NATO Advanced Study Institute on Mining Massive Data Sets for Security

Web Spam Detection

author: Carlos Castillo, Universita di Roma "La Sapienza"
author: Marcin Sydow, Polish-Japanese Institute of Information Technology

Description

Web spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to detect spam pages efficiently and accurately. This talk presents spam detection systems that combine link-based and content-based features, and use the topology of the Web graph by exploiting the link dependencies among the Web pages.

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*Slides*
0:00	Fighting Web Spam
0:29	Collaborators
0:56	Contents
1:43	Part I - Introduction
1:45	Search Engines
1:46	The Web today (1)
1:56	The Web today (2)
2:02	The Web today (3)
2:26	The Web today (4)
2:31	The Web today (5)
2:42	The Web today (6)
2:43	The Web today (7)
2:56	Searching information - among the top Web activities (1)
3:09	Searching information - among the top Web activities (2)
3:18	Searching information - among the top Web activities (3)
3:29	Searching information - among the top Web activities (4)
3:38	Why search engines? (1)
3:54	Why search engines? (2)
3:58	Why search engines? (3)
4:13	Some available statistics (1)
4:35	Some available statistics (2)
4:49	Some available statistics (3)
5:21	Some available statistics (4)
5:45	Search Engine Architecture
7:15	Search Engines - seemingly simple task (1)
7:17	Search Engines - seemingly simple task (2)
8:25	Crawler architecture
9:12	Ranking
11:35	Ranking System
13:03	Text-based Ranking - classic IR approach
15:27	WWW-specic issues concerning text analysis
16:43	A Remedy - Link Analysis
17:57	Example: PageRank - Basic Idea of Authority Flow
19:04	PageRank Equations
20:15	PageRank - summary
22:03	Web Spam
22:08	A bit of Web Economics. . . (1)
22:21	A bit of Web Economics. . . (2)
23:22	Advertising Market Shares (USA, 2006)
23:59	Internet Advertising (USA, 2006)
24:58	The Central Role of Search Engines in WWW
25:52	What is Spam?
27:00	Spam is destructive (1)
27:08	Spam is destructive (2)
27:15	Spam is destructive (3)
27:26	Spam is destructive (4)
27:34	Spam vs SEO
28:38	Spam taxonomy (1)
28:47	Spam taxonomy (2)
28:49	Spam taxonomy (3)
29:08	Spam taxonomy (4)
29:21	Spam taxonomy (5)
29:49	Spam techniques (1)
31:02	Spam techniques (2)
32:34	Spam techniques (3)
32:51	Naïve Web Spam
33:07	Hidden text
33:26	Made for Advertising
33:58	Search engine?
34:14	Fake search engine
34:27	Normal content in link farms
34:47	Cloaking
35:27	Redirection
36:41	Redirects using Javascript
37:11	Problem: obfuscated code
37:43	Problem: really obfuscated code
38:06	Fighting Spam (1)
38:31	Fighting Spam (2)
38:34	Fighting Spam (3)
38:35	Fighting Spam (4)
38:41	Fighting Spam (5)
38:59	Fighting Spam (6)
39:10	Fighting Spam (7)
39:30	Fighting Spam (8)
39:42	ML can help greatly (1)
40:10	ML can help greatly (2)
40:15	ML can help greatly (3)
40:19	ML can help greatly (4)
40:31	ML can help greatly (5)
40:43	ML can help greatly (6)
41:06	Part II - Reference Corpus & State of the Art

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