Web and Social Information Extraction - A.Y. 2019/2020

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.

IMPORTANT INFORMATION: From Monday, March 9th until the end of COVID-19 emergency, lessons will be REGULARLY held in streaming following the same schedule of in-class lessons. Students will receive the link to the videoconference few minutes before the beginning of the lesson. Information will be distributed through the course Google group and you will be able to participate in the videoconference ONLY using your institutional account. Videorecorded lessons and labs are available on a drive folder ONLY to students of the Google group.

Course schedule

II semester:

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

Textbooks

• Ricardo Baeza-Yates, Berthier Ribeiro-Neto Modern Information Retrieval, Addison Wesley Longman Publishing Co. Inc.
• Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, University Press. 2008.
• David Easley and Jon Kleinberg: Networks, Crowds, and Markets: Reasoning About a Highly Connected World http://www.cs.cornell.edu/home/kleinber/networks-book/
• Chen, Lakshmanan and castillo: Information and Influence Propagation in Social networks https://wiki.eecs.yorku.ca/course_archive/2014-15/F/4412/_media/social_networks.pdf

Exam

Standard grading policy:

• a) Written or oral exam on course material (50%). For those who attend classes, written test is split into a mid-term and a final test (usually, April and June).
• b) Project (teams of 2) (50%). The quality of developed software is matter of evaluation.

The interactivity grade will be 1/3 of your final grade, and incorporates also interactivity and contributions during Labs.

Final grade will be computed as follows:

• 1/3 interactivity
• 1/3 project
• 1/3 final written test (hopefully in presence..)

Project

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: <a data-saferedirecturl="https://www.google.com/url?q=https://docs.google.com/document/d/1NYVXM_X7qEX95RdFHnw0M3H2b-2BU_72IwmMxWVNca8/edit?usp%3Dsharing&source=gmail&ust=1559035338277000&usg=AFQjCNF9cfAdDVVAucDhoRLQa0NDtctAXw" href="https://docs.google.com/document/d/1NYVXM_X7qEX95RdFHnw0M3H2b-2BU_72IwmMxWVNca8/edit?usp=sharing" target="_blank">https://docs.google.com/document/d/1NYVXM_X7qEX95RdFHnw0M3H2b-2BU_72IwmMxWVNca8/edit?usp=sharing</a>.

best project in 2019 : MiningEvolvingTopics_best_2019.pdf (code can be requested to Bardh Prenkaj)

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 2020

assigned (recommender Systems in Tourism)

Google Group

Please Subscribe to Web and Social 2020 Group
Web and Social 2020 on Google Groups

Slides and course materials (note: UPDATED = 2020)

Syllabus

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-