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Web and Social Information Extraction - A.Y. 2018/2019

The course presents algorithms and architectures to retrieve information from the web and to analyze social networks. Topics are information retrieval, web search engines, web mining, social network analytics.

Instructors Telephone Office hours Studio

Paola Velardi

Giovanni Stilo

Bardh Prenkay

(teaching assistant)

06-49918356 send e-mail Via Salaria 113 - 3° floor n. 3412

Course schedule

II semester:

When   Where

Monday 14.00-16.30 Aula 2 - Aule L di Ingegneria
Wednesday 16.30-19.00 Aula 2 - Aule L di Ingegneria (Lab will be held in Colossus

labs- via salaria 113 - Since 2nd week of March)

Course Organization

The course presents architectures and algorithms related with the extraction of information from the Web, analyzing both Web Search engines and on-line Social Networks. A number of lessons are held in the Colossus lab.

During the lab, students will learn:

  • Lucene, an open-source text-search library. With Lucene, you will learn to index and search a document archive
  • How to useTwitter API, to track and index Twitter messages, to create and analyze word time series, and more
  • To build a web scraper and trace the content of a forum

Self Assessment and Final Project

Self assessments and final project are sent to all students by email (google group).

Summary of Course Topics

Architecture of Information Retrieval systems. Tokenisation, stop-word removal and stemming; morphology; selection of index terms, use of thesauri. Inverted indices. Boolean and vector-space retrieval models, ranked retrieval and text-similarity metrics. Performance metrics: recall, precision, and F-measure. Evaluations on benchmark text collections. Latent Semantic Indexing. Relevance feedback. Query expansion.

Web Information Retrieval. Browsing and Scraping. Link analysis: Page Rank and HITS.

Social Network analysis: Opinion Mining, Social Network analysis, Community Detection, Social Media Analytics, Recommender systems



  • a) Written or oral exam on course material (50%)
  • b) Project (teams of 2) (50%). The quality of developed software is matter of evaluation.


The project is presented after the first 4-5 weeks of the course.

Find here, as an example, the best project in 2018: wsie_report_moschella_spini.zip:

Project 2019

Find here the dataset for the project: datasetwebsocial2019.zip

The description of the project can be downloaded from this link: https://docs.google.com/document/d/1NYVXM_X7qEX95RdFHnw0M3H2b-2BU_72IwmMxWVNca8/edit?usp=sharing.

Project 2018

The project is on Recommender Systems.

The description is provided in Project_Description_2018.pdf

The description includes the link from where you can download the main dataset for the project, the Wiki_MID dataset.

The three additional datasets are: S21.tsv S22_preferences.tsv and S23.tsv

Project 2019

to be decided

Google Group

Please Subscribe to Web and Social 2019 Group
Web and Social 2019 on Google Groups

Slides and course materials (UPDATED = 2019)

Last Update Topic PPT PDF Details Suggested readings
2019 Introduction   1.WS1.introcompressed.pdf Introduction, architecture of IR systems, text processing, indexing

A survey on Indexing Techniques for Big Data: Taxonomy and Performance Evaluation (2015) https://www.researchgate.net/publication/273082158_A_survey_on_Indexing_Techniques_for_Big_Data_Taxonomy_and_Performance_Evaluation

The case for learned index structures (April 30th, 2018) https://arxiv.org/pdf/1712.01208.pdf

2019 Basic Ranking Models pptx pdf Basic ranking models: Boolean, Vector Space https://nlp.stanford.edu/IR-book/pdf/01bool.pdf
2019 Query Expansion ppt   Improving basic IR models




Retrieval with Latent Semantic

Indexing & Word Embeddings




Alternative Ranking Models based on word similarities https://cs224d.stanford.edu/lecture_notes/notes1.pdf
2019 Evaluation 3.EvaluationIR.pptx

Performance measures and benchmarking of IR systems


see also: https://trec.nist.gov/ for TREC evaluation challenges

2019 Web Search ppt   Web IR: crawling, scraping, searching on the Web; Categorization of web pages  
2019 Link Analysis 7.link_analysis.pptx
  Hyperlink-based Ranking (HITS and Page Rank) https://pdfs.semanticscholar.org/43b6/d922bcfcc8f8fcd3e7d22c8dc732653d9571.pdf
2019 Social Media Analytics A1   p 9a.SocialMediaAnalyticsA1-compresso.pdf Social Network Analysis PART A1: node-centric measures of influence  
2019 Social Media Analytics A2 9b.SocialMediaAnalyticsA2.pptx   Social Network Analysis PART A2: graph-based measures Surveytopknodesinsocialnetworks.pdf

Social Media Analytics B

10.SocialMediaAnalytics_community_detectionB.pptx   Social Network Analysis PART B: community detection




Social Media Analytics C

  Spread of Influence in Social Networks Chapter 2 of https://wiki.eecs.yorku.ca/course_archive/2014-15/F/4412/_media/social_networks.pdf
2016 Opinion Mining pdf   Searching for opinions on the Web https://link.springer.com/content/pdf/10.1007/s10462-017-9599-6.pdf
2019 Recommender Systems pdf   Collaborative filtering, Content-based recommenders, Semantic recommenders

http://shuaizhang.tech/2017/07/28/Summary-of-Recommender-System-Surveys-in-recent-years/ (a portal)

https://ieeexplore.ieee.org/document/8506344 ("A Survey of Collaborative Filtering-Based
Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks" 2018)

2017 Lab: Lucene   pdf Lucene text search engine library in java (Prof. Giovanni Stilo)  
2017 SAX temporal strings   pdf Event mining with SAX (Prof. Giovanni Stilo)  
2017 Lab: Maven Core   pdf Maven Core (Prof. Giovanni Stilo)  
2018 Lab: Twitter Api   pdf Twitter Api (Prof. Giovanni Stilo)  
2017 Lab: Scraping   pdf Scrapers (Prof. Giovanni Stilo)  
2017 Lab: Crawling   pdf Crawling Principles (Prof. Giovanni Stilo)  
2017 Lab: Time Series   pdf Tracing Temporal Streams of Words in Twitter (Prof. Giovanni Stilo)  
2017 Lab: Graph-G Library     Graph Libraries  


Part A: Web Information Retrieval

  • Introduction, Architecture of IR systems
  • Text processing, Indexing
  • Boolean and Vector Space Models
  • Query expansion, understanding users' needs
  • Ranking and query expansion based on word similarities (Singular value Decomposition, Word Embeddings)
  • Evaluation methods: experimental and theoretical methods
  • Web Information Retrieval
  • Link Analysis

Part B:Social Information Extraction

  • Social Network Analysis: graph-based measures, community detection, topic diffusion, temporal analysis
  • Opinion Mining
  • Recommender Systems

Topics for Final Dissertation

e-health, network medicine, event detection in social media and on the web, enterprise social networks, applications to social studies, temporal information retrieval, semantic recommenders, prediction and edetection of trendy topics

Merit-based (= high grades, very good programming skills) scholarships are available - ask the instructor-

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