Internet Studies
Faculty Members
The specialty has now 2 faculty members
Prof. Ronen Feldman: Text Mining, Data Mining, Social
Media Analysis, Information Extraction, Link Analysis, Big
Data, Internet based Entrepreneurship.
Dr. Lev Muchnik: Social Network Analysis, Big Data, Crowd
Sourcing.
Big Data
Big data is an all-encompassing term for any collection of data sets so
large and complex that it becomes difficult to process using traditional
data processing applications.
Big data is being generated by everything around us at all times. Every
digital process and social media exchange produces it. Systems, sensors
and mobile devices transmit it. Big data is arriving from multiple sources
at an alarming velocity, volume and variety. To extract meaningful value
from big data, you need optimal processing power, analytics capabilities
and skills.
84% Of Enterprises See Big Data Analytics Changing Their Industries'
Competitive Landscapes In The Next Year
Courses
Introduction to Internet Technologies
Text Mining: Techniques and Applications
Data Mining Techniques
Networks: theory and application
Social Aspects of Socio-technical Systems
Internet Technologies Research Seminar
Macy’s
ISI evercore experiment: Results
Our Sentiment Engine delivered a significant improvement in returns compared
to Evercore recommendations alone
5 workdays after the
recommendation
1 workday after the
recommendation
0.40%
0.30%
0.20%
0.10%
0.00%
10 workdays after the
recommendation
1.50%
1.00%
0.50%
0.00%
Analyst
Newsletter
(n=102)
Sentiment
Analysis
(n=90)
Weighted average
2.00%
1.50%
1.00%
0.50%
0.00%
Agreement
(n=76)
Analyst
Newsletter
(n=102)
S&P benchmark
Sentiment
Analysis
(n=90)
Weighted average
Analyst
Newsletter
(n=102)
S&P benchmark
15 workdays after the
recommendation
2.00%
1.50%
1.00%
0.50%
0.00%
Agreement
(n=76)
Weighted average
30 workdays after the
recommendation
6.00%
4.00%
2.00%
0.00%
Analyst
Newsletter
(n=102)
Sentiment
Analysis
(n=90)
Weighted average
Agreement
(n=76)
S&P benchmark
Analyst
Newsletter
(n=49)
Sentiment
Analysis
(n=90)
Sentiment
Analysis
(n=43)
Weighted average
Agreement
(n=34)
S&P benchmark
Agreement
(n=76)
S&P benchmark
Drug
Terms
Analysis
Byetta - Side Effects Analysis
7
Byetta appeared much
more than chance with
the following side
effects:
• “Nose running” or
“runny nose”
• “No appetite”
• “Weight gain”
• “Acid stomach”
• “Vomit”
• “Nausea”
• “Hives”
Course number: 55693
Semester 1
Tuesday: 18:30-21:15
Networks: theory and application
This course will introduce the modern network theory and its numerous applications ranging from
online social and mobile communication networks to search, marketing, fraud detection,
epidemiology and dynamic processes on networks such as spread of information, opinions and
behaviors. The objective is to learn to recognize and deal with the phenomena characteristic to the
highly interconnected environments we and our businesses inhabit today.
We will cover subjects like network classification, random graph models, network motifs, community
structure, resilience of networks to disruption and social network – specific concepts such as wordof-mouth, structural holes, peer effects, homophily and information cascades .
Emphasis will be made on practical application of the learned material and in particular network
data collection, its analysis and interpretation.
The course in given in English
By: dr. Lev Muchnik (lev.muchnik@huji.ac.il)
Course number: 55695
Semester 2
Sunday: 15:30-18:15
Social Aspects of Socio-technical Systems
It is still early to grasp the extent to which the recent surge of communication and data management
technologies will eventually affect the way individuals, businesses and societies operate. But the
change is already profound enough to review the variety of phenomena it inspired. This course will
discuss socio-technical systems from social and business perspectives and develop the skills
necessary to model and analyze them.
We will discuss various forms of collective behavior, wisdom of crowds, public opinion formation,
information markets, social media, emergence of the computational social science and implications
of availability of huge amounts of social data (i.e. privacy, personalization, etc.).
We will gain experience in empirical analysis of such systems and their modelling using analytical and
computational tools, learn causal inference techniques and agent-based modelling.
By: dr. Lev Muchnik (lev.muchnik@huji.ac.il)
Companies we collaborate with: