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 DataBig 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

CoursesIntroduction 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

Analyst News-letter

(n=102)

Sentiment Analysis(n=90)

Agreement(n=76)

0.00%

0.10%

0.20%

0.30%

0.40%

1 workday after the recommenda-tion

Weighted average S&P benchmark

Analyst News-letter

(n=102)

Sentiment Analysis(n=90)

Agreement(n=76)

0.00%0.20%0.40%0.60%0.80%1.00%1.20%

5 workdays after the recommenda-tion

Weighted average S&P benchmark

Analyst News-letter

(n=102)

Sentiment Analysis(n=90)

Agreement(n=76)

0.00%

0.50%

1.00%

1.50%

2.00%

10 workdays after the recom-mendation

Weighted average S&P benchmark

Analyst News-letter

(n=102)

Sentiment Analysis(n=90)

Agreement(n=76)

0.00%

0.50%

1.00%

1.50%

2.00%

15 workdays after the recom-mendation

Weighted average S&P benchmark

Analyst News-letter(n=49)

Sentiment Analysis(n=43)

Agreement(n=34)

0.00%1.00%2.00%3.00%4.00%5.00%

30 workdays after the recom-mendation

Weighted average S&P benchmark

OUR SENTIMENT ENGINE DELIVERED A SIGNIFICANT IMPROVEMENT IN RETURNS COMPARED TO EVERCORE RECOMMENDATIONS ALONE

Drug Terms AnalysisByetta - Side Effects Analysis

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”

7

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 word-of-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

Course number: 55693Semester 1Tuesday: 18:30-21:15

By: dr. Lev Muchnik (lev.muchnik@huji.ac.il)

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.

Course number: 55695Semester 2Sunday: 15:30-18:15

By: dr. Lev Muchnik (lev.muchnik@huji.ac.il)

Companies we collaborate with: