DATA MINING IN SOCIAL NETWORK

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CONTENTS

DATA, KNOWLEDE,INFORMATION

DATA MINING

SOCIAL NETWORK,SOCIAL NETWORK ANALYSIS

DATA MINING IN SOCIAL NETWORKS: 1. GRAPH MINING.

2. TEXT MINING

ACCESSING DATA FROM FACEBOOK

APPLICATIONS OF SOCAIL NETWORK ANALYSIS

LIMITATIONS OF SOCIAL NETWORK ANALYSIS.

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DATA,INFORMATION& KNOWLEDGE

DATA:

FACTS AND STATISTICS COLLECTED TOGATHER FOR REFERENCE ANALYSIS.

THE QUANTITIES ,CHARACTERS ,SYMBOLS ON WHICH OPERATIONS ARE PERFORMED BY A COMPUTER, BEING STORED AND TRANSMITTED.

INFORMATION: THE PATTERNS, ASSOCIATIONS,RELATIONSHIP AMONG ALL THESE

DATA CAN PROVIDE INFORMATION.FOR EXAMPLE ANALYSIS OF SALE TRANSACTION DATA CAN GIVE INFORMATION ABOUT WHICH PRODUCTS ARE SELLING WHEN.

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DATA,INFORMATION& KNOWLEDGE

KNOWLEDGE:

INFORMATION CAN BE CONVERTED INTO KNOWLEDGE ABOUT HISTORICAL PATTERNS AND FUTURE TRENDS.

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DATA MINING

FROM THE LARGE DATA SET FIND THE:

USEFUL

UNKNOWN

INFROMATION.

THE OVERALL ROLE OF DATA MINING IS TO EXTRACT INFORMATION FROM THE DATA SET AND TRANSFORM IT INTO AN UNDERSTANDABLE DATA FOR FURTHUR USE

THE PROCESS OF COLLECTING,SEARCHING THROUGH AND ANALYSING A LARGE AMOUNT OF DATA IN A DATABASE , AS TO DISCOVER PATTERNS AND RELATIONSHIPS.

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A social network is a social structure between actors, mostly individuals or organizations

It indicates the ways in which they are connected through various social familiarities, ranging from casual acquaintance to close familiar bonds

SOCIAL NETWORK6

SOCIAL NETWORK ANALYSIS:DEFINITON

SOCIAL NETWORK ANALYSIS FOCUSES ON THE STRUCTRE OF RELATIONSHIP AMONG A SET OF ACTORS.

Social network analysis maps and measures formal and informal relationships to identify what facilitates or impedes the information and knowledge flows that bind interacting units, viz., who knows whom and who shares what information and knowledge with whom through what media.

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SOCIAL MEDIA PLATFORM

BLOGGING

MICROBLOGS

COMMUNITY-BASED OUESTION ANSWER( C-QA)

EMAILS AND CHAT

HYBRID APPLICATIONS

WIKIS

SOCIAL NEWS

SOCIAL BOOKMARKING

MEDIA SHARING,OPINION VIEWS AND RATINGS

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DATA MINING

TEChNIquE IN SOCIAL

MEDIA

GRAPh MININGTEXT

MINING

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GRAPh MINING

1.)Graph mining:Graphs(or networks) constitute a prominent data structure and

appear essentially in all form of information . Example include the web graph ,social network. Typically, communities correspond to , group of nodes , where nodes within the same community ( or clusters) tend to be highly similar sharing common features ,while on the other hand nodes of different communities show low similarities.

Extracting useful knowledge (patterns, outliers ,etc) from structured data that can be represented as graph.

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GRAPh MINING

• Graph mining is used for understanding relationship as well as content.

• Phone provider can look at phone call records using graph mining.

Example of graph mining in Facebook :Query example: “Restaurants in Pune liked by friends”

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GRAPh DEFINITION12

2.The lines linking each node denote the relationships and interaction between them in order to complete the task.

3. Node 13 represents the webpage and node 14 represents the target audience for the webpage.

1. Each individual or team is shown as a circular node on the diagram. Numbered for ease of reference.

4. The nodes may each have additional

connections outside of the task network identified.

Nodes 1, 3, 4, 5, 6, 8, 11, 14 are the most peripheral

with the least connections.

NETWORK DIAGRAM13

There are two cliques in the network where all nodes are connected to each other: 7-9-10 and 10-12-13.

The nodes with more links show who is well connected in the network

Node 7 has the most connections and

therefore the highest degree centrality.

NETWORK DIAGRAM14

APRIORI-BASED APPROACh 15

PATTERN-BASED APPROACh16

EXAMPLE OF GRAPh MINING FROM FACEBOOK

Sample query for graph search Result for graph search

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TEXT MINING

2.)Text mining:

It is an emerging technology that attempts to extract meaningful information from unstructured textual data. Text mining is an extension of data mining to textual data. A social network contains a lot of data in the nodes of various forms.

For example a social network may contain blogs, articles , messages etc.

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TEXT MINING PROCESS

Data collection:

The data collector module continuously downloads data from one or more social platform and stores raw data into the database. Based on application type the parameters are specified with the API call.

Data Modelling:

This is the process used to define and analyse the data requirements needed to support the application process within the scope of corresponding applications.

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MINING METhODS(TEXT MINING)

MINING METhODS:

1.) Clustering Analysis: Automatic or semi-automatic analysis of large quantity of data to extract previously unknown interesting patterns such as groups of data records known as cluster analysis.

2.) Anomaly detection: It’s the search for items or events which do not confirm to an expected pattern.

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ACCESS DATA FROM FACEBOOK

Facebook platform provides API,SDK for developing applications which access the Facebook data. The Facebook SDK provides a fast native, Facebook integration ,using the exact same implementation regardless of which environment you are deployed to.

In mobile Facebook provides SDK for:

1. iOS platform

2. Android platform.

For web development SDK are provided by both Facebook and the community:Php, JavaScript ,ruby,node.js, C#

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FACEBOOK API

Search API: The graph API is a simple HTTP based API that gives access to the Facebook social graph, uniformly represented objects in graph and connection between them.

FQL: Facebook Query Language enables you to use a SQL type interface to query the data exposed by the graph API.

Dialogs: Facebook offers a number of dialogs to a Facebook Login, posting a person’s timeline or sending requests.

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FACEBOOK API

Chat: One can integrate a Facebook chat into a web-based desktop or mobile instant messaging products.

Ads API: This allows you to build your own app as a customized alternative to the Facebook ads.

Public feed API: This lets you read a stream of public comments that have been posted.

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APPLICATIONS OF SOCIAL NETWORK ANALYSIS

If they are understood ,better relationships and knowledge flows can be measured, monitored, and evaluated, perhaps (for instance) to enhance organizational performance

Identify individuals, teams, and units who play central roles.

Discern information breakdowns, bottlenecks, structural holes, as well as isolated individuals, teams, and units.

Make out opportunities to accelerate knowledge flows across functional and organizational boundaries.

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APPLICATIONS OF SOCIAL NETWORK ANALYSIS

Strengthen the efficiency and effectiveness of existing, formal communication channels.

Leverage peer support.

Improve innovation and learning.

Refine strategies.

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LIMITATIONS OF SNA

Connections may sometimes may not depict correct hierarchy.

SNA does not show the effectiveness or quality of the relationships between people. Some connections may be more productive than others. But sometimes such connections are not considered.

SNA does not show breakdowns in communication or barriers

In many cases the graphs are large scale hence difficult to control

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CONCLuSION

SOCIAL MEDIA : BIG, RICH AND OPEN DATA-BILLION USERS,BILLION CONTENTS

-TEXTUAL MULTIMEDIA

-BILLIONS OF CONNECTIONS

CHALLENGES:-LARGE – SCALE NETWORK

-NOISE

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ThANKYOu

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