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BM631 Research Methods.docx
BM631 Research Methods.docx
studywriters
This paper based on the research carried out in the area of data mining depends for managing bulk amount of data with mining in social media on using composite applications for performing more sophisticated analysis. Enhancement of social media may address this need. The objective of this paper is to introduce such type of tool which used in social network to characterised Medicine Usage. This paper outlined a structured approach to analyse social media in order to capture emerging trends in medicine abuse by applying powerful methods like Machine Learning. This paper describes how to fetch important data for analysis from social network. Then big data techniques to extract useful content for analysis are discussed. Sindhu S. B | Dr. B. N Veerappa "Social Media Datasets for Analysis and Modeling Drug Usage" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25246.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/25246/social-media-datasets-for-analysis-and-modeling-drug-usage/sindhu-s-b
Social Media Datasets for Analysis and Modeling Drug Usage
Social Media Datasets for Analysis and Modeling Drug Usage
ijtsrd
The exploration of social conversations for addressing patient’s needs is an important analytical task in which many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in order to generate insight from social media data, which can be considered as one of the most challenging source of information available today due to its sheer volume and noise. This study is based on the work by Scott Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The mechanism for automatically identifying and generating a taxonomy from social conversations is developed and pressured tested using public data from media sites focused on the needs of cancer patients and their families. Moreover, a novel method for generating the category’s label and the determination of an optimal number of categories is presented which extends Scott and Jeffrey’s research in a meaningful way. We assume the reader is familiar with taxonomies, what they are and how they are used.
Identifying Structures in Social Conversations in NSCLC Patients through the ...
Identifying Structures in Social Conversations in NSCLC Patients through the ...
IJERA Editor
The exploration of social conversations for addressing patient’s needs is an important analytical task in which many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in order to generate insight from social media data, which can be considered as one of the most challenging source of information available today due to its sheer volume and noise. This study is based on the work by Scott Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The mechanism for automatically identifying and generating a taxonomy from social conversations is developed and pressured tested using public data from media sites focused on the needs of cancer patients and their families. Moreover, a novel method for generating the category’s label and the determination of an optimal number of categories is presented which extends Scott and Jeffrey’s research in a meaningful way. We assume the reader is familiar with taxonomies, what they are and how they are used.
Identifying Structures in Social Conversations in NSCLC Patients through the ...
Identifying Structures in Social Conversations in NSCLC Patients through the ...
IJERA Editor
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media Emre Kıcıman Microsoft Research [email protected] Matthew Richardson Microsoft Research [email protected] ABSTRACT Every day, people take actions, trying to achieve their per- sonal, high-order goals. People decide what actions to take based on their personal experience, knowledge and gut in- stinct. While this leads to positive outcomes for some peo- ple, many others do not have the necessary experience, knowl- edge and instinct to make good decisions. What if, rather than making decisions based solely on their own personal experience, people could take advantage of the reported ex- periences of hundreds of millions of other people? In this paper, we investigate the feasibility of mining the relationship between actions and their outcomes from the aggregated timelines of individuals posting experiential mi- croblog reports. Our contributions include an architecture for extracting action-outcome relationships from social me- dia data, techniques for identifying experiential social media messages and converting them to event timelines, and an analysis and evaluation of action-outcome extraction in case studies. 1. INTRODUCTION While current structured knowledge bases (e.g., Freebase) contain a sizeable collection of information about entities, from celebrities and locations to concepts and common ob- jects, there is a class of knowledge that has minimal cov- erage: actions. Simple information about common actions, such as the effect of eating pasta before running a marathon, or the consequences of adopting a puppy, are missing. While some of this information may be found within the free text of Wikipedia articles, the lack of a structured or semi-structured representation make it largely unavailable for computational usage. With computing devices continuing to become more embedded in our everyday lives, and mediating an increasing degree of our interactions with both the digital and physical world, knowledge bases that can enable our computing de- vices to represent and evaluate actions and their likely out- comes can help individuals reason about actions and their Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected] KDD’15, August 10-13, 2015, Sydney, NSW, Australia. Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM 978-1-4503-3664-2/15/08 ...$15.00. DOI: http://dx.doi.org/10.1145 ...
Towards Decision Support and Goal AchievementIdentifying Ac.docx
Towards Decision Support and Goal AchievementIdentifying Ac.docx
turveycharlyn
Social networking sites are a significant source of information to know the behavior of users and to know what is occupying society of all ages and accordingly helpful information can be provided to specialists and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The study and analysis of social media data are done to provide the necessary information to increase investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people occupy on the communication sites through their tweets about the labor market and investment. Given the huge volume of data and also its randomness, a survey of the data will be done and collected from through keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study analysis and conclusion will be based on data-mining and its techniques of analysis and deduction .
Increasing the Investment’s Opportunities in Kingdom of Saudi Arabia By Study...
Increasing the Investment’s Opportunities in Kingdom of Saudi Arabia By Study...
AIRCC Publishing Corporation
Social networking sites are a significant source of information to know the behavior of users and to know what is occupying society of all ages and accordingly helpful information can be provided to specialists and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The study and analysis of social media data are done to provide the necessary information to increase investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people occupy on the communication sites through their tweets about the labor market and investment. Given the huge volume of data and also its randomness, a survey of the data will be done and collected from through keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study analysis and conclusion will be based on data-mining and its techniques of analysis and deduction.
INCREASING THE INVESTMENT’S OPPORTUNITIES IN KINGDOM OF SAUDI ARABIA BY STUDY...
INCREASING THE INVESTMENT’S OPPORTUNITIES IN KINGDOM OF SAUDI ARABIA BY STUDY...
ijcsit
https://www.irjet.net/archives/V9/i1/IRJET-V9I1223.pdf
Detection of Fake News Using Machine Learning
Detection of Fake News Using Machine Learning
IRJET Journal
Recommended
course material
BM631 Research Methods.docx
BM631 Research Methods.docx
studywriters
This paper based on the research carried out in the area of data mining depends for managing bulk amount of data with mining in social media on using composite applications for performing more sophisticated analysis. Enhancement of social media may address this need. The objective of this paper is to introduce such type of tool which used in social network to characterised Medicine Usage. This paper outlined a structured approach to analyse social media in order to capture emerging trends in medicine abuse by applying powerful methods like Machine Learning. This paper describes how to fetch important data for analysis from social network. Then big data techniques to extract useful content for analysis are discussed. Sindhu S. B | Dr. B. N Veerappa "Social Media Datasets for Analysis and Modeling Drug Usage" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25246.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/25246/social-media-datasets-for-analysis-and-modeling-drug-usage/sindhu-s-b
Social Media Datasets for Analysis and Modeling Drug Usage
Social Media Datasets for Analysis and Modeling Drug Usage
ijtsrd
The exploration of social conversations for addressing patient’s needs is an important analytical task in which many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in order to generate insight from social media data, which can be considered as one of the most challenging source of information available today due to its sheer volume and noise. This study is based on the work by Scott Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The mechanism for automatically identifying and generating a taxonomy from social conversations is developed and pressured tested using public data from media sites focused on the needs of cancer patients and their families. Moreover, a novel method for generating the category’s label and the determination of an optimal number of categories is presented which extends Scott and Jeffrey’s research in a meaningful way. We assume the reader is familiar with taxonomies, what they are and how they are used.
Identifying Structures in Social Conversations in NSCLC Patients through the ...
Identifying Structures in Social Conversations in NSCLC Patients through the ...
IJERA Editor
The exploration of social conversations for addressing patient’s needs is an important analytical task in which many scholarly publications are contributing to fill the knowledge gap in this area. The main difficulty remains the inability to turn such contributions into pragmatic processes the pharmaceutical industry can leverage in order to generate insight from social media data, which can be considered as one of the most challenging source of information available today due to its sheer volume and noise. This study is based on the work by Scott Spangler and Jeffrey Kreulen and applies it to identify structure in social media through the extraction of a topical taxonomy able to capture the latent knowledge in social conversations in health-related sites. The mechanism for automatically identifying and generating a taxonomy from social conversations is developed and pressured tested using public data from media sites focused on the needs of cancer patients and their families. Moreover, a novel method for generating the category’s label and the determination of an optimal number of categories is presented which extends Scott and Jeffrey’s research in a meaningful way. We assume the reader is familiar with taxonomies, what they are and how they are used.
Identifying Structures in Social Conversations in NSCLC Patients through the ...
Identifying Structures in Social Conversations in NSCLC Patients through the ...
IJERA Editor
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media Emre Kıcıman Microsoft Research [email protected] Matthew Richardson Microsoft Research [email protected] ABSTRACT Every day, people take actions, trying to achieve their per- sonal, high-order goals. People decide what actions to take based on their personal experience, knowledge and gut in- stinct. While this leads to positive outcomes for some peo- ple, many others do not have the necessary experience, knowl- edge and instinct to make good decisions. What if, rather than making decisions based solely on their own personal experience, people could take advantage of the reported ex- periences of hundreds of millions of other people? In this paper, we investigate the feasibility of mining the relationship between actions and their outcomes from the aggregated timelines of individuals posting experiential mi- croblog reports. Our contributions include an architecture for extracting action-outcome relationships from social me- dia data, techniques for identifying experiential social media messages and converting them to event timelines, and an analysis and evaluation of action-outcome extraction in case studies. 1. INTRODUCTION While current structured knowledge bases (e.g., Freebase) contain a sizeable collection of information about entities, from celebrities and locations to concepts and common ob- jects, there is a class of knowledge that has minimal cov- erage: actions. Simple information about common actions, such as the effect of eating pasta before running a marathon, or the consequences of adopting a puppy, are missing. While some of this information may be found within the free text of Wikipedia articles, the lack of a structured or semi-structured representation make it largely unavailable for computational usage. With computing devices continuing to become more embedded in our everyday lives, and mediating an increasing degree of our interactions with both the digital and physical world, knowledge bases that can enable our computing de- vices to represent and evaluate actions and their likely out- comes can help individuals reason about actions and their Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected] KDD’15, August 10-13, 2015, Sydney, NSW, Australia. Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM 978-1-4503-3664-2/15/08 ...$15.00. DOI: http://dx.doi.org/10.1145 ...
Towards Decision Support and Goal AchievementIdentifying Ac.docx
Towards Decision Support and Goal AchievementIdentifying Ac.docx
turveycharlyn
Social networking sites are a significant source of information to know the behavior of users and to know what is occupying society of all ages and accordingly helpful information can be provided to specialists and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The study and analysis of social media data are done to provide the necessary information to increase investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people occupy on the communication sites through their tweets about the labor market and investment. Given the huge volume of data and also its randomness, a survey of the data will be done and collected from through keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study analysis and conclusion will be based on data-mining and its techniques of analysis and deduction .
Increasing the Investment’s Opportunities in Kingdom of Saudi Arabia By Study...
Increasing the Investment’s Opportunities in Kingdom of Saudi Arabia By Study...
AIRCC Publishing Corporation
Social networking sites are a significant source of information to know the behavior of users and to know what is occupying society of all ages and accordingly helpful information can be provided to specialists and decision-makers. According to official sources, 98.43% of Saudi youth use social networking sites. The study and analysis of social media data are done to provide the necessary information to increase investment opportunities within the Kingdom of Saudi Arabia, by studying and analyzing what people occupy on the communication sites through their tweets about the labor market and investment. Given the huge volume of data and also its randomness, a survey of the data will be done and collected from through keywords, the priority of arranging the data, and recording it as (positive - negative - mixed). The study analysis and conclusion will be based on data-mining and its techniques of analysis and deduction.
INCREASING THE INVESTMENT’S OPPORTUNITIES IN KINGDOM OF SAUDI ARABIA BY STUDY...
INCREASING THE INVESTMENT’S OPPORTUNITIES IN KINGDOM OF SAUDI ARABIA BY STUDY...
ijcsit
https://www.irjet.net/archives/V9/i1/IRJET-V9I1223.pdf
Detection of Fake News Using Machine Learning
Detection of Fake News Using Machine Learning
IRJET Journal
The use-of-social-media-in-the-recruitment-process
The use-of-social-media-in-the-recruitment-process
The use-of-social-media-in-the-recruitment-process
Preeti Bhaskar
The use of social media in the recruitment process
The use of social media in the recruitment process
Bhagyashree Zope
Pano nga ba ang pag gawa ng research methodology
Mukha ng research methodology
Mukha ng research methodology
GAMALI Roper
Social Media: In the Work Place and Patterns of Usage Trevor Nesbit, University of Canterbury, Canterbury, New Zealand Abstract: As the adoption of social media increases, a number of important themes have emerged. The two main themes that are investigated in this study are the perceived benefits and risks of using social media in theworkplace;and thepatternsofusageof socialmedia.The themeof theperceivedbenefits and risks of using social media in the workplace is investigated through a literature review and a survey of third year commerce students about their perceptions. The pattern of usage theme is also explored through the same survey of a group of third year commerce students. The analysis and dis- cussion of the results from the survey highlighted a number of interesting issues connected to the two themes. The two main issues relating to the perceived benefits and risks of using social media in the work placeare firstly, that use of socialmedia tools to enhanceemployeeretention is not seen as being important by the group of respondents in this study in comparison with other benefits identified in the literature; and secondly, that the reduction of trust in an organisation and incompatibility with organ- isational culture are not seen as being amongst the significant risks and challenges when using social media in the work place by the group of respondents in the study. The three main issues relating to the patterns of usage theme include that Facebook is the most frequently used social media tool by the students surveyed who were under the age of 30; that there is potentially a difference between the genders in the frequency with which Wikis are used; and that defining what constitutes frequent use of one social media tool may be different to what constitutes frequent use of another social media tool. Other issuesraised in this study includesocialmediaasanappropriatemarketing tool toreachpeople under the age of 30 (and potentially other age groups), and has potential to be used as part of educa- tional programmes, however some care would need to be taken over the choice of social media tool. Keywords: Social Media, Work Place Introduction THE PURPOSE OF this paper is to investigate the use of social media by exploringtwo themes. The first theme relates to the perceived benefits and risks of using socialmedia in the workplace and is carried out by an investigation of the literature relating to the use of social media in the work place and through a survey of a group of third year commerce students at the University of Canterbury. The second theme relates to patterns of usage and is explored using the same survey of third year commerce students. A number of pieces of literature are reviewed and concepts are identified which are then analysed and discussed to identify a number of benefits pertaining to the use of social media in the work place, as well as the risks and challenges of using social media in the work place. The results o.
Social Media In the Work Place and Patterns of UsageTrevor .docx
Social Media In the Work Place and Patterns of UsageTrevor .docx
jensgosney
Paper Writing Service - HelpWriting.net 👈
Sample Methodology Essay
Sample Methodology Essay
Custom Papers Texas A&M University-Kingsville
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A_Comparison_of_Manual_and_Computational_Thematic_Analyses.pdf
A_Comparison_of_Manual_and_Computational_Thematic_Analyses.pdf
LandingJatta1
Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data.
Predicting user behavior using data profiling and hidden Markov model
Predicting user behavior using data profiling and hidden Markov model
IJECEIAES
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Research Evaluation And Data Collection Methods
Research Evaluation And Data Collection Methods
Jessica Robles
Week 8: Quantitative Research Design Previous Next Instructions For this assignment, you will build on your assignment last week to further explore how you might examine your research problem using a quantitative methodology. Respond to the following questions: · Please restate the research problem, purpose, and research questions you developed previously and incorporate any faculty feedback as appropriate. This week, be sure to also include hypotheses for each of your research questions. · How might surveys be used to answer your research questions? What are the advantages and disadvantages of using surveys to collect data? · How might you use an experiment or quasi-experiment to answer your research questions? What are the advantages and disadvantages of using (quasi)experiments to collect your data? · It is also important to consider how you might analyze the potential data you collect and factors that could affect those analyses. Specifically, what are Type I and Type II errors? How might these impact your study? What is statistical power? How might this impact your study? What steps can you take ahead of time to help avoid issues related to Type I & II errors as well as power? Be sure to use scholarly sources to support all assertions and research decisions. Length: 5 to 7 pages, not including title and reference pages Grading Rubric Criteria Content (4 points) Points 1 State research problem, purpose, research questions and hypotheses 2 2 Discussed in detail the advantages and disadvantages of using surveys to collect data 1 3 Explained how you could use experiments or quasi-experiments to collect data for your study and the advantages and disadvantages of these designs 1 Organization (1 point) 4 Organized and presented in a clear manner. Included a minimum of five scholarly references, with appropriate APA formatting applied to citations and paraphrasing. 1 Total 5 Your paper should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards. Be sure to adhere to Northcentral University’s Academic Integrity Policy. Upload your document and click the Submit to Dropbox button. Running head: Numerical Data Numerical Data 2 Assignment: Numerical Data Shameka Jester February 18, 2018 Northcentral University Violations of individual rights have been a major issue in today’s society. Numerous stakeholders are fighting for social justice of persons, as well as protection of their individual rights. A key and fundamental right that has increasingly been violated is right to privacy, especially in the wake of the rapid advancement in technology (Grumbling, 2016). Although legislation has been established to address t.
Week 8 Quantitative Research DesignPrevious Next Instructio.docx
Week 8 Quantitative Research DesignPrevious Next Instructio.docx
philipnelson29183
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Presentation to the University of Cape Town Emerging Researchers Programme by Michelle Willmers, Project Manager: OpenUCT Initiative (5 June 2014)
Altmetrics, Impact Analysis and Scholarly Communication