Yo, what it is! You know what it is, it’s your man Kingmusa— and welcome to The Study Guide! I'm here to break down today's class notes and help us learn together. Today we are going over Big Data & Machine Learning and we will be focusing on Module 2: Big Data & Machine Learning" Let's dive into our module on Big Data & Machine Learning. We're exploring the relationship between big data and machine learning and their importance in data-intensive applications.
Key Concept of the Day:
Today, we're focusing on understanding big data, which is the vast amount of data that traditional storage methods cannot handle, and machine learning, the ability of computer systems to learn from data to make predictions. We'll also touch on how these two concepts are interconnected and crucial for generating valuable business insights.Big Data, a computing paradigm, uses large data volumes for applications, analytics, and machine learning. Machine Learning, a branch of AI, enables systems to learn from data and improve without explicit programming. Big Data Analytics derives intelligence from data by consuming, curating, organizing, and reading structured and unstructured data for decision-making.
Machine Learning systems learn from data to make automated decisions. Artificial Intelligence simulates human intelligence in machines that think and act like humans. Cloud computing enables big data analytics and machine learning through scalable infrastructure and automated data processing. Distributed cloud environments optimize performance and scalability, eliminating bottlenecks and data silos. High-performance computing (HPC) cloud technology powers advanced applications like machine learning with optimized hardware, automated processing, and scalability. WekaIO platformA platform for big data and machine learning applications, offering fast cloud file systems and high-performance GPUDirect connectivity. Data security includes in-flight and at-rest encryption for GRC requirements. Data management provides agile access and management for edge, core, and cloud development. Scalability supports exabytes of storage across billions of files. Machine learning definition tools and technology derive meaning from data. Machine learning value automatically adapts to data patterns, surpassing human capabilities.
Machine learning applications are widely used in photo tagging, video recommendations, Google search, image recognition, fraud detection, recommendation systems, text and speech systems, diabetic retinopathy and skin cancer detection, retail, and transportation. Machine learning is rapidly becoming an expected feature in products, similar to mobile-friendly websites or apps. Machine learning definition uses data to train predictive models for answering questions on unseen data. Machine learning overview uses data to train predictive models for answering questions on unseen data.
Big data and machine learning are revolutionizing how we process and use large amounts of information. They enable us to derive meaningful insights and develop intelligent solutions to address real-world challenges across various industries.
Here are the main points:
- Big data refers to extremely large and complex datasets that are difficult to manage with traditional tools.
- Machine learning is the ability of computer systems to learn from data, make predictions, and support decision-making.
- Cloud computing plays a crucial role by providing the necessary infrastructure and tools for big data processing and machine learning.
- These fields complement each other, with big data providing the raw information, and machine learning transforming it into valuable insights.
- Big data analytics, machine learning, and AI are distinct but related disciplines.
Data is crucial for machine learning, enabling model improvement and insight discovery. Future episodes will explore specific machine learning techniques, tools, and problem-solving approaches. Social media’s impact on public health, including risks and benefits, needs further understanding. The WHO emphasizes addressing misinformation through risk communication and accurate information dissemination for public health responses. The COVID-19 pandemic highlighted the limitations of existing knowledge in combating misinformation, emphasizing the need for research. Over 1 million deaths and 40 million infections since December 2019. Widespread anxiety and online misinformation hindered public health efforts. Health literacy challenges distinguish reliable from unreliable medical information online, even for those with good health literacy.
Excessive misinformation on social media, especially during COVID-19, burdens the public and necessitates content moderation solutions. Social media data offers valuable insights for improving public health responses during crises. A study in Kurdistan, Iraq, highlighted the prevalence of COVID-19 misinformation.Health-related misinformation on social media during COVID-19 highlights the need for solutions. Infodemiology techniques analyzed online content and correlated digital big data to the pandemic’s progression. Infodemiology can aid pandemic response planning, resource optimization, and identifying misinformation and public concerns. Multimodal data from social media, news, and online behavior is used to develop evidence-based public health interventions. Researchers used Online Ecological Recognition (OER), combining big data and AI, to develop predictive models. The study found increased negative emotions, social risk sensitivity, decreased positive emotions, and life satisfaction during the pandemic. The results align with the Behavioural Immune System (BIS) theory, suggesting heightened negative emotions during disease threats exacerbated by the COVID-19 infodemic. Initially, negative emotions were balanced by positive emotions as users used social media for peer support. Positive outcomes included increased group cohesion and monetary/supply donations to regions in need.
Social media can be leveraged for health promotion, raising awareness, and optimizing response strategies.Social media has both positive and negative impacts. To mitigate the negative impacts, collaboration between social media platforms, NGOs, and governments is crucial to address misinformation. Potential long-term solutions include new legislation governing online misinformation, similar to tobacco advertising regulations. However, enforcing such legislation is complex due to the large number of individual sources compared to corporations. Big data surveillance methods, AI analytics, and developing alternative platforms for reliable health information are potential solutions. Online health communities (OHCs) offer positive impacts through peer support and quality data, while also mitigating negative impacts through policies against misinformation and medical practitioner involvement.
Social media, a double-edged sword in public health, both spreads misinformation and offers potential positive applications. Advanced online health communities (OHCs) should leverage social media’s strengths while addressing its weaknesses, possibly through expert moderation and digital tools. Future research should explore social media’s potential in public health, particularly for chronic pain and mental health disorders, through cross-disciplinary collaborations. The author, DG, has relevant investments in AskDr, Doctorbell, VISRE, and Shyfts, and holds a position as Physician Leader (Telemedicine) at Raffles Medical Group. The other author has no conflicts of interest. Health information sources include AskDr, a reliable health information source, and a study by Audrain-Pontevia et al. (2019) on patient compliance in online health communities, focusing on COVID-19 misinformation. Ethical guidelines for e-health, tobacco marketing restrictions, and a study on US hospital telehealth capacity during COVID-19 are also relevant. This research examines the impact of COVID-19 lockdowns on public sentiment in Wuhan and Lombardy using psycholinguistic analysis on social media data from Weibo and Twitter. It explores the relationship between search trends and telehealth readiness through a cross-sectional analysis. A mixed-methods study on moderators resigning from WebMD communities and a systematic review on health literacy in web-based health information environments are also included.
Another study investigates digital health for patients with chronic pain during the COVID-19 pandemic. Two studies use social media data to study COVID-19’s impact. A study explores online discussion sites for research. Statista provides data on daily social networking time worldwide from 2012 to 2019. Another study examines social media big data in the context of COVID-19, using data from the top 50 affected countries. The review discusses social media big data in the COVID-19 context, including WHO publications. Keywords include coronavirus—COVID-19, public health, big data, social media, and health promotion. The open-access article is licensed under the Creative Commons Attribution License (CC BY). Reviewers are Yee Ling Boo (RMIT University) and Yanchang Zhao (CSIRO).
Social media, a double-edged sword in public health, serves as both an information-sharing tool and a platform for misinformation dissemination, posing challenges during crises like the COVID-19 pandemic. Big data generated by social media platforms can be analyzed to track trends, understand public sentiment, and shape public health strategies. Researchers, marketers, and governments utilize social media data to influence populations, as seen in the Cambridge Analytica scandal. Social media mining involves analyzing data to extract patterns and insights about users, used for targeted marketing campaigns. A study analyzed tweets about brands to gauge user sentiment and identify common topics, revealing insights like promotion popularity and negative sentiment towards specific companies. Researchers collected over 10.5 million tweets from London Underground stations over a year using Twitter’s geotagging function. The collected tweets were analyzed to determine the most popular topics discussed at each station during specific times of the day and week. The study recommends using the analyzed data to create targeted digital out-of-home advertisements in the London Underground.
Privacy concerns arise from social media mining. Concerns arise when users feel their data is used without consent. Misuse of social media data, like in the Cambridge Analytica scandal, can lead to legal consequences. It raises questions about its influence on elections and democratic processes. Data-driven decision making, exemplified by Michael Lewis’s “Moneyball,” highlights the use of data and statistics in baseball recruitment. A major data breach and its potential misuse in the US election were exposed in a Guardian article. Social media analysis, as outlined in Reza Zafarani et al.’s “Social Media Mining An Introduction,” involves collecting and analyzing data from social media platforms to understand user behaviors and sentiments. Social media mining is used in advertising studies to gauge brand perceptions and determine effective advertising strategies. Privacy concerns, particularly regarding political influence, and NLP applications in email filters, smart assistants, and search engines further complicate the issue.
In a nutshell, this module introduces you to the interconnected world of big data and machine learning, explaining their concepts, relationship, and significance in today's technology-driven world.Machine learning in social media analyzes user data, classifies content, and delivers personalized experiences. It improves user engagement and content delivery by enabling data analysis, clustering, and prediction. Machine learning automates data organization and analysis, handling vast amounts of social media information. It identifies and filters spam, junk content, and potential threats, safeguarding user data and the platform. It enables personalized advertising by analyzing user data and preferences, increasing marketing campaign effectiveness. Businesses use built-in analytics tools to track metrics like likes, comments, and clicks to strategically schedule posts. Sentiment analysis helps businesses understand public opinion on their products, competitors, and industry trends.
Data visualization provides insights into connections and trends. Social media allows users to share photos, messages, and video calls, and is used for business, advertising, connecting, and marketing. It provides a platform for people with similar interests to connect and communicate easily. Machine learning enhances social media through algorithms and applications.Machine Learning in Social Media identifies, classifies, and clusters data, enabling personalized user experiences and content optimization. Applications include chatbots, image recognition, social media monitoring, and sentiment analysis. Benefits include improved media content quality, automated data management, and spam filtering.
That wraps up today’s episode of The Study Guide. Remember, we teach to learn, and I hope this has helped you understand Module 2: Big Data & Machine Learning better. Keep studying, keep learning, and keep pushing toward your academic goals. Don’t forget to follow me on all platforms @Kingmusa428 and check out more episodes at kingmusa428.com. See y’all next time!
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