The document discusses a framework for a recommendation system based on user behavior modeling derived from social networks, particularly emphasizing personality traits using the 'big five' model. It highlights data collection and analysis methods, including sentiment analysis of social media content to categorize users' behaviors. The application of this system aims to assist businesses and organizations in making informed decisions based on consumer interests and tendencies.

1. User Behavior Modeling &
Recommendation System Based On
Social Networks
Presented by Alam Shah
Najeeb, Ahmad Taher
Hossain MD. Shakawat
American International University-Bangladesh (AIUB)
Advisor: Md. Saddam Hossain, Assistant Professor, Department of
Computer Science, American International University-Bangladesh.

2. Abstract
 Our main approach is to suggest a person
regarding the person’s specific interests
which are anticipated based on the
person’s public data analysis. These data
can be used in further business analysis to
recommend products or services of
different companies depending on the
consumer’s personal choice.

3. Slide Life:
 Definition of Behaviour
 BIG FIVE factors
 Reality Demand
 Applications
 Scope of Implementation
 Relative Works
 Research Question
 Research Methodology
 Data Collection
 Data Analysis
 Results
 Recommendation System
 Conclusion

4. Applications
 Behaviour is the way in which one acts or
conducts oneself, especially towards others.
 Psychologists believe that there are five basic
dimensions of personality, often referred to as
the “Big Five” personality traits [1]. The five
broad personality traits described by the theory
are extraversion, agreeableness, openness to
experience, conscientiousness, and neuroticism.
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Definition of Behavior Big Five Reality Demands Scope of Implementation
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Introduction Related Works Research Questions
Research
Methodology
Conclusion
Definition of Behaviour

5. Introduction Related Works Research Questions
Definition of Behavior Big Five Reality Demands Scope of Implementation
 Extraversion: refers to excitability, sociability,
talkativeness, assertiveness and high amounts
of emotional expressiveness towards others.
 Agreeableness: refers to being helpful,
cooperative and sympathetic towards others.
 Conscientiousness: It concerns the way in
which we control, regulate, and direct our
impulses.
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Big Five Factors [1]
Research
Methodology
Conclusion
Applications

6. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Definition of Behavior Big Five (cont.) Reality Demands Applications
Scope of Implementation
Big Five Factors (cont.)
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 Neuroticism: Is characterized by moodiness,
worry, envy, frustration, jealousy, and loneliness.
 Openness to experience: It involves with active
imagination (fantasy), aesthetic sensitivity,
attentiveness to inner feelings, preference for
variety, and intellectual curiosity.

7. Introduction Related Works Research Questions
Definition of Behavior Big Five Reality Demands Scope of Implementation
 An employee needs a vacation and if
his/her boss is listed as friend on
OSN(online social networks) then the
employee gets the chance to apply for his
demand according to boss’s behavior
generated by the system (Neuroticism
indicates higher chances of disagree when
Agreeableness indicates higher chances of
agree).
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Reality Demands
Research
Methodology
Conclusion
Applications

8. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Definition of Behavior Big Five Reality Demands (cont.) Scope of Implementation
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Reality Demands (cont.)
 A business company wants to attract new
customers. If the company can know which
types of people want to buy its products
then it will be easier for the company to
reach the right customers.

9. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Definition of Behavior Big Five Reality Demands (cont.) Scope of Implementation
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Reality Demands (cont.)
 A company manager needs to hire an
employee, to hire the right guy for the right
job the employer might want to analyze the
employee’s behaviour.

10. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Definition of Behavior Big Five Reality Demands (cont.) Scope of Implementation
Reality Demands (cont.)
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 A political organisation wants to gain more
popularity. If the party can determine which
types of people are already its member
and which types of people are eager to join
their party then the party can recruit more
efficiently.

11. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Definition of Behavior Big Five Reality Demands Scope of Implementation
Usefulness of this project
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 To help business companies to make the right
marketing decisions by analyzing people’s
behaviour and current trends.
 To help an organisation to recruit the right
person of the right job.
 To help people know each other better.
 To help social scientists to analyze people’s
behaviour.

12. Introduction Related Works Research Questions
Scope of Implementation
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Definition of Behavior Big Five Reality Demands Applications
Scope of Implementation
 In this era of internet social networks are
very popular among people. Two third of
the world population spent 10% of their
time in internet in online social networks.
[2]

13. Location Based Social Network Collaborative Recommendation Big Five Modeling
Location Based Social Network [3]
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Introduction Related Works Research Questions
Research
Methodology
Conclusion
Sentiment Analysis of Informal Text
 People in an existing social network can
expand their social structure with the new
interdependency derived from their
locations. As location is one of the most
important components of user context,
extensive knowledge about an individual’s
interests and behaviour can be learned
from the person’s location.

14. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Location Based Social Network Collaborative Recommendation Sentiment Analysis of Informal Text
Big Five Modeling
Collaborative Recommendation Based
Social Network [4]
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 Recommendation systems can be based
on user collaboration.

15. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Location Based Social Network Collaborative Recommendation Sentiment Analysis of Informal Text
Big Five Modeling
Sentiment Intensity Analysis of Informal
Texts [5]
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 Sentiment Intensity Analysis of Informal
texts: Sentiment analysis, also known as
opinion mining, has known considerable
interest recently.

16. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Location Based Social Network Collaborative Recommendation Sentiment Analysis of Informal Text
Big Five Modeling
Big Five Modelling [1]
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It is possible to create an automated system
that can categorize users according to Big
Five personality.
1. Extraversion
2. Agreeableness
3. Conscientiousness
4. Neuroticism
5. Openness to experience

17. Introduction Related Works Research Questions
Research
Methodology
Conclusion
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Research Questions
 How to categorize users of OSN according
to Big Five factors from their behaviours in
OSN?
 Sub research Questions:
1. How OSN represent one user?
2. How can we analyze user behaviour ?
3. How to categorize user behaviour in Big
Five factors?

18. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Methodology Data Collection Data Analysis
Result
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Research Methodology
 We follow co-relational and exploratory
methodology in this research. We use this
methodology to make relationship among
text corpus from social network with
psychological theory of personality.

19. USER
LIWC
Mapping
OSN(Twitter)
Twitter API
Represents
Figure: Modelling User Behaviour

20. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Methodology Data Collection Result
Data Collection
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 Twitter, a social network site, can be used for
sentiment analysis as it has a very large
number of short messages(tweets) created by
its users [6]. So we used Twitter to collect
users’ data. All the data collected are public
data so there are no barriers to use these
data. Using Twitter REST api 1.1, we
collected users’ public tweets, re-tweets and
followed pages in text files.

21. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Methodology Data Collection(cont.) Data Analysis
Result
Recommendation
System
Ethical Issues regarding Data Collection
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 We did not use Facebook(another social
networking site) because most of the users
of Facebook restrict their profile
information and status updates to 'private'.
So extracting or using private data will not
be ethical. On the other hand many users
of Twitter make their profile public. So
there are no barriers to use these public
data.

22. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Methodology Data Collection Result
Data Analysis
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 We used Linguistic Inquiry and Word
Count (LIWC) which is a text analysis
software program designed by James W.
Pennebaker, Roger J. Booth, and Martha
E. Francis.

23. Introduction Related Works Research Questions
Methodology Data Collection Result
WHY LIWC
Research
Methodology
Conclusion
Data Analysis(cont.)
Recommendation
System

24. Introduction Related Works Research Questions
LIWC features
Research
Methodology
Conclusion
 LIWC2007 is designed to accept written or
transcribed verbal text which has been stored as a
text or ASCII file using any of the popular
word processing software packages.
 LIWC2007 reads each designated text file, one
target word at a time in a fraction of second.
 32 word categories tapping psychological constructs,
7 personal concern categories, 3 paralinguistic
dimensions, and 12 punctuation categories.
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Methodology Data Collection Data Analysis(cont.)
Result
Recommendation
System

25. Introduction Related Works Research Questions
Research
Methodology
Methodology Data Collection Result
Table: LIWC output variable Information
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Category Example
Biological Process Eat, blood, Pain
Ingestion Dish, Eat, Pizza
Achievement Earn, Hero, Win
Insight Think, Know, Consider
Hear Hearing, Listening
Conclusion
Data Analysis(cont.)
Recommendation
System

26. Table: LIWC categories under Big Five Factors
Extraversion Openness to
Experience
Neuroticism Conscientiou
sness
Agreeableness
Social
process
Leisure Swear words Relativity Positive emotion
Family Insight Negation Motion Feel
Friends Body Negative
emotion
Space Discrepancy
Humans Ingestion Anxiety Time Tentative
Biological
Anger Religion Hear
process
Sexual Sadness Death
Achievement Sexual Money
Certainty
See

27. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Methodology Data Collection Result
Results
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 LIWC saved the result after reading a word
document in a .dat file.
 Linear regression formula is used to sum up the
values of each categories.
f(x) = x1+x2+x3+.......+xi
 Percentage formula part / whole = % /100 is used to
visualize the result in pie chart.

28. Methodology Data Collection Result(cont.)
Results in a pie chart
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Openness
Extraversion
Agreeableness
Neuroticism
Conscientiousness
Introduction Related Works Research Questions
Research
Methodology
Conclusion
Data Analysis
Recommendation
System

29. USER
Figure: Recommendation System

30. Introduction Related Works Research Questions
Research
Methodology
Conclusion
Methodology Data Collection Data Analysis
Result
Recommendation System
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 User A, B, C are followers of Age of Empires game
page. After analyzing their tweets and retweets,
machine maps the users’ behaviour and it seems
that the major part of their behaviour is extrovert.
Now after analyzing the tweets/retweets of another
user X, if the machine finds that majority of this
user’s behaviour is influenced by extroversion then
we can recommend this user game like Age of
Empires.

31. Methodology Data Collection Result
Big Five Factor Video Games Movies Music
Extraversion Strategy(Age of
Empires,
Commandos)
Political, Fantasy,
Family
Rock
Openness to
Experience
Racing(Need for
Speed)
Comedy, Sports,
Drama
Classical, New
released, Vocal
Neuroticism Shooting(Call of
Duty, Counter
Strike)
Crime, Action,
Horror
Pop, Heavy
metal
Conscientiousne
ss
Chess, Sudoku Political, History,
Conspiracy
Classical
Agreeableness Sports
Games(Fifa)
Romantic, Drama Romantic
Introduction Related Works Research Questions
Research
Methodology
Conclusion
Data Analysis
Recommendation
System(cont.)
Table: Types of products under big five factor
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32. Conclusion
 To the best of our knowledge this is the first
recommendation system based on Big Five factors.
In our thesis we proved that personality can be
determined by analyzing language cues.
 At this moment our system can only use text
information, But in future our system will be able to
mine data from shared links or videos. There is a big
scope for analyzing exclamatory sentences or
smileys. Our system can not understand sarcastic
behaviour at this moment, it neither can understand
double negation.
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Introduction Related Works Research Questions
Research
Methodology
Conclusion

33. Thank You


34. References
1. Kendra Cherry. The big five personality dimensions, 2012. Accessed:2010-
09-30.
2. Nielsen Online Report. Social networks & blogs now 4th most popular
online activity, 2009.
3. Zheng, Yu. "Location-based social networks: Users." Computing with Spatial
Trajectories. Springer New York, 2011. 243-276.
4. Konstas, Ioannis, Vassilios Stathopoulos, and Joemon M. Jose. "On social
networks and collaborative recommendation." Proceedings of the 32nd
international ACM SIGIR conference on Research and development in
information retrieval. ACM, 2009.
5. Paltoglou, Georgios, et al. "Sentiment analysis of informal textual
communication in cyberspace." Proc. Engage (2010): 13-25.
6. A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and
opinion mining.,” in LREC, 2010. 2 3 4 5 6 7 8 9
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