Social Media Mining is the process of obtaining big data from user-generated content on social media websites and mobile apps in order to extract patterns, form conclusions about users, and act upon the information, often for the purpose of advertising to users or conducting research.

2. What is Data Mining?
 Data Mining is a Process of Discovering and Extracting Patterns in
Large Data Sets Methods at the Intersection of Statistics,
Database Systems, and Machine Learning.
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3. Social Media
 Social Media is Defined as a Group of Web-Based App that
Permits the Exchanges and Creation of User-Generated Content.
 Social Media gives clients an easy-to-use way to communicate
with each other on an unparalleled scale.
 Facebook is a social networking site, with more than 2.74 Billion
monthly active users as of 2021.
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4. Classification of Social Media
 There are 9 types of Social Media:
 Online Social Networking
 Micro-Blogging
 Blogging
 Social News
 Social Bookmarking
 Wikis
 Opinion, Rating & Reviews
 Answers
 Media Sharing
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5. What is Social Media Mining?
 Social Media are interactive technologies that permit the creation
or exchange of ideas, information, career interests, and other
forms of appearance via virtual networks and communities.
 The primary objectives of the data mining procedure are to
efficiently handle large-scale information, gain insightful
knowledge, and scrape actionable patterns.
 Users on Twitter generate over 500 million Tweets every day.
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6.  Social Media Mining is a growing multidisciplinary area where
researchers of various backgrounds can do vital contributions
that matter for social media development and research.
 Scraping Data from Social Media can enlarge research ability to
understand new phenomena to develop innovation and offer
better opportunities.
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7. The Reason For Growth of Social Media
Mining
 This is How Social Media Growth is Driven:
1. How can a user be heard?
2. Which source of information should a user use?
3. How user experience can be improved?
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8. The Amount of Data
 For example, Twitter and Facebook report Web data from approximately
2.90 Billion Facebook users and 63.9 million Twitter U.S. visitors per
month, respectively.
 As per the video-sharing site YouTube, more than 5 billion videos are
viewed per day, and 60 hours of videos are uploaded every minute.
 The picture sharing site Flickr, as of 2021, hosts more than 6 Billion photo
images.
 Web-based, collaborative, and multilingual Wikipedia hosts over 20
Million articles attracting over 365 Million readers.
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9. Challenges in Social Media Mining
 Social Media Data Are:
 Vast
 Noisy
 Distributed
 Unstructured
 Dynamic
These characteristics pose challenges to the data mining task to
invent new efficient techniques and algorithms.
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10. Tools Used For Social Media Mining
 Twitter Tools
 Data Mining Tools
 Text Mining Tools
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 Cloud4Trend
 Twitter Tracker
 Google Fast Flip

11. What is The Use of Data Mining in
Social Media?
 You will get Social Media Data Everywhere
 Overload of Data
 Data Overloaded (Blogs, Photos, Videos, Bookmarks)
 Interaction Overloaded (Taggers, Friends, Followers)
How to Scrape Data from this Chaos?
(Social Media captures the‘ pulse of humanity’)
 Directly study behavior & opinion of millions of users to increase insight into:
 Human Behavior
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 Market Analytics  Product Sentiments

12. Application of Social Media Mining
 Personalization
 Suggesting Markets
 Targeted Marketing
 Community Analysis
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 Sentiment Analysis
 Opinion Mining
 Social Recommendation
 Influence Modeling

13. Research Issues in Social Media Mining
Community Analysis
Social Recommendation
Influence Modeling
Sentiment Issue in Social Media Mining
Privacy, Trust, and Security
Information Diffusion and Provenance
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14. Thank You For Visit