Study Guide: Social Media Analytics & AI - Module 13

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 Future of Research in Social Media and we will be focusing on Module 13: Future of Research in Social Media" Let's dive into our module on the Future of Research in Social Media. We're exploring the key trends shaping social media analytics.

Key Concept of the Day: 

Today, we're focusing on the future of social media research, including the importance of data visualization for understanding large datasets, the impact of the Internet of Things (IoT) on data generation, and the role of algorithms and AI in shaping online experiences. This week’s module explores the future of social media research, emphasizing the significance of data, data visualization, the IoT’s impact, and algorithms and AI’s influence. It discusses mobile video marketing, virtual reality in communication, micro-influencers, and personalized interactions. 

Technological advancements like faster internet, more powerful computers, and new interaction methods drive these changes. Social media is also evolving with trends and communication processes. Our goal is to understand how people use technology, especially video calling and social media, and their online behavior. We’ll cover data collection, digital transformation, social media integration, AI in communication, and virtual reality’s impact. Social media users are growing, with an expected 6-6.4 billion worldwide by 2025, generating vast amounts of data. The IoT connects physical objects with sensors and software, enabling data exchange and communication. 

While IoT improves lives, managing large data amounts can be challenging. Data-driven strategies can use IoT to reduce traffic, enhance healthcare, and make homes more efficient. IoT is projected to create trillions of dollars in value across sectors like predictive maintenance, smart homes, smart cities, and smart offices. Data storytelling, like movie visuals, uses data to tell stories. We’ll compare data visualization to Hollywood storytelling and illustrate how data-driven strategies can make a real difference using Cary Elwes’s story.

Understanding these trends is crucial for navigating the evolving landscape of social media, addressing ethical considerations, and developing effective strategies for research and application.

Here are the main points:

1. Data visualization is essential for making sense of the increasing volume, variety, and velocity of data.
2. The Internet of Things (IoT) is expanding the amount of data available for analysis.
3. Algorithms influence what users see on social media, raising concerns about transparency and filter bubbles.
4. AI is transforming content creation and analysis, with ethical implications that need to be considered.
5. Future research will focus on algorithm transparency, privacy, mental health, and AI ethics.

Data’s power drives innovation across industries. Analyzing and understanding large datasets is crucial, and data visualization makes it accessible and insightful. Social media algorithms shape user experiences, raising questions about transparency and potential biases. Despite lacking green screens, The Princess Bride was creative and resourceful. Hollywood uses sets, costumes, props, and special effects to bring movies to life. Animation in Hollywood brings imagination to life by making things move. Disney, which has been making animated movies since 1937, has classics like Snow White and the Seven Dwarfs and Frozen. Tableau, a data visualization company, collaborates with Hollywood names like Pixar co-founder Pat Hanrahan and former LucasFilm CTO Dave Story to create data visualizations. 

Startups like Visual Cue use animation and storytelling to make data visualizations more interactive and understandable, aiding decision-making. Gapminder Trendalyzer, a bubble chart, uses animation to show trends over time in three dimensions, with time as the fourth dimension. Animation enhances storytelling and highlights important results, especially with narration. However, analyzing unfamiliar data can be challenging, as analysts may spend time deciphering the animation’s meaning. Animation can make statistical data visually appealing, but careful design ensures data context remains clear. Streaming data visualization analyzes and predicts real-time data, changing how we discover things from reactive to proactive. Data visualization types include animations. It’s divided into two main types: pre-existing data visualization, which shows past data movement, and streaming data visualization, which shows real-time data updates. 

Streaming data visualization provides real-time insights, enabling proactive decisions, especially in stock trading. Visual sedimentation, inspired by sedimentation, visualizes streaming data that can’t be represented by traditional charts. It represents data streams as objects falling and building up over time, similar to sediment in a river. The tool handles various data types, including tweets, Wikipedia edits, and birth and death rates. It uses a special layout and forces like gravity, decay, and friction to make the data flow and settle. The Visual Sedimentation Toolkit is a free JavaScript library compatible with D3.js, jQuery, and Box2DWeb.

Mobile-first design prioritizes mobile devices, adapting to larger screens later. With 1.2 billion global mobile users and 25% being mobile-only, mobility simplifies visual analytics and self-discovery. Users prioritize user experience and interactivity (70%), followed by app design, visual analysis, and security. Popular chart types include column, stacked column, area, line, pie, comparison, waterfall, scatter, and bubble charts. The wearable devices market, growing at 35% CAGR (2014-2019), faces challenges like small screens, clunky designs, and limited battery life. Wearable devices include convenience, smartwatch/communication, and fitness trackers. RFID tag bracelets serve as room keys, credit cards, FastPass tickets, and photo logs. Magicband collects user data, enhancing Disney’s guest experience and revenue through activity and spending tracking. Combining data improves crowd management, helping Disney understand crowd movement and patterns. 

The Apple Watch, a personal device with iPhone-like features, boasts a large market share, expected to reach 48% by 2017 from 40% in 2015. Fitbit, a fitness tracker, monitors physical activity, sleep, and eating habits, providing insights into distance, activity, calories burned, and elevation gain. It displays activity data on an OLED screen, adds games, and alerts users when they reach goals. Fitbit sends data to a wireless base station for analysis and transformation into useful information. Women can wear stylish jewelry accessories like Ringly, a gemstone ring that connects to their phones and sends notifications. Check out Ralph Lauren’s PoloTech™ shirt! It has various features to help you stay fit, including tracking heart rate, breathing, and balance. Personal Visual Analytics (PVA) uses visual representations to help you understand your data better, like having a personal data detective. PVA designs tools that analyze data in everyday situations, considering preferences and budget. It’s like a personal data assistant that helps you make the most of your time and resources. PVA combines ideas from different fields to create powerful and enjoyable data experiences. It aims to help you understand and explore your data, fostering creativity and discovery. PVA also shows how data can be used for fun and engagement. It’s working on making analytics more enjoyable through playful visualizations and gamification. Gamification rewards behaviors within a visual analytics system, like earning virtual badges or using simulations for recruitment. Gameplay involves tactical aspects like rules and player interactions, while mechanics involve technical aspects like rules and challenges. 

Data visualization is interactive, allowing you to manipulate data and explore possibilities. Gamification, gameplay, and playful data visualization all involve interactivity, which helps you gain insights, spark creativity, and foster visual thinking. Data visualization has six main interaction categories: select, explore, reconfigure, encode, abstract/elaborate, filter, and connect. Interactivity is crucial for visual discovery, allowing you to balance analysis with creativity.

MURAL, an interactive display system, aids scientists and info visualizers in research projects and presentations. With 15.4 million pixels, it can be displayed on a massive 7-foot high, 16-foot wide glass screen. MURAL uses augmented reality (AR) to make abstract data more engaging and understandable beyond charts and graphs. Classcraft, an online educational role-playing game, makes learning fun through play and teamwork, motivating students and improving grades. It also increases student engagement, improves classroom behavior, and maximizes classroom space. 

As data-driven companies shift towards a visual culture of data discovery, consumers and individuals are experiencing changes in their visual habits. Visualization simplifies analytics, making complex information easier to understand and valuable. To embrace this visual revolution, consider using visualization tools and techniques, ensuring mobile-first websites, tackling complex website challenges, discussing data storage solutions with Hitachi Data Systems, exploring animated transitions in data graphics, and prioritizing personalized visualization based on individual needs. Algorithms, sets of instructions computers follow to display content on platforms like dating apps, online stores, and search engines, are kept secret by tech companies. However, lawmakers are pushing for greater transparency in algorithms. The filter bubble issue arises when addictive algorithms limit users’ exposure to different ideas. Regulating algorithms is challenging, potentially infringing on people’s right to speak, use technology, and use social media.

In a nutshell, this module explores the future of social media research, emphasizing the importance of data visualization, the impact of IoT, and the transformative role of algorithms and AI. The Internet of Things (IoT) generates vast data, emphasizing the need for robust visualization tools. AI transforms social media, from content creation to analysis, presenting opportunities and ethical challenges. Future research focuses on algorithm transparency, user privacy, mental health impact, and ethical AI implications. Understanding how speech, tech, and social media affect society is crucial. Researchers aim to make social media algorithms transparent and ethical, especially regarding user privacy and public opinion. They’ll explore social media’s impact on mental health and find ways to reduce negative effects like addiction and misinformation. 

AI ethics and bias are significant concerns. Integrating AI into social media raises ethical questions about potential biases and their impact on social interactions. Social media research is essential for developing methods for studying social media, creating a community of learning and sharing knowledge, and designing safer, more inclusive, and beneficial platforms. Focusing on follower growth rate, especially compared to competitors, helps assess performance and make improvements. Comparing social media metrics like reach and impressions is important. Organic posts often don’t reach many people, so paid advertising is usually more effective. 

Making content more human makes it more enjoyable and helps reach a wider audience. Stories that connect with people tend to get more likes and comments, making them more visible. The engagement rate shows how many people interact with posts compared to views, while the click-through rate (CTR) is crucial for posts with links, indicating the effectiveness of call-to-action. The CTR measures link clicks and traffic driving. The conversion rate measures desired actions after a link click, like purchases. Tools like Facebook Pixel or LinkedIn Insight Tag track conversions and provide marketing insights. Reviewing social media analytics every three to six months provides meaningful insights. Monitoring analytics during paid ad campaigns ensures desired results and necessary changes. Engaging content keeps people on social media.

That wraps up today’s episode of The Study Guide. Remember, we teach to learn, and I hope this has helped you understand Module 13: Future of Research in Social Media 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!