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Why we need data mining in social
media
 People share more data
 Products are more customized
 More users produce more data
 Market research
 Product development and design
Graph mining
 Graphs appear in all form of information
 In this approach communities corresponding to the same, are clustered
together
 These clustered data tend to have more similarities
Graph mining usage
 It is used by google to group links on a result page based on meta data
 Graph mining also sorts the results based on the hit count of a link.
 Each time an user clicks a links, the corresponding page rank is incremented
 The links with top counts are grouped together
Graph mining on facebook
 Searching people: Friends of friends who are single in Pollachi, here the
people with the higher mutual friends are grouped and sorted accordingly
 Searching places: Restaurants in Chennai liked by friends
 Searching interests: Movie most of my friend likes
 Adds are tailored for user
Text mining
 Text mining is an emerging technology that attempts to extract meaningful
information from unstructured textual data.
 Social networks contain a lot of texts and posts made daily
Usage of text mining
 Automatic processing of emails:
 Texts are classified automatically
 Junk mails are separated
 We could potentially extract a complete information from a website
automatically
Text mining methods
 Cluster analysis:
 Automatic or semi automatic analysis of large data and extract previously unknown
patterns
 Anomaly detection:
 It is the search of items or events that do not fit the expected pattern
Conclusion
 Valuable information is hidden in vast amount of social media
 Presenting suitable opportunities on extracting data form social media, we
could learn a lot
 A great example for knowledge extraction on social media is that, Twitter
found that BJP is gonna win the election before the voting day itself

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Data mining on Social Media

  • 1.
  • 2. Why we need data mining in social media  People share more data  Products are more customized  More users produce more data  Market research  Product development and design
  • 3. Graph mining  Graphs appear in all form of information  In this approach communities corresponding to the same, are clustered together  These clustered data tend to have more similarities
  • 4. Graph mining usage  It is used by google to group links on a result page based on meta data  Graph mining also sorts the results based on the hit count of a link.  Each time an user clicks a links, the corresponding page rank is incremented  The links with top counts are grouped together
  • 5. Graph mining on facebook  Searching people: Friends of friends who are single in Pollachi, here the people with the higher mutual friends are grouped and sorted accordingly  Searching places: Restaurants in Chennai liked by friends  Searching interests: Movie most of my friend likes  Adds are tailored for user
  • 6. Text mining  Text mining is an emerging technology that attempts to extract meaningful information from unstructured textual data.  Social networks contain a lot of texts and posts made daily
  • 7. Usage of text mining  Automatic processing of emails:  Texts are classified automatically  Junk mails are separated  We could potentially extract a complete information from a website automatically
  • 8.
  • 9. Text mining methods  Cluster analysis:  Automatic or semi automatic analysis of large data and extract previously unknown patterns  Anomaly detection:  It is the search of items or events that do not fit the expected pattern
  • 10. Conclusion  Valuable information is hidden in vast amount of social media  Presenting suitable opportunities on extracting data form social media, we could learn a lot  A great example for knowledge extraction on social media is that, Twitter found that BJP is gonna win the election before the voting day itself