Adversarial information retrieval: Difference between revisions
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{{short description|Information retrieval strategies in datasets}} |
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'''Adversarial information retrieval''' ('''adversarial IR''') is a topic in [[information retrieval]] related to strategies for working with a data source where some portion of it has been manipulated maliciously. Tasks can include gathering, indexing, filtering, retrieving and ranking information from such a data source. Adversarial IR includes the study of methods to detect, isolate, and defeat such manipulation. |
'''Adversarial information retrieval''' ('''adversarial IR''') is a topic in [[information retrieval]] related to strategies for working with a data source where some portion of it has been manipulated maliciously. Tasks can include gathering, indexing, filtering, retrieving and ranking information from such a data source. Adversarial IR includes the study of methods to detect, isolate, and defeat such manipulation. |
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On the Web, the predominant form of such manipulation is [[spamdexing|search engine spamming]] (also known as spamdexing), which involves employing various techniques to disrupt the activity of [[web search engines]], usually for financial gain. Examples of spamdexing are [[Google bomb|link-bombing]], [[comment spam (disambiguation)|comment]] or [[referrer spam]], [[spam blog]]s (splogs), malicious tagging. [[Reverse engineering]] of [[ranking function|ranking algorithms |
On the Web, the predominant form of such manipulation is [[spamdexing|search engine spamming]] (also known as spamdexing), which involves employing various techniques to disrupt the activity of [[web search engines]], usually for financial gain. Examples of spamdexing are [[Google bomb|link-bombing]], [[comment spam (disambiguation)|comment]] or [[referrer spam]], [[spam blog]]s (splogs), malicious tagging. [[Reverse engineering]] of [[ranking function|ranking algorithms]], [[click fraud]],<ref>Jansen, B. J. (2007) [https://faculty.ist.psu.edu/jjansen/academic/jansen_click_fraud.pdf Click fraud]. IEEE Computer. 40(7), 85-86.</ref> and [[web content filtering]] may also be considered forms of adversarial [[data manipulation]].<ref>B. Davison, M. Najork, and T. Converse (2006), [https://web.archive.org/web/20090320173324/http://www.acm.org/sigs/sigir/forum/2006D/2006d_sigirforum_davison.pdf SIGIR Worksheet Report: Adversarial Information Retrieval on the Web (AIRWeb 2006)]</ref> |
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== Topics == |
== Topics == |
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== History == |
== History == |
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The term "adversarial information retrieval" was first coined in 2000 by [[Andrei Broder]] (then Chief Scientist at [[Alta Vista]]) during the Web plenary session at the [[Text Retrieval Conference|TREC]]-9 conference.<ref>D. Hawking and N. Craswell (2004), [http://es.csiro.au/pubs/trecbook_for_website.pdf Very Large Scale Retrieval and Web Search (Preprint version)]</ref> |
The term "adversarial information retrieval" was first coined in 2000 by [[Andrei Broder]] (then Chief Scientist at [[Alta Vista]]) during the Web plenary session at the [[Text Retrieval Conference|TREC]]-9 conference.<ref>D. Hawking and N. Craswell (2004), [http://es.csiro.au/pubs/trecbook_for_website.pdf Very Large Scale Retrieval and Web Search (Preprint version)] {{Webarchive|url=https://web.archive.org/web/20070829092407/http://es.csiro.au/pubs/trecbook_for_website.pdf |date=2007-08-29 }}</ref> |
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== See also == |
== See also == |
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*[[Artificial intelligence content detection]] |
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*[[Information retrieval]] |
*[[Information retrieval]] |
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*[[Spamdexing]] |
*[[Spamdexing]] |
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Latest revision as of 00:49, 16 November 2023
Adversarial information retrieval (adversarial IR) is a topic in information retrieval related to strategies for working with a data source where some portion of it has been manipulated maliciously. Tasks can include gathering, indexing, filtering, retrieving and ranking information from such a data source. Adversarial IR includes the study of methods to detect, isolate, and defeat such manipulation.
On the Web, the predominant form of such manipulation is search engine spamming (also known as spamdexing), which involves employing various techniques to disrupt the activity of web search engines, usually for financial gain. Examples of spamdexing are link-bombing, comment or referrer spam, spam blogs (splogs), malicious tagging. Reverse engineering of ranking algorithms, click fraud,[1] and web content filtering may also be considered forms of adversarial data manipulation.[2]
Topics
[edit]Topics related to Web spam (spamdexing):
- Link spam
- Keyword spamming
- Cloaking
- Malicious tagging
- Spam related to blogs, including comment spam, splogs, and ping spam
Other topics:
- Click fraud detection
- Reverse engineering of search engine's ranking algorithm
- Web content filtering
- Advertisement blocking
- Stealth crawling
- Troll (Internet)
- Malicious tagging or voting in social networks
- Astroturfing
- Sockpuppetry
History
[edit]The term "adversarial information retrieval" was first coined in 2000 by Andrei Broder (then Chief Scientist at Alta Vista) during the Web plenary session at the TREC-9 conference.[3]
See also
[edit]References
[edit]- ^ Jansen, B. J. (2007) Click fraud. IEEE Computer. 40(7), 85-86.
- ^ B. Davison, M. Najork, and T. Converse (2006), SIGIR Worksheet Report: Adversarial Information Retrieval on the Web (AIRWeb 2006)
- ^ D. Hawking and N. Craswell (2004), Very Large Scale Retrieval and Web Search (Preprint version) Archived 2007-08-29 at the Wayback Machine
External links
[edit]- AIRWeb: series of workshops on Adversarial Information Retrieval on the Web
- Web Spam Challenge: competition for researchers on Web Spam Detection
- Web Spam Datasets: datasets for research on Web Spam Detection