This document discusses social data mining. It begins by defining data, information, and knowledge. It then defines data mining as extracting useful unknown information from large datasets. Social data mining is defined as systematically analyzing valuable information from social media, which is vast, noisy, distributed, unstructured, and dynamic. Common social media platforms are described. Graph mining and text mining are discussed as important techniques for social data mining. The generic social data mining process of data collection, modeling, and various mining methods is outlined. OAuth 2.0 authorization is also summarized as an important process for applications to access each other's data.
A glimpse into what social media is all about and how the researchers in the world are using social media. Social media is not a mere hype and not a platform to leverage word-of-the-mouth practices as is the common perception of it in Pakistan: it is much more than that and this is what this talk presented.
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
A glimpse into what social media is all about and how the researchers in the world are using social media. Social media is not a mere hype and not a platform to leverage word-of-the-mouth practices as is the common perception of it in Pakistan: it is much more than that and this is what this talk presented.
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
Social Media Mining - Chapter 8 (Influence and Homophily)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Social Media Mining - Chapter 10 (Behavior Analytics)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
key note address delivered on 23rd March 2011 in the Workshop on Data Mining and Computational Biology in Bioinformatics, sponsored by DBT India and organised by Unit of Simulation and Informatics, IARI, New Delhi.
I do not claim any originality either to slides or their content and in fact aknowledge various web sources.
Brief tutorial on Influence and Homophily in social networks. Key concepts. How to distinguish influence from correlation. Information diffusion processes. Influence Maximization Problem
and viral marketing.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
Social Media Mining - Chapter 8 (Influence and Homophily)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Social Media Mining - Chapter 10 (Behavior Analytics)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
key note address delivered on 23rd March 2011 in the Workshop on Data Mining and Computational Biology in Bioinformatics, sponsored by DBT India and organised by Unit of Simulation and Informatics, IARI, New Delhi.
I do not claim any originality either to slides or their content and in fact aknowledge various web sources.
Brief tutorial on Influence and Homophily in social networks. Key concepts. How to distinguish influence from correlation. Information diffusion processes. Influence Maximization Problem
and viral marketing.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Brief demonstration of social network analysis in R using the package igraph. Based on a presentation by Drew Conway at a NYC R Statistical Programming Meetup.
Introduction to Social Media for ResearchersHelen Dixon
Slides from the Introduction to Social Media for Researchers course produced by Dr Helen Dixon for Postgraduate Research Students at Queen's University Belfast.
Easy to digest information on the importance of hydration in sport, the physiological effects of dehydration on performance, the role of sports drinks as an ergogenic aid!
Problem: People use social media to showcase an artificial life, aimed to be better than their peers.
Insight: "I want to break up with social media as it makes me feel depressed and insecure".
Solution: Escape the enemy on social media.
Strategy: "US V.S THEM" - poke fun at others who dampen your day.
Execution: Instagram & Weibo.
To find the students awareness of social networks.
b. To find for what purposes the students are using social networks.
c. To find effects of social networks on studies of the students.
d. To find Student’s ideas on how social networks can be used positively for education purposes.
e. To find average time spent on social networks by UNIVOTEC students
f. To find average expenditure spend by students on sustenance in social network
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)paperpublications3
Abstract: The main aim of this project is secure the user login and data sharing among the social networks like Gmail, Facebook and also find anonymous user using this networks. If the original user not available in the networks, but their friends or anonymous user knows their login details means possible to misuse their chats. In this project we have to overcome the anonymous user using the network without original user knowledge. Unauthorized user using the login to chat, share images or videos etc This is the problem to be overcome in this project .That means user first register their details with one secured question and answer. Because the anonymous user can delete their chat or data In this by using the secured questions we have to recover the unauthorized user chat history or sharing details with their IP address or MAC address. So in this project they have found out a way to prevent the anonymous users misuse the original user login details.
Introduction to the Responsible Use of Social Media Monitoring and SOCMINT ToolsMike Kujawski
These are my slides from a custom tool-based demonstration workshop I was asked to do where I went over various free tools that can be used to obtain valuable public data.
This Presentation covers data mining, data mining techniques,
data analysis, data mining subtypes, uses of data mining, sources of data for mining, privacy concerns.
The project is to ask college related queries and get the responses through a chatbot an Artificial Conversational Entity. This System is a web application which provides answer to the query of the student. Students just have to query through the bot which is used for chatting. Students can chat using any format there is no specific format the user has to follow. This system helps the student to be updated about the college activities.
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
Identical Users in Different Social Media Provides Uniform Network Structure ...IJMTST Journal
The primary point of this venture is secure the client login and information sharing among the interpersonal organizations like Gmail, Face book and furthermore find unknown client utilizing this systems. On the off chance that the first client not accessible in the systems, but rather their companions or mysterious client knows their login points of interest implies conceivable to abuse their talks. In this venture we need to defeat the mysterious client utilizing the system without unique client information. Unapproved client utilizing the login to talk, share pictures or recordings and so on. This is the issue to be overcome in this venture .That implies client initially enlist their subtle elements with one secured question and reply. Since the unknown client can erase their talk or information. In this by utilizing the secured questions we need to recuperate the unapproved client talk history or imparting subtle elements to their IP address or MAC address. So in this venture they have discovered an approach to keep the mysterious clients abuse the first client login points.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
Information is overloaded in the Internet due to the unstable growth of information and it makes information search as complicate process. Recommendation System (RS) is the tool and largely used nowadays in many areas to generate interest items to users. With the development of e-commerce and information access, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Web recommendation systems assist the users to get the exact information and facilitate the information search easier. Web recommendation is one of the techniques of web personalization, which recommends web pages or items to the user based on the previous browsing history. But the tremendous growth in the amount of the available information and the number of visitors to web sites in recent years places some key challenges for recommender system. The recent recommender systems stuck with producing high quality recommendation with large information, resulting unwanted item instead of targeted item or product, and performing many recommendations per second for millions of user and items. To avoid these challenges a new recommender system technologies are needed that can quickly produce high quality recommendation, even for a very large scale problems. To address these issues we use two recommender system process using fuzzy clustering and collaborative filtering algorithms. Fuzzy clustering is used to predict the items or product that will be accessed in the future based on the previous action of user browsers behavior. Collaborative filtering recommendation process is used to produce the user expects result from the result of fuzzy clustering and collection of Web Database data items. Using this new recommendation system, it results the user expected product or item with minimum time. This system reduces the result of unrelated and unwanted item to user and provides the results with user interested domain.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Social Media Influence Analysis using Data Science TechniquesMuhammad Bilal
The major purpose of this literature search report is to demonstrate the usage of different tactics of data science to investigate impact of social media while considering the interaction between influences and their followers.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
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“To be integrated is to feel secure, to feel connected.” The views and experi...AJHSSR Journal
ABSTRACT: Although a significant amount of literature exists on Morocco's migration policies and their
successes and failures since their implementation in 2014, there is limited research on the integration of subSaharan African children into schools. This paperis part of a Ph.D. research project that aims to fill this gap. It
reports the main findings of a study conducted with migrant children enrolled in two public schools in Rabat,
Morocco, exploring how integration is defined by the children themselves and identifying the obstacles that they
have encountered thus far. The following paper uses an inductive approach and primarily focuses on the
relationships of children with their teachers and peers as a key aspect of integration for students with a migration
background. The study has led to several crucial findings. It emphasizes the significance of speaking Colloquial
Moroccan Arabic (Darija) and being part of a community for effective integration. Moreover, it reveals that the
use of Modern Standard Arabic as the language of instruction in schools is a source of frustration for students,
indicating the need for language policy reform. The study underlines the importanceof considering the
children‟s agency when being integrated into mainstream public schools.
.
KEYWORDS: migration, education, integration, sub-Saharan African children, public school
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Improving Workplace Safety Performance in Malaysian SMEs: The Role of Safety ...AJHSSR Journal
ABSTRACT: In the Malaysian context, small and medium enterprises (SMEs) experience a significant
burden of workplace accidents. A consensus among scholars attributes a substantial portion of these incidents to
human factors, particularly unsafe behaviors. This study, conducted in Malaysia's northern region, specifically
targeted Safety and Health/Human Resource professionals within the manufacturing sector of SMEs. We
gathered a robust dataset comprising 107 responses through a meticulously designed self-administered
questionnaire. Employing advanced partial least squares-structural equation modeling (PLS-SEM) techniques
with SmartPLS 3.2.9, we rigorously analyzed the data to scrutinize the intricate relationship between safety
behavior and safety performance. The research findings unequivocally underscore the palpable and
consequential impact of safety behavior variables, namely safety compliance and safety participation, on
improving safety performance indicators such as accidents, injuries, and property damages. These results
strongly validate research hypotheses. Consequently, this study highlights the pivotal significance of cultivating
safety behavior among employees, particularly in resource-constrained SME settings, as an essential step toward
enhancing workplace safety performance.
KEYWORDS :Safety compliance, safety participation, safety performance, SME
Enhance your social media strategy with the best digital marketing agency in Kolkata. This PPT covers 7 essential tips for effective social media marketing, offering practical advice and actionable insights to help you boost engagement, reach your target audience, and grow your online presence.
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Surat Digital Marketing School is created to offer a complete course that is specifically designed as per the current industry trends. Years of experience has helped us identify and understand the graduate-employee skills gap in the industry. At our school, we keep up with the pace of the industry and impart a holistic education that encompasses all the latest concepts of the Digital world so that our graduates can effortlessly integrate into the assigned roles.
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2. Data, Information, Knowledge(1)
Data
Facts and statistics collected together for reference or analysis.
The quantities, characters, or symbols on which operations
are performed by a computer, being stored and transmitted.
Information
The patterns, associations, or relationships among all this data
can provide information. For example, analysis of retail point
of sale transaction data can yield information on which
products are selling and when.
3. Data, Information, Knowledge(2)
Knowledge
Information can be converted into knowledge about historical
patterns and future trends. For example, summary
information on retail supermarket sales can be analyzed in
light of promotional efforts to provide knowledge of
consumer buying behavior. Thus, a manufacturer or retailer
could determine which items are most susceptible to
promotional efforts.
4. What is Data Mining ?
From the large dataset find the :
Unknown
Useful
Information.
The overall goal of the data mining process is to extract
information from a data set and transform it into an
understandable structure for further use.
The process of collecting, searching through, and analyzing a
large amount of data in a database, as to discover patterns or
relationships
5. What is Social Data Mining ?
Social media is designed as a group of Internet-based
applications that build on the ideological and technological
foundations of Web 2.0 and that allow the creation and
exchanges of user-generated content.
Vast amounts of user-generated content are created on social
media sites every day i.e. facebook, Twitter, Google+
Systematically analyzing the valuable information from the
Social media is Social data mining
Social media data are largely user-generated content which is
vast, noisy, distributed, unstructured, and dynamic
8. Why Important ?
The WWW is vast
People shares more data
Advertising and marketing
Products are more customized
More devices produce more data
Market Research
Customer Experience
Brand Loyalties
Product development and design
Communication, Marketing
9. Structures in Social Media
Social structures represent social relationships between
community members. Accordingly, social applications are often
designed to systemically support these properties.
Social structures represent social relationships between
community members. For example, in online forums, a useful
criterion provided by a social structure is whether or not a
member is an expert in a specific topic.
10. Types of Social Media Structure
Hierarchical Structure
Objects used in social data mining often possess a natural
hierarchical structure. For example, even a short document comprises a
number of sentences. Accordingly, hierarchical structures has been
frequently addressed in information representation.
Conversational Structure
We can identify conversational structures explicitly or implicitly
in most social platform involving interactions between users. For
example, in emails and forums, conversational structures are formed by
replies.
11. Data Mining Techniques for
Social Media
Graph Mining
Graphs (or networks) constitute a dominant data structure and
appear essentially in all forms of information. Examples include the
Web graph, social networks. Typically, the communities correspond to
groups 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 similarity.
Extracting useful knowledge (patterns, outliers, etc.) from
structured data that can be represented as a graph.
12. Graph Mining usage
Google uses page rank as one of many predictors for the relevance of
a web page. The link structure in the world-wide-web network
provides valuable contextual information about which pages are
deemed most relevant by the web page creators—this contextual link
structure is then used to predict relevance for a user’s query.
Useful for understand relationships as well as content (text, images),
Social media host tries to look at certain online groups and predict
about the group whether the group will flourish or disband.
13. Graph Mining usage cont.
Phone provider looks at cell phone call records to determine
whether an account is a result of identity theft.
Facebook Graph Search
Query examples
Searching people: “friends of friends who are single female in Rajkot”
Searching interests: “movies my friends like”, “TV shows my friends
like”, “Videos by TV shows liked by my friends”.
Searching places: “Restaurant in Rajkot liked by friends”
16. Text Mining
Text mining 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. Social networks contain
a lot of text in the nodes in various forms. For example, social
networks may contain links to posts, blogs or other news articles.
17. Usage of text mining (1)
Automatic processing of messages, emails
common application for text mining is to aid in the automatic
classification of texts. For example, it is possible to "filter" out
automatically most undesirable "junk email" based on certain
terms or words that are not likely to appear in legitimate messages
Investigating competitors by crawling their web sites
Another type of potentially very useful application is to
automatically process the contents of Web pages in a particular
domain. For example, you could go to a Web page, and begin
"crawling" the links you find there to process all Web pages that
are referenced.
18. Usage of text mining (2)
Medical
Mining medical records to improve care of patient
Security applications
Many text mining software packages are marketed for security
applications, especially monitoring and analysis of online plain
text sources such as Internet news, blogs, etc. for national security
purposes.
20. Generic Process of social data
mining
Web 2.0 data source
Data Collection
Data Modeling
Used In
application
Mining Methods
• Cluster & community Detection
• static analysis
• Classification
21. Text Mining Process stages (1)
Data Collection
The data collector module continuously downloads the from one or
more social platform and stores the raw data into the database
(e.g.BigData) or normal database. Based on application type the
parameters are specified with the API call.
Data Modeling
Data modeling is a process used to define and analyze data
requirements needed to support the application processes within the
scope of corresponding application. In the data modeling stage data
is model in various data model based on the application nature
22. Text Mining Process stages (2)
Mining Methods
Cluster analysis
automatic or semi-automatic analysis of large quantities of data to
extract previously unknown interesting patterns such as groups of
data records known as cluster analysis.
Anomaly detection
It is the search for items or events which do not confirm to an
expected pattern
23. Text Mining Process stages (3)
Static analysis
Analysis of historical business activities, stored as static data in data
warehouse databases, to reveal hidden patterns and trends.
Examples of what businesses use data mining for include
performing market analysis to finding the root cause of
manufacturing problems
Can be used to assist in discovering previously unknown strategic
business information.
To prevent customer attrition and acquire new customers
Cross-sell to existing customers
Manage customers with more accuracy.
24. OAuth 2.0
OAuth is an open standard for authorization
It provides a process for end-users to authorize third-party access to
their server resources without sharing their credentials (typically, a
username and password pair), using user-agent redirections.
Open authentication protocol which enables applications to access
each other’s data.
26. Authorization flow steps(1)
First the user accesses the client web application. In this web app is
button saying "Login via Facebook" (or some other system like
Google or Twitter).
Second, when the user clicks the login button, the user is redirected
to the authenticating application (e.g. Facebook). The user then logs
into the authenticating application, and is asked if s/he wants to
grant access to her data in the authenticating application, to the
client application. The user accepts.
Third, the authenticating application redirects the user to a redirect
URI, which the client app has provided to the authenticating app.
providing this redirect URI is normally done by registering the client
application with the authenticating application.
27. Authorization flow steps(2)
Fourth, the user accesses the page located at the redirect URI in the
client application. In the background the client application contacts
the authenticating application and sends
Once the client application has obtained an access token, this access
token can be sent to the Facebook, Google, Twitter etc. to access
resources in these systems, related to the user who logged in.
29. Roles of users and applications
in Auth 2.0 (2)
Resource Owner
The resource owner is the person or application that owns the data that is
to be shared. For instance, a user on Facebook or Google could be a
resource owner.
Resource Server
The resource server is the server hosting the resource owned by the
resource server. For instance, Facebook or Google is a resource server
Client Application
The client application is the application requesting access to the resources
stored on the resource server. The resources, which are owned by the
resource owner. A client application could be a game requesting access to a
users Facebook account.
30. Roles of users and applications
in Auth 2.0 (3)
Authorization Server
The authorization server is the server authorizing the client
application to access the resources of the resource owner.
The authorization server and the resource server can be the same
server
31. Big data
Big data is the term for a collection of data sets so large and complex
that it becomes difficult to process using on-hand database
management tools or traditional data processing applications. The
challenges include capture, storage, search, sharing, transfer, analysis
and visualization.
Some Examples :
Facebook has more than 1.15 billion active users generating social
interaction data.
More than 5 billion people are calling, texting, tweeting and
browsing websites on mobile phones
Scientific instruments generate large amount of data
33. Application Big data
Google Flu Trends uses search terms to predict the spread of the flu
virus
MIT are using mobile phone data to establish how peoples' locations
and traffic patterns can be used for urban planning
Statistician Nate Silver predicted the outcome of the US election
down to each individual state in 2012.
Big Data can bring the intelligence of online shopping into the retail
environment
34. Tools used in Big data (1)
NoSQL databases
NoSQL, it means non relational or Non-SQL database. There are
several database types that fit into this category, such as key-value
stores and document stores, which focus on the storage and retrieval
of large volumes of unstructured, semi-structured, or even structured
data.
Map Reduce by Google
This is a programming paradigm that allows for massive job
execution scalability against thousands of servers or clusters of
servers.
The "Map" task, where an input dataset is converted into a different set of
key/value pairs, or tuples
The "Reduce" task, where several of the outputs of the "Map" task are
combined to form a reduced set of tuples
35. Tools used in Big data (2)
Hadoop by Apache
Hadoop is by far the most popular implementation of MapReduce,
being an entirely open source platform for handling Big Data. It is
flexible enough to be able to work with multiple data sources, either
aggregating multiple sources of data in order to do large scale
processing.
36. Access Data from Twitter (1)
Twitter is an online social networking and microblogging service
that enables users to send and read "tweets", which are text messages
limited to 140 characters.
Twitter, provides various APIs that allows developers to build upon
and extend their applications in new and creative ways.
Twitter for Websites
Twitter for Websites is a suite of products that enables websites to easily
integrate Twitter. It is ideal for site developers looking to quickly and easily
integrate very basic Twitter functions.
37. Access Data from Twitter (2)
Search API
The Search API designed for products looking to allow a user to query
for Twitter content. This may include finding a set of tweets with specific
keywords, finding tweets referencing a specific user, or finding tweets
from a particular user.
REST API
The REST API enables developers to access some of the core primitives
of Twitter including timelines, status updates, and user information. If
you're building application that leverages core Twitter objects, then this
is the API which can be useful.
39. Access Data from Twitter (3)
Streaming API
Streaming APIs offered by Twitter give developers low latency access to
Twitter's global stream of Tweet data. This API is for those developers
with data intensive needs. To build a data mining product or are
interested in analytics research, the Streaming API is most suited for such
things.
41. Access Data from facebook
Facebook platform provides various API,SDK for develop application
which access the facebook data. The Facebook SDK provides a fast,
native, Facebook integration, using the exact same implementation,
regardless of which environment you're deploying to.
For Mobile platform facebook provides SDK for two platform
iOS platform
Android platform
For Web development SDK are provided by both Facebook and the
community
Php
Javascript
Ruby
Node.js
C#
42. Facebook APIs (1)
Search API
The Graph API is a simple HTTP-based API that gives access to the
Facebook social graph, uniformly representing objects in the graph and the
connections between them. Most other APIs at Facebook are based on the
Graph API.
FQL
Facebook Query Language, or FQL, enables you to use a SQL-style
interface to query the data exposed by the Graph API.
Dialogs
Facebook offers a number of dialogs for Facebook Login, posting to a
person's timeline or sending requests
43. Facebook APIs (2)
Chat
One can integrate Facebook Chat into Web-based, desktop, or mobile
instant messaging products.
Ads API
The Ads API allows you to build your own app as a customized alternative
to the Facebook Ads.
Public Feed API
The Public Feed API lets you read the stream of public comments as they
are posted to Facebook.
44. Friend Locator - Facebook App
Facebook application to display friend’s current location
and home town on Google map using jquery, google map
api and facebook platform.
It uses Oauth and FQL for accessing the client data from
the facebook.
48. Example of Mining Social Media
The core principal in mining of social sites is attribute-value that is
gathering by applying various algorithms. Attribute for any social
networking site can be categorized into two parts:
Individual Attributes
Community Attributes
Individual attribute describe the personal information about the
human like Gender, birth date, address, phone number, email
address etc.
Community attributes like friend list, tagged pictures, followers.
49. If we consider the example of facebook then Nowadays Facebook
users these days can control photo tagging and the sharing of their
friend list with the public user can also share the status with specific
people or group but still user cannot control friends sharing their
friend lists or uploading photos of them from their profiles to the
public.
By collecting and assess the vast amount of facebook user data one
can obtain general behavior of the user. Facebook provides the
sharing option for the phone number and personnel information, if
user discloses this sensitive information in their profile. The user
vulnerability will be increase to become the victim.
50. Conclusion
Valuable information is hidden in vast amounts of social media
data, presenting ample opportunities social media mining to discover
actionable knowledge that is otherwise difficult to find. Social media data
are vast, noisy, distributed, unstructured, and dynamic, which poses novel
challenges for data mining. In this paper, we offer a brief introduction to
mining social media, use illustrative examples to show that burgeoning
social media mining is spearheading the social media research, and
demonstrate its invaluable contributions to real-world applications.
51. References
[1] PritamGundecha, Huan Liu “Mining Social Media: A Brief
Introduction”, ISBN No 978-0-9843378-3-5
[2] Brain Amento, Loren Terveen , Will Hill “Experiments in Social
Data Mining”.
[3] Roosevelt C. Mosley Jr., FCAS, MAAA “Social Media Analytics:
Data Mining Applied to Insurance Twitter Posts”.
[4] Facebook Development - https://developers.facebook.com/
[5] Twitter Development - https://dev.twitter.com/
[6] Social Networking Statistics & Facts - http://visual.ly/100-socialnetworking-statistics-facts-2012