mining purpose. The released data reveals some information, which is considered to be private and personal. Privacy of information in such scenario becomes subject of research. In recent years, data anonymization techniques become subject of research. In this paper, we provide a review of the statistical Anonymization techniques that can be used for preserving privacy of publish data. Microdata publishing consist of variety of different anonymization methods like Generalization, Bucketization and Suppression. But for high dimensional data generalization loses significant amount of data and affected from curse of dimensionality. Whereas, bucketization fails to preserve membership disclosure and it needs a clear difference between quasi attributes and Sensitive attributes. Suppression reduces quality of data drastically. To deal with these problems, slicing method is introduced. Slicing is new anonymization technique for preserving publish data. In this data is partitioning horizontally as well as vertically. Dimensionality of the data is decreased by slicing. Utility is preserved & correlations between attributes which are highlycorrelated together are maintained. Compare to generalization, slicing provides better utility of data. Compare with bucketization, slicing is more effective. High-dimensional data can be handled by slicing. Slicing also Provides attribute disclosure and membership disclosure.

“Collection Bias” and the Importance of Natural History Collections in Species Habitat Modeling: A Case Study UsingThoracophorus costalisErichson (Coleoptera: Staphylinidae: Osoriinae), with a Critique of GBIF.org

When attempting to understand a species' distribution, knowing how many collections should be surveyed to achieve an adequate sample (exhaustiveness) is important. A test for exhaustiveness using species distribution models created with Diva-GIS was performed on county level locality information recorded from more than 4,900 specimens of Thoracophorus costalis Erichson (Staphylinidae: Osoriinae) borrowed from 38 collections. Size and location of distribution models based on specimens from single collections varied greatly, indicating “collection bias.” At least 15 collections needed to be combined before the resultant model averaged 90% of the area of a reference model created from all available specimens. By themselves, alternative distribution data from literature, Bugguide.net, and GBIF.org performed poorly, resulting in models with less than 15% the area of the reference model. Comments on the use of online data, the importance of maintaining and growing regional collections, and the future of natural history collections are included.

Online ethnography: a study of software developers and software development

Purpose– Qualitative research has made important contributions to social science by enabling researchers to engage with people and get an in-depth understanding of their views, beliefs and perceptions about social phenomena. With new and electronically mediated forms of human interaction (e.g. the online world), there are new opportunities for researchers to gather data and participate with or observe people in online groups. The purpose of this paper is to present features, challenges and possibilities for online ethnography as an innovative form of qualitative research.Design/methodology/approach– Ethnography is about telling a story about what happens in a particular setting or settings. In order to do this online, it is important to revisit, adopt and adapt some ideas about traditional (offline) ethnography. The paper distinguishes online ethnography from other types of research. It draws some generic features of online ethnography and identifies challenges for it. With these ideas in mind the paper presents and provides a reflection of an online ethnography of software developers.Findings– Online ethnography can provide valuable insights about social phenomena. The paper identifies generic features of this approach and a number of challenges related to its practice. These challenges have to do with to the choice of settings, use of online data for research, representation of people and generation of valuable and useful knowledge. The paper also highlights issues for future consideration in research and practice.Practical implications– The ethnography helped the researcher to identify and address a number of methodological challenges in practice and position herself in relation to relevant audiences she wanted to speak to. The paper also suggests different orientations to online ethnography. Lessons learned highlight potential contributions as well as further possibilities for qualitative research in the online world.Originality/value– Online ethnography offers possibilities to engage with a global audience of research subjects. For academics and practitioners the paper opens up possibilities to use online tools for research and it shows that the use of these tools can help overcome difficulties in access and interaction with people and to study a diversity of research topics, not only those that exist online. The paper offers guidance for researchers about where to start and how to proceed if they want to conduct online ethnography and generate useful and valuable knowledge in their area of interest.

Online medication history retrieval.

The difficulty of obtaining accurate medication history from inmates at the time of incarceration is daunting. This article summarizes the success of a large urban jail in the use of online data to identify medication history upon incarceration. This article describes the scope of available prescription data, the implementation of online retrieval, system limitations, planned improvements, and suggestions of additional applications of online retrieval services.

Informed, Uninformed and Participative Consent in Social Media Research

The use of online data is becoming increasingly essential for the generation of insight in today's research environment. This reflects the much wider range of data available online and the key role that social media now plays in interpersonal communication. However, the process of gaining permission to use social media data for research purposes creates a number of significant issues when considering compatibility with professional ethics guidelines. This paper critically explores the application of existing informed consent policies to social media research and compares with the form of consent gained by the social networks themselves, which we label ‘uninformed consent’. We argue that, as currently constructed, informed consent carries assumptions about the nature of privacy that are not consistent with the way that consumers behave in an online environment. On the other hand, uninformed consent relies on asymmetric relationships that are unlikely to succeed in an environment based on co-creation of value. The paper highlights the ethical ambiguity created by current approaches for gaining customer consent, and proposes a new conceptual framework based on participative consent that allows for greater alignment between consumer privacy and ethical concerns.

Data not seen: The uses and shortcomings of social media metrics

This paper looks at the use of online data, especially social media metrics, to assess media audiences, with particular focus on musicians. It shows how audiences are defined by different sets of people, and grounds the use of social media to understand audiences in the history of mass media audience measurement. The second half of the paper focuses on visible social media metrics — likes, followers, and other such counts — outlining their appeal as measures and highlighting their fallibility and ambiguity. Throughout, the paper argues that different people construct information systems to collect, store, analyze, and interpret data, and that these are shaped by value systems. Metric and big data analysis generally serves economic values, while other approaches to data may be more appropriate for assessing social and personal values.

Addressing the policy challenges and opportunities of “Big data”

Addressing the policy challenges and opportunities of “Big data”

Stabilisation operationalised: Using time series analysis to understand the dynamics of research collaboration

The aim of the paper is to investigate the use of online data and time series analysis, in order to study the dynamics of new types of research collaboration in a systematic way. Two international research teams were studied for more than 3 years, and quantitative data about their internet use together with observation of their collaboration patterns were gathered. Time series analysis (ARIMA modelling) was performed on their use of internet, and specific types of models related to specific ways of conducting research at a distance. The paper proposes the use of online data and ARIMA models to identify the stabilisation of a complex system, such as a research team, and investigate everyday research practices.

A new adaptive controller of facts-based FMRLC aimed at improving power system stability

Various control techniques using Advanced Super-conducting Magnetic Energy Storage (ASMES) aimed at improving power system stability have been proposed. As fuzzy controller has proved its value in some applications, the number of investigations employing fuzzy controller with ASMES has been greatly increased over recent period. Nevertheless, it is sometimes difficult to specify the rule base for some plants, or the need can arise for tuning the rule-base parameters if the plant changes. In order to solve such problems the Fuzzy Model Reference Learning Controller (FMRLC) is proposed. This paper investigates multi-inputs/multi-outputs FMRLC for time-variant nonlinear system. This provides the motivation for adaptive fuzzy control, whereby the focus is placed on the automatic on-line synthesis and tuning of fuzzy controller parameters (i.e., the use of on-line data for continuous learning of the fuzzy controller which ensures that the performance objectives are met). The simulation results show that the proposed robust controller is able to work with nonlinear power system (i.e., single machine connected at infinite bus), under various fault conditions and significant disturbances.