L2 Introduction SMA-Part I.pptx introduction to social media
- 1. Evolution of Social Media Analytics
- 2. The field of social media analytics has undergone significant transformations over the years, driven by ● advancements in technology, ● changes in user behavior, and ● the increasing importance of social media in ○ personal, ○ business, and ○ societal contexts.
- 4. Evolution of Social Media Analytics ● Early stages (Pre-2000s): Social media platforms like blogs, forums, and message boards begin to gain popularity, but there are few tools available for analyzing the data generated by these platforms. ● Rise of Web 2.0 (2000s): Social media platforms like MySpace, LinkedIn, and Facebook gain widespread popularity, and new tools and technologies for data collection and analysis begin to emerge. ● Growth of Social Media (2010s): Platforms like Twitter, Instagram, and YouTube experience rapid growth, and social media analytics becomes increasingly important for businesses and researchers. ● Advancement in technology (2012-2015): A new generation of analytics tools and technologies, such as machine learning and natural language processing, become available for analyzing social media data, making it possible to extract more insights from social media data.
- 5. Evolution of Social Media Analytics ● Business Adoption (2015-2022): Social media analytics becomes increasingly important for businesses, as more and more companies begin to use data from social media platforms to inform their decision-making. ● Real-time data and AI (2022-now): Advancement in technology enables social media analytics to access real-time data and use of artificial intelligence, which allows businesses to make decisions and predictions in real-time. ● The future: Social media analytics is expected to continue to evolve, with new technologies and techniques emerging that will make it even more powerful and valuable for businesses and researchers
- 6. The Early Days (2000s): Basic Metrics ● Platforms & Landscape: Early platforms like MySpace, Friendster, and later Facebook and Twitter, laid the groundwork for social interaction online. ● Focus: Initial analytics focused on basic metrics like follower counts, likes, and page views. ● Tools: Basic built-in tools provided simple insights, such as Facebook Insights for pages and Twitter’s native analytics. ● Usage: Analytics were primarily used to measure basic engagement and content reach.
- 7. Rise of Advanced Tools (2010–2015): Engagement & Sentiment Analysis ● Platforms: The rise of Instagram, LinkedIn, and Pinterest diversified social media ecosystems. ● Focus: ○ Tracking engagement metrics such as shares, retweets, and comments. ○ The emergence of sentiment analysis to gauge public opinion and brand reputation. ● Tools: Third-party platforms like Hootsuite, Sprout Social, and Google Analytics began to offer more robust reporting features. ● Advancements: ○ The use of APIs for real-time data collection. ○ Basic demographic insights to understand audience segments.
- 8. AI and Big Data Integration (2015–2020): Predictive Analytics ● Technological Shifts: The integration of machine learning and AI transformed raw data into actionable insights. ● Focus: ○ Predictive analytics to forecast trends and user behavior. ○ Analysis of video and image content with AI-powered tools. ○ Tracking ROI and attribution models for ad campaigns. ● Tools: ○ Advanced platforms like HubSpot, Socialbakers, and Brandwatch offered comprehensive dashboards. ○ Sentiment and emotion analysis became more accurate, moving beyond text to analyze emojis and multimedia. ● Usage: ○ Businesses began using analytics to inform product development, customer service, and influencer collaborations.
- 9. Real-Time and Behavioral Insights (2020–Present) Technological Breakthroughs: ○ Natural Language Processing (NLP) advancements for nuanced sentiment analysis. ○ The use of blockchain for transparency in social media ad metrics. ○ Enhanced algorithms for identifying fake news and bot activity. Focus: ● Real-time analytics for live content (e.g., streams on TikTok, Instagram Live). ● Deep behavioral insights to understand purchase intent, loyalty, and advocacy. Tools: ● AI-driven platforms like Talkwalker, Sprinklr, and Meltwater. ● Integration with Customer Relationship Management (CRM) systems to provide a unified customer view. Applications: ● Proactive crisis management. ● Personalization at scale, using user-specific data to tailor content and ads.
- 10. Future Trends: Immersive and Ethical Analytics Focus Areas: ● Immersive Experiences: Analytics for augmented reality (AR) and virtual reality (VR) interactions on platforms like the metaverse. ● Ethical Analytics: Emphasis on data privacy and transparency, driven by regulations like GDPR and CCPA. ● Hyper-Personalization: Leveraging AI to provide individualized recommendations and ads. ● Sustainability Metrics: Analyzing the impact of social media campaigns on sustainability goals. Tools: Emerging tools will likely incorporate blockchain, quantum computing, and federated learning to enhance data security and processing power.
- 13. Impact of Social Media on the Student Community ● Social media has profoundly influenced the student community, offering both opportunities and challenges. ● Its pervasive nature has affected ○ students' education, ○ social interactions, ○ mental health, and ○ personal development
- 14. Positive Impacts 1. Enhanced Learning Opportunities ● Access to Resources: Platforms like YouTube, LinkedIn Learning, and educational pages on Instagram and TikTok provide tutorials, lectures, and resources. ● Collaborative Learning: Social media fosters group discussions, peer collaboration, and global knowledge exchange through platforms like Slack, Discord, or Facebook Groups. ● Skill Development: Students can learn new skills, such as coding, graphic design, or content creation, through free and paid resources shared on social platforms.
- 15. Positive Impacts 2. Networking and Career Development ● Professional Connections: LinkedIn and similar platforms allow students to network with industry professionals and peers. ● Internships and Job Opportunities: Many companies share job openings and internships directly on social media. ● Portfolio Building: Platforms like Behance and Instagram enable students to showcase their talents and creativity.
- 16. Positive Impacts 3. Social Awareness and Activism ● Increased Awareness: Social media educates students on social, political, and environmental issues, making them more informed citizens. ● Activism: Students can participate in movements, raise funds, and advocate for causes through viral campaigns and petitions.
- 17. Positive Impacts 4. Creativity and Expression ● Platforms for Expression: Students can explore and share their creativity through art, writing, videos, and photography on platforms like Instagram, TikTok, and Medium. ● Content Creation: Many students leverage social media to become content creators, building personal brands and even earning income.
- 18. Positive Impacts 5. Support and Community ● Mental Health Support: Social media provides access to mental health resources, support groups, and motivational content. ● Global Communities: Students can find and connect with like- minded individuals, fostering a sense of belonging.
- 19. Negative Impacts 1. Distraction from Studies ● Reduced Focus: Excessive use of social media can lead to procrastination and decreased academic performance. ● Time Management Issues: Students often spend hours scrolling, impacting their productivity.
- 20. Negative Impacts 2. Mental Health Concerns ● Anxiety and Depression: Constant comparison with others’ curated lives can lead to feelings of inadequacy and low self-esteem. ● Cyberbullying: Students are vulnerable to online harassment, which can cause severe emotional and psychological distress. ● Addiction: Overuse of social media can lead to addictive behaviors, disrupting daily life.
- 21. Negative Impacts 3. Privacy and Security Risks ● Data Misuse: Students may unknowingly share personal information, leading to privacy breaches. ● Online Predators: Young users are particularly vulnerable to malicious actors and online scams.
- 22. Negative Impacts 4. Unrealistic Expectations ● Pressure to Conform: Influencers and trends can create unrealistic standards for appearance, lifestyle, and success. ● FOMO (Fear of Missing Out): Students often feel pressured to participate in activities or trends to stay relevant.
- 23. Negative Impacts 5. Spread of Misinformation ● Credibility Issues: Students may encounter and share false or misleading information. ● Influence on Beliefs: Misinformation can shape opinions, sometimes in harmful ways.
- 24. Strategies to Mitigate Negative Impacts ● Digital Literacy Education: Teaching students to critically evaluate content and understand online risks. ● Time Management Tools: Apps to handle Screen Time help students limit distractions. ● Parental and Institutional Guidance: Educators and parents can provide support and set boundaries for healthy usage. ● Promoting Balance: Encouraging offline activities and real-world interactions to reduce dependence on social media.
- 25. Role of Social Media Analytics in Business and Research
- 26. Role of Social Media Analytics in Business and Research ● Understanding the customer ● Measuring campaign performance ● Identifying influencers ● Improving customer service ● Monitoring competitors ● Research ● Predictive Analysis ● Real-time data
- 27. The importance of social media analytics in today's digital world:
- 28. The importance of social media analytics in today's digital world: ● Social media is a powerful marketing tool: With over 4.8 billion active users on social media platforms, social media analytics allows businesses to reach and engage with a large, global audience. ● Understanding consumer behavior: Social media analytics provides insights into consumer behavior, preferences and sentiment which can inform marketing and product development strategies. ● Identifying trends: Social media analytics can be used to identify and capitalize on emerging trends and topics, which can help businesses to stay ahead of the curve. ● Real-time data: With the use of real-time data and AI, social media analytics can help businesses to make decisions and predictions in real-time.
- 29. The importance of social media analytics in today's digital world: ● Enhancing customer experience: Social media analytics can be used to improve customer service, by identifying and addressing customer complaints and feedback in real-time. ● Competitive advantage: Social media analytics can help businesses to monitor their competitors and identify areas for improvement, which can give them a competitive edge in the marketplace. ● Research: Social media analytics can be used in research to gain a better understanding of public opinion, sentiment, and behavior in various fields such as politics, healthcare, and education. ● Informed decision-making: With the help of social media analytics, businesses can make informed decisions that improve the effectiveness of their marketing strategies and the overall performance of their organization.
- 30. Different types of Social Media Data and Platforms
- 31. Different types of Social Media Data and Platforms ● Social networks: Platforms like Facebook, LinkedIn, and Twitter allow users to create profiles, connect with others, and share content. Data from these platforms can be analyzed to understand user demographics, behavior, and relationships. ● Microblogs: Platforms like Twitter and Tumblr allow users to post short, text-based updates. Data from these platforms can be analyzed to understand the spread of information, opinion, and sentiment. ● Media sharing platforms: Platforms like Instagram, YouTube, and TikTok allow users to share photos, videos, and other types of media. Data from these platforms can be analyzed to understand engagement, virality, and audience preferences.
- 32. Different types of Social Media Data and Platforms ● Forums and discussion boards: Platforms like Reddit and Quora allow users to participate in discussions and share information on a wide range of topics. Data from these platforms can be analyzed to understand user interests and opinions. ● E-commerce and review platforms: Platforms like Amazon, Yelp, and TripAdvisor allow users to share reviews and ratings of products and services. Data from these platforms can be analyzed to understand consumer sentiment and preferences. ● Blogs and news sites: Platforms like Medium, WordPress, and news sites allow users to share long-form content, thoughts and opinions. Data from these platforms can be analyzed to understand consumer interest and opinion on various topics. ● Real-time platforms: Platforms like Twitch and Periscope allow users to share live video content. Data from these platforms can be analyzed to understand audience engagement and preferences.
- 33. Different Types of Insights from Social Media Analytics
- 34. Different Types of Insights from SMA ● Demographic insights: Social media analytics can provide insights into the demographic characteristics of social media users, such as age, gender, location, and education level. ● Behavioral insights: Social media analytics can provide insights into the behavior of social media users, such as their interests, hobbies, and online habits. ● Sentiment insights: Social media analytics can be used to understand the sentiment and opinions of social media users about a brand, product, or service through sentiment analysis. ● Audience engagement insights: Social media analytics can provide insights into how audiences engage with a brand's social media content, such as likes, comments, shares, and click-through rates.
- 35. Different Types of Insights from SMA ● Campaign performance insights: Social media analytics can provide insights into the performance of social media campaigns, such as reach, engagement, and conversion rates. ● Influencer insights: Social media analytics can be used to identify key influencers within a target audience, and understand how they engage with a brand's social media content. ● Consumer insights: Social media analytics can provide insights into the needs, wants and preferences of the consumers, which can inform product development and marketing strategies. ● Competitor insights: Social media analytics can be used to monitor the social media activity of competitors, which can provide insights into their strategies and tactics. ● Real-time insights: With the use of real-time data and AI, social media analytics can provide insights in real-time, which can help businesses to make decisions and predictions in real-time.
- 37. Social Media Analytics Challenges ● Data quality and completeness: Social media data is often incomplete, unstructured and difficult to clean, which can make it difficult to extract meaningful insights. ● Data privacy and security: Social media analytics requires access to sensitive user data, which can raise privacy and security concerns. ● Data volume and velocity: Social media platforms generate large amounts of data at a high velocity, which can make it difficult to process and analyze in a timely manner. ● Lack of standardization: Different social media platforms have different data structures and APIs, which can make it difficult to integrate and analyze data from multiple platforms.
- 38. Social Media Analytics Challenges ● Biased data: Social media analytics can be biased by the self-selection of users who are more likely to engage with social media, which can lead to a skewed representation of the general population. ● Lack of context: Social media data is often stripped of context, which can make it difficult to understand the meaning and intent behind it. ● Ethical and legal considerations: Social media analytics raises ethical and legal considerations such as informed consent, data protection and compliance. ● Lack of expertise: Social media analytics requires a certain level of expertise in data science and analytics, which can be a challenge for organizations that lack the necessary resources. ● Lack of Integration: Social media analytics is often used in isolation, which can make it
- 39. Social Media Analytics Tools and Techniques
- 40. Social Media Analytics Tools and Techniques ● Data collection and scraping tools: Tools such as Scrapy, BeautifulSoup, and Apify allow for the collection and scraping of data from social media platforms. ● Data visualization tools: Tools such as Tableau, Power BI, and Gephi allow for the visualization of social media data in an easy-to-understand format. ● Text mining and natural language processing (NLP) tools: Tools such as NLTK, CoreNLP, and TextBlob allow for the analysis of text data, including sentiment analysis and opinion mining. ● Network analysis tools: Tools such as Gephi, Cytoscape, and NodeXL allow for the analysis of connections and relationships between social media users.
- 41. Social Media Analytics Tools and Techniques ● Machine learning tools: Tools such as scikit-learn, TensorFlow, and Keras allow for the application of machine learning algorithms to social media data, including classification and clustering. ● Social media management tools: Tools such as Hootsuite, Buffer, and Sprout Social allow for the scheduling and management of social media content. ● Sentiment analysis tools: Tools such as Sentiment140, NLTK, and TextBlob allow for the analysis of sentiment of social media data. ● Social listening tools: Tools such as Mention, Hootsuite Insights, and Brand24 allow for the monitoring of social media conversations and mentions of a brand or topic.
- 42. Any Question ??